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Chronobiology of absence epilepsy
Magdalena Kinga Smyk
© Magdalena Kinga Smyk
ISBN: 978-94-6284-031-7
Printed in Kraków
Chronobiology of absence epilepsy
Proefschrift
ter verkrijging van de graad van doctor
aan de Radboud Universiteit Nijmegen
op gezag van de rector magnificus prof. dr. Th.L.M. Engelen,
volgens besluit van het college van decanen
in het openbaar te verdedigen op maandag 26 oktober 2015
om 14.30 uur precies
door
Magdalena Kinga Smyk
geboren op 7 oktober 1983
te Rzeszów (Polen)
Promotoren:
Prof. dr. E.L.J.M. van Luijtelaar
Prof. dr. M.H. Lewandowski (Uniwersytet Jagielloński w Krakowie,
Polen)
Manuscriptcommissie:
Prof. dr. G. Flik
Prof. dr. J.H. Meijer (Universiteit Leiden)
Prof. dr. G. Hess (Uniwersytet Jagielloński w Krakowie, Polen)
Chronobiology of absence epilepsy
Doctoral Thesis
to obtain the degree of doctor
from Radboud University Nijmegen
on the authority of the Rector Magnificus prof. dr. Th.L.M. Engelen,
according to the decision of the Council of Deans
to be defended in public on Monday, October 26, 2015
at 14.30 hours
by
Magdalena Kinga Smyk
Born on October 7, 1983
in Rzeszów (Poland)
Supervisors:
Prof. dr. E.L.J.M. van Luijtelaar
Prof. dr. M.H. Lewandowski (Uniwersytet Jagielloński w Krakowie ,
Poland)
Doctoral Thesis Committee:
Prof. dr. G. Flik
Prof. dr. J.H. Meijer (Leiden University)
Prof. dr. G. Hess (Uniwersytet Jagielloński w Krakowie, Poland)
CONTENTS
CHAPTER 1
General Introduction
CHAPTER 2
Differential temporal organization of
sleep-wake
states
and
absence
seizures in light versus dark phase in a
genetic animal model
57
CHAPTER 3
Endogenous
rhythm
of
absence
epilepsy: relationship with general
motor activity and sleep-wake states
87
CHAPTER 4
Internal desynchronization facilitates
seizures
113
CHAPTER 5
Re-entrainment of sleep-wake states
and absence seizures after a phase
delay
137
CHAPTER 6
General Discussion
181
ADDENDUM English Summary
Nederlandse Samenvatting
Curriculum Vitae
Schools and Courses
Awards
List of Publications
Acknowledgements
Podziękowania
Donders Series
9
219
224
230
230
231
231
235
239
245
CHAPTER 1
General Introduction
Biological timing: from milliseconds to annual cycles
An accurate processing of time information requires an efficient
clock. Living organisms have developed a multitude of timing
mechanisms based on oscillations, unidirectional processes or a
combination of these two, to perceive succession and duration of
events. Perception of succession applies to the recognition of a
sequential characteristic of events, their temporal order. Perception of
duration means that the persistence of an event over time and the
interval between two events can be identified (Fraisse, 1984; Wittman,
1999; Mattel and Meck, 2000; Rensing et al., 2001). The time scales
over which organisms process temporal information and generate
timed behavior encompass several orders of magnitude (Hinton and
Meck, 1997; Buonomano and Karmarkar, 2002; Buonomano, 2007,
Agostino et al., 2011). A microsecond scale is used by sound
localization mechanisms based on the ability to discriminate between
very short time intervals needed for sound to travel from one ear to
the other (Buonomano, 2007). However, conscious detection of
temporal order is possible when interval separating events exceed 20
– 40 milliseconds (Steinbuchel, 1995; Pastore and Farrington, 1996).
This time scale, in a range of milliseconds to seconds and minutes
(“interval
timing”),
is
critical
for
sensory
processing,
motor
coordination, speech recognition or associative learning (Mattel and
Meck, 2000; Buonomano, 2007).
Opposite to these rather fast processes, organisms use also
longer intervals such as hours or days. Many biological functions occur
regularly and repeat each day at precisely predictable time. Core body
temperature of humans living a conventional lifestyle reaches its
minimum around 5 a.m. (Waterhouse et al., 2005). Blood pressure in
healthy subjects is reduced about 10 – 20% in the night in comparison
to its mean daytime value (Synder et al., 1964). Human cortisol and
11
rodent corticosterone, hormones implicated in response to stress, but
also
in
metabolic,
anti-inflammatory
and
immunosuppressive
processes and responses, reach their maximum value just before the
onset of the period of daily activity: in the early morning in humans
and just before the dark phase in rats (Atkinson and Waddell, 1997;
Perreau-Lenz et al., 2004; Chan and Debono, 2010). Cortisol is not an
exception, a variety of hormones such as melatonin, thyroidstimulating hormone (TSH), and prolactin, cycle with a period of 24 h
(Czeisler et al., 2005; Schechter and Boivin, 2010). These are a few
examples of many circadian rhythms (a term originated from the Latin
words circa, meaning “about”, and dies meaning “day”), oscillations of
biological processes representing the internal notion of time.
The frequency of oscillations is one of the criteria on which
rhythmic processes are classified. While the duration of circadian
rhythms does not deviate significantly from 24 h, the range of ultradian
(shorter than a day) and infradian (longer than a day, e.g. lunar, tidal,
annual) periodicities may vary enormously. To illustrate, the period of
ultradian rhythms may vary from milliseconds to hours, and that from
infradian rhythms from days to years. For example, the period length
of gamma oscillations as occurring in neuronal network is 0.01 – 0.03
sec, while the rapid-eye-movement (REM)/slow-wave sleep cycle in
humans, both within the ultradian range, is 1.5 h (Achermann and
Borbély, 1990; Achermann et al., 1993; Fries et al., 2007). Similarly,
the infradian human menstrual cycle lasts about 28 days, while the
reproduction cycle of seasonal breeding animals lasts about 12 months
(Chiazze et al., 1968; Lee and Zucker, 1991; Woodfill et al., 1994).
Besides the frequency, rhythmic phenomena may also be classified
according to the biological system in which they are observed: a single
cell, complex organism or a population, to their origin: endogenous or
exogenous, and to their function (Aschoff, 1981).
12
Different time mechanisms in the mammalian brain are
interrelated. The circadian pacemaker has been reported to affect the
perception of time interval (Poppel and Giedke, 1970). Next, the
seasonal-encoding mechanisms use the circadian timing system to
estimate the day length; this is necessary for recognition of different
seasons of the year (Goldman, 2001; Prendergast et al., 2002).
Circadian timing system: from single-cell oscillators to
multi-oscillatory machinery
The
presence
of
an
internal
mechanism
providing
synchronization of physiological and behavioral processes to recurrent
24 h environmental cycles was obviously advantageous from an
evolutionary point of view. A circadian timing system, present already
in phylogenetically early, primitive organisms such as cyanobacteria
(Synechococcus elongatus), allows the anticipation and adaptation to
light-dark, temperature, or predators cycles, increasing therefore
chances of survival in a particular ecological niche (Pittendrigh, 1993;
Ouyang et al., 1998; Woelfle et al., 2004; Hut and Beersma, 2011).
The mechanism of circadian oscillations is based on complex molecular
feedback loops, both positive and negative, regulating transcription of
rather conservative core clock genes. Cyclic changes in clock genes
expression and proteins level generate rhythmic physiological output
(Reppert and Weaver, 2002; Dvornyk et al., 2003; Roenneberg and
Merrow, 2003; Ko and Takahashi, 2006).
The circadian clock, a self-sustained mechanism oscillating with
a period length close to 24 h, needs to be entrained to external
environmental time to fulfill its role. Entrainment occurs when a stable
phase-relationship between the internal and external oscillations is
established, that is when their period lengths are equal (Pittendrigh,
1981). By an input pathway entraining factors, called “Zeitgebers” (a
13
term originated from the German words Zeit meaning “time” and geben
meaning “to give”) reach a central oscillator in which a rhythmic signal
is generated and then transmitted via an output pathway to dependent
oscillators, and manifests as an overt rhythm (Golombek and
Rosenstein, 2010). Light is one of the strongest synchronizers for
biological rhythms among the different Zeitgebers (Pittendrigh, 1960).
In mammals, photic information perceived by the retina is transmitted
via the retino-hypothalamic tract (RHT, input pathway) to the central
circadian pacemaker: the suprachiasmatic nuclei of the hypothalamus
(the SCN) (Moore and Lenn, 1972; Zordan et al., 2001). The SCN
generates a rhythmic signal which, by multiple synaptic connections
and humoral factors, reaches different brain areas (output pathways).
As a result, rhythmic fluctuations in e.g. core body temperature, sleepwake cycle or hormone secretion are observed (Buijs and Kalsbeek,
2001). The presence of Zeitgebers is not necessary for rhythms
generation considering the self-sustained character of circadian
oscillations.
Indeed,
circadian
rhythms
persist
in
constant
environmental conditions, however, in such a “free-running” state their
period length deviate slightly from 24 h (Bünning, 1964; Herzog and
Schwartz, 2002). Other circadian parameters such as amplitude or
acrophase (the time point at which a rhythm peaks) may be also
affected.
The mammalian circadian timing system has a complex,
hierarchical organization. Multiplicity of entraining factors, classified
generally as photic and non-photic, requires a variety of input
pathways (Hannibal and Fahrenkrug, 2006; Morin, 2013). Besides the
photic input of the SCN from the RHT, the SCN also receives indirect
input from the intergeniculate leaflet (IGL) of the lateral geniculate
nucleus of the thalamus and two pretectal nuclei (posterior and olivary)
(Mikkelsen and Vrang, 1994; Moore and Card, 1994; Meijer and
Schwartz, 2003). All three structures are innervated by the retina.
14
Information about non-photic Zeitgebers comes from several brain
structures and regions involved in arousal, sleep-wake regulation,
feeding, stress responses, reproduction, olfactory, immune and
autonomic functions (Hastings et al., 1997; Krout et al., 2002). The
central pacemaker itself has also a complex structure. The SCN
consists of about 10 000 – 15 000 densely-packed neurons oscillating
with different frequencies, which all together yields a 24 h rhythm
(Gillette, 1991; Welsh et al., 1995; Honma et al., 1998). The
distribution of SCN cell’s phenotype with regard to neurotransmitters
and direct retinal connections is not uniform in humans and in rodents
(Hofmann et al., 1996; Dai et al., 1997; Morin and Allen, 2006).
Besides this central master circadian pacemaker, there are numerous
peripheral oscillators, both in the brain (e.g. pineal and pituitary gland,
paraventricular nucleus of the hypothalamus) and in peripheral organs
such as liver, lungs or skeletal muscles (Yamazaki et al., 2000; Abe et
al., 2002). These peripheral clocks are in a stable phase-relationship
with the SCN and the external environment. However, when the
entrainment
is
desynchronization
lost
or
between
temporally
master
and
disturbed,
peripheral
the
internal
(dependent)
oscillators occurs. This phenomenon has been demonstrated by studies
investigating the effects of phase-shifts in a light-dark cycle resembling
jet lag or shift work. The amount of time required for full entrainment
to a new photoperiod is not only species dependent, it also varies
between different oscillators, however the master pacemaker always
re-synchronizes as first (Yamazaki et al., 2000; Nagano et al., 2003;
Lee et al., 2009).
Lack of internal synchrony is thought to have diverse health
consequences starting from relatively mild discomfort associated with
feelings as often reported in jet lag, through serious sleep disturbances
to depression or a higher risk of cancer (Arendt and Marks, 1982;
15
Schernhammer et al., 2001; Knutsson, 2003; Schernhammer et al.,
2003; Megdal et al., 2005; Blask, 2009).
Melatonin – a hand of the clock
The simplified model of the circadian timing system described
above assumes a linear relationship between its elements: Zeitgebers,
input pathways, central oscillator and output pathways. However,
there is evidence suggesting much more complex relationship between
the components. Clock-dependent rhythms may give feedback to the
master pacemaker or affect the input pathways (LaVail, 1980; Pfeffer
et al., 2012). Also Zeitgebers themselves are able to directly influence
physiological functions. This phenomenon is called “masking”. As a
result of masking, the usual shape and/or parameters of a rhythm
change as a consequence of random or non-random environmental
stimuli. These alterations persist only for the duration of the stimulus
while the endogenous components of the rhythm remain unchanged.
An example of masking is the elevation of body temperature after a
hot bath, direct effect of light on states of vigilance, or light-induced
suppression of melatonin synthesis (Borbély et al., 1975; Bronstein et
al., 1987; Aschoff, 1999).
The pineal hormone melatonin is an example of a variable
which, produced and secreted under the control of the central
pacemaker, is able to affect the action of the central pacemaker itself.
In mammals, melatonin is synthesized and released from the pineal
gland into the circulation predominantly during darkness, it encodes
therefore the length and the phase of the dark period, regardless of
the ecological type of the animal: diurnal or nocturnal (Vanecek, 1998).
Melatonin binds with high affinity to G-protein-coupled MT1 and MT2
melatonin receptors, which are distributed widely in the brain (Weaver
et al., 1989). There is a high inter-species and inter-structure
16
variability in the distribution of melatonin receptors; however, two
major brain regions with high presence and expression of these
receptors were identified: pituitary pars tuberalis and the SCN (Weaver
et al., 1989; Stankov et al., 1991; Reppert et al., 1994). The rhythmic
synthesis and secretion of melatonin is one of the direct outputs of the
central pacemaker, a “hand of the clock”. The melatonin rhythm is
diminished after an SCN lesion or at exposure to constant bright light,
as many other circadian rhythms (Perlov et al., 1981; Honma et al.,
1996; Perreau-Lenz et al., 2003; Tapia-Osorio et al., 2013). On the
other hand, the presence of functional melatonin receptors in the SCN
makes the master pacemaker susceptible to its action. The hormone
has been found to decrease the firing rate of SCN neurons, as well as
to modify neuronal activity of other brain nuclei (Mason and Brooks,
1988; Naranjo-Rodriguez et al., 1991; Liu et al., 1997). This ability
implicates that exogenous melatonin, given at a particular time of a
day, entrains and shifts the clock (Sharkey and Eastman, 2002). The
free-running activity rhythm in rodents, induced by exposure to
constant environmental conditions, has been synchronized to the
timing of the administration of the hormone. The entrainment was
achieved mostly by advancing the onset of activity towards the
injection time (Redman et al., 1983; Slotten et al., 1999; Pitrosky et
al. 1999). Moreover, melatonin was reported to accelerate the speed
of re-entrainment after the phase shift in the light-dark cycle (Illnerova
et al., 1989; Golombek and Cardinali, 1993; Sharma et al., 1999; Hirai
et al., 2005). Because of these chronobiological properties exogenous
melatonin and its analogues are considered as a potential therapeutic
treatment for jet lag and diseases in which circadian disturbances are
present (Srinivasan et al., 2008; Spadoni et al., 2010; Srinivasan et
al., 2010).
17
Rhythms description
Any rhythmic process can be described by four parameters:
period, mean level (MESOR), amplitude and acrophase. The period of
a rhythm is the duration of a full cycle, or in other words, the distance
from one peak to another. In circadian nomenclature different
notations for the endogenous period of an organism and the period of
the Zeitgeber are used: τ and T, respectively. There are several
methods for the determination of the period starting from a simple
visual inspection of the actogram (the graphical display of data along
two time axes) to numerical procedures such as autocorrelation,
Fourier transform, Enright (chi square) periodogram, and cosinor fit
(Enright, 1965; Halberg et al., 1967; Refinetti et al., 2007).
The mean of the rhythm represents a central value around
which the oscillation occurs. Although arithmetic mean is commonly
used for an estimation of a central tendency in statistic, rhythmadjusted mean: MESOR, yielded by the cosinor method, is more
accurate and precise in the analysis of circadian rhythms especially
when the data points are spaced unequally (Refinetti et al., 2007).
Amplitude is defined as a one-half of the distance between the
minimal and maximal value of the periodic function (Morris, 1992). If
the estimation of the parameter is based on cosine fitting procedure,
amplitude represents the distance between MESOR and peak or trough
of the wave (Fernandez et al., 2009).
Acrophase of the rhythm, a lag of the crest time of the cosine
curve fitted to the data from a defined reference time point, represents
the time at which a maximum value of the rhythms occurs. The
acrophase can be expressed in radians, degrees or hours (Refinetti et
al., 2007; Fernandez et al., 2009).
Three of the parameters: MESOR, amplitude and acrophase can
be computed by means of the cosinor method. The method assumes
18
that cosine curves of a known period can be fitted to the data by means
of a least squares method as an estimate of the pattern of the
smoothed rhythm (Halberg et al., 1967). The model can be described
by the equation:
xi = M + A cos (Θi + φ) + ei
xi – the value of the function at time t,
M – mesor,
A – amplitude,
Θ – angular frequency (the coefficient to translate from hours
to degrees),
φ – acrophase,
ei – an error term assumed to be independent and normally
distributed with mean zero and fixed unknown variance
Figure 1. Schematic representation of cosinor analysis and circadian
parameters
19
To compute mesor, amplitude and acrophase, a system of three
equations can be derived and solved in algebraic form.
A large advantage of the Cosinor method is that it allows the
estimation of all parameters (mesor, amplitude and acrophase) at once
together with confidence intervals. The method is thought to be
sensitive in detection of rhythmicity even in noisy data (low signal-tonoise ratio), and superior over others such as Fourier, Enright or LombScargle periodograms (Bingham et al., 1993).
There are two additional parameters used in the description of
circadian rhythms: waveform, informing about the shape of the
rhythm, and prominence (robustness) informing about rhythm’s
strength and endurance. Prominence is represented by the percentage
of the variance accounted for by the fitted cosine model, which
corresponds to the coefficient of determination R 2 in regression
analysis (Halberg et al., 1973). The waveform is determined according
to the Cosinor method by the harmonic content (the amplitude and
acrophase pairs of all harmonic terms included in the model) to account
for the non-sinusoidality of the signal.
Circadian rhythms in pathology and disease
Circadian control upon physiological processes makes their
occurrence predictable and indeed symptoms of some medical
conditions tend to manifest or worsen during a particular time of a day
(Smolensky and Peppas, 2007). For example, symptoms of immunerelated conditions such as allergic rhinitis or rheumatoid arthritis
aggravate usually during the night or upon awaking, both periods of
time coinciding with high concentration of pro-inflammatory agents
(Kowanko et al., 1982; Reinberg et al., 1988; Smolensky et al., 1995;
Petrovsky et al., 1998). Patients suffering from migraine attacks or
depression may expect worsening of the symptoms in the early
20
morning (Wehr, 1982; Solomon, 1992). During the night, the
symptoms of biliary colic and attacks of peptic ulcer are more frequent
(Moore and Halberg, 1987; Rigas et al., 1990) and cardiovascular
emergencies, e.g. sudden cardiac death, acute myocardial infarction or
stroke, occur predominantly in the morning (Cohen et al., 1997; Elliot,
1998; Portaluppi et al., 2012).
Epilepsy: circadian rhythms in the occurrence of
seizures
Interestingly,
opposite
to
pathologies
occurring
in
the
cardiovascular system, symptoms of some diseases such as epilepsy
show a high variability in the timing of their occurrence. A possible
explanation
might
be
that
epilepsy,
a
neurological
disorder
characterized by recurrent seizures, is not a homogenous disease. It
has many forms and underlying causes, which in fact may interfere
differently with the circadian timing system.
According to the World Health Organization (WHO) 50 million
people worldwide suffer from epilepsy, 80% of the cases are found in
developing regions. The incidence of the disease shows a bimodal
distribution with dominant peaks in childhood and in the elderly and a
slight gender-preference towards males (Kotsopoulos et al., 2002).
Considering
their
etiology,
epilepsies
may
be
classified
as
structural/metabolic, genetic and of unknown origin. In the first class,
termed previously “symptomatic”, the underlying cause of seizure
development is associated with structural or metabolic condition such
as head trauma, cerebral tumor or infection. Genetic epilepsies,
previously “idiopathic”, have a well-defined or presumed genetic
background with seizures being the core symptoms of the disorder
(Shorvon, 2010; Berg et al., 2010).
21
Epileptic seizures, clinical manifestation of which may vary from
general convulsions to brief moments of reduced consciousness
without
any
motor
signs,
result
from
abnormal
excessive
or
synchronous neuronal activity in the brain (Fisher, 2005). In general,
seizures may be classified as focal, they have their origin in a network
restricted to one hemisphere (usually to a small area in a particular
lobe), and generalized, then they have their origin at some point
within, rapidly engaging bilaterally distributed networks (Berg et al.,
2010). The recent, simplified classification of seizures recommended
by the International League Against Epilepsy (ILEA) is presented in
Table 1.
Classification of seizures
Generalized seizures
Tonic-clonic (in any combination)
Absence
Typical
Atypical
Absence with special features
Myoclonic absence
Eyelid myoclonia
Myoclonic
Myoclonic
Myoclonic atonic
Myoclonic tonic
Clonic
Tonic
Atonic
Focal seizures
Unknown
Epileptic spasm
Table 1. ILAE classification of seizures. Berg et al., 2010
22
A vast variety of seizure types, their location and underlying
mechanisms may contribute to a different circadian pattern of their
occurrence. A non-uniform distribution of convulsive seizures was first
described by Gowers (1885), who reported 21% of patients having
seizures exclusively during the night, 43% during the day, and 37% of
patients, whose seizures showed no preference for a particular time of
the day, appearing both during the day as well as during the night.
These results were then confirmed by Langdon-Down and Brain (1929)
who classified seizures as diurnal, nocturnal and diffuse. More recent
studies have revealed that seizures follow a distinct temporal pattern
depending on the location of the focus. Epileptic activity starting in the
temporal lobe (temporal lobe epilepsy, TLE) is most pronounced during
the day, while epileptic activity starting in the frontal (frontal lobe
epilepsy, FLE) and parietal lobe occurs more frequently during the night
(Quigg et al., 1998; Quigg and Straume, 2000; Pavlova et al., 2004;
Durazzo et al., 2008; Hofstra et al., 2009; Karafin et al., 2010;
Zarowski et al., 2010; Loddenkemper et al., 2011). Another important
factor contributing to a non-uniform occurrence of seizures across the
24 h day is the state of vigilance. TLE seizures tend to occur during
wakefulness, while FLE seizures during sleep. Focal seizures may
become generalized seizures and this happens more often during sleep.
Also during sleep (stages I and II of slow-wave sleep), the majority of
generalized seizures with spike-wave pattern are recorded (Kellaway
et al., 1980; Halasz, 1991; Bazil and Walczak, 1997; Crespel et al.,
2000; Pavlova et al., 2004; Sinha et al., 2006; Hofstra et al., 2009;
Ramgopal, 2012). Human studies analyzing the diurnal variation of
seizures and the relationship between seizures and states of vigilance
are presented in Table 2.
The majority of the studies describing the daily rhythms in
epileptic activity was conducted in medical facilities, with strictly
controlled environmental conditions providing entrainment. However,
23
to confirm a true circadian nature of a rhythmic process, it is necessary
to exclude the presence of Zeitgebers. Two studies to date addressed
the issue, and only one used humans with epilepsy. Pavlova and coworkers investigated the putative circadian variation of interictal
epileptiform discharges (IED) in patients subjected to a forced
desynchrony protocol, a method in which all behaviors are evenly
scheduled across all circadian phases with environmental conditions
maintained constant. In this pilot study circadian variation in IED
independently of sleep-wake states was found, although in a small
number of patients with a high variability in the phase of IED no
circadian rhythm could be established (Pavlova et al., 2009).
The group of Quigg investigated the occurrence of seizures in a
rat model of TLE. Animals were kept in 12:12 light-dark cycle and then
transferred to constant darkness. In this condition, a circadian rhythm
in the occurrence of seizures was preserved and this proved its
endogenous origin (Quigg et al., 2000).
24
Authors
Design
Kellaway et al.
R
(1980)
N
(seizures)
Sleep/wake
19 adult,
SWDs more
children
frequent in
(N/A)
SWS than in
Diurnal pattern
EEG
N/A
S
S
wake and REM
Martins da Silva
R
et al. (1984)
17 adults
IEDs more
IEDs peak 23:00 –
(N/A)
frequent
07:00 and 09:30 –
during SWS
11:00
than in REM
Horita et al.
R
(1991)
4 children
SWDs most
SWDs concentrated in
(N/A)
frequent
the last third of
during SWS
nocturnal sleep
S
stage I
Bazil and
R
Walczak (1997)
188 adults
37% FLE
(1116)
sleep, 74%
N/A
S
N/A
S
LTLE, XTLE occur
S
TLE wake
Crespel et al.
R
(1998)
30 adults
61% FLE
(133)
sleep, 89%
TLE wake
Quigg et al.
R
(1998)
98 adults
N/A
(1350)
randomly, MTLE peak
at 15:00
Quigg and
C
1 (1009)
N/A
Straume (2000)
TL seizure peak at
N/A
12:10, PL seizure peak
at 02:50
Herman et al.
(2001)
P
133 adults
57% FLE, 43% N/A
(613)
TLE, 40%
MTLE, 24%
NTLE and 13%
OLE in sleep
25
S
Minecan et al.
R
(2002)
55 adult,
children
(117)
61% in stage
N/A
S
2, 20% in
stage 1, 14%
in stage 3 – 4
SWS, 5% in
REM
Pavlova et al.
R
(2004)
26 adults
41% XTLE and TLE peak 15:00 –
(90)
19% TLE in
19:00 (50%), XTLE
sleep
peak 19:00 – 23:00
S
(47%)
Sinha et al.
R
(2006)
75 adults
Secondary
(362)
generalization
N/A
S
MTLE peak 16:00 –
I
more
frequently in
sleep
Durazzo et al.
R
(2008)
131 adults
N/A
(669)
19:00 and 07:00 –
10:00, NTLE 16:00
19:00, FLE and PLE
04:00 – 07:00, OLE
16:00 – 19:00
Hofstra et al.
R
(2009 a)
100 adults,
Children: 36% TLE peak 05:00 –
76 children
TLE and 12%
11:00 (34%) and
(808)
XTLE in sleep;
11:00 – 17:00 (34%)
adults: 13%
in children and 11:00
TLE and 30%
– 17:00 in adults
XTLE in sleep
(37%); XTLE peak
S
11:00 - 17:00 (40%)
in children
Hofstra et al.
(2009 b)
R
26 adults, 7 88% MTLE,
TLE peak 11:00 –
children
83% NTLE,
17:00, FLE 23:00 –
(450)
83% PLE in
05:00, PLE 17:00 –
wake; 100%
23:00
FLE in sleep
26
I
Zarowski et al.
R
(2010)
100
GE more
GE peak 06:00 –
children
frequent
12:00, FLE 03:00 –
(456)
during wake,
06:00
S
focal epilepsy
during sleep
Karafin et al.
R
(2010)
Loddenkemper
R
et al. (2011)
60 adults
N/A
MTLE peak 06:00 –
(median
08:00 and 15:00 –
10/patient)
17:00
225
GE, TLE more
GE peak 06:00 –
children
frequently
12:00, TLE 21:00 –
(1008)
during wake,
09:00, FLE 00:00 -
PLE, FLE MLE
06:00, PLE 06:00 –
in sleep,
09:00, OLE 09:00 –
S
S
12:00 and 15:00 –
18:00
Pavlova et al.
(2012)
R
44 adults
N/A
(129)
FLE cluster around
S
05:15 – 07:30, TLE
cluster around 18:45 –
23:56
Table 2. Studies in humans with epilepsy analyzing the diurnal variation of the
occurrence of seizures and the relationship between seizures and sleep-wake
states. Focal seizures restricted to the temporal lobe tend to occur during
daytime and in wakefulness. Frontal lobe and secondarily generalized seizures
are more frequent during sleep. Time in 24 hour clock mode. Study design: C
– case report, P – prospective, R – retrospective. Epilepsy syndromes: FLE –
frontal lobe epilepsy, MTLE – mesial temporal lobe epilepsy, NTLE – neocortical
temporal lobe epilepsy, GE – generalized epilepsy, FLE – frontal lobe epilepsy,
PLE – parietal lobe epilepsy, OLE – occipital lobe epilepsy, TLE – temporal lobe
epilepsy, XTLE – extra temporal lobe epilepsy. EEG: I – intracranial, S –
surface. IEDs – interictal epileptiform discharges, SWDs – spike-wave
discharges, SWS – slow-wave sleep, REM – rapid eye movement sleep, N/A –
information not available. Modified after Loddenkemper et al., 2011
Childhood absence epilepsy (CAE)
Childhood absence epilepsy (CAE) belongs to a group of genetic,
generalized epilepsies. A positive family history is present in 14 to 45%
27
of these people and a concordance of 70 – 85% in monozygotic twins
and 30% in first degree relatives (Berkovic, 1998; Bianchi et al., 1995).
The genotype of the disease is complex, including autosomal-dominant
gene(s) mutation(s) determining typical EEG trait and channelopathies
(Crunelli and Leresche, 2002). CAE is an age-related syndrome with
onset between 4 and 10, most usually between 5 and 7 years of age,
and the annual incidence rate of 6.3 – 8.0/100000 (Blom et al., 1978;
Olsson, 1988; Loiseau et al., 1990). It is characterized by frequent (10
to hundreds per day) short (about 10 sec.), typical absence seizures
occurring
awareness
with
and
severe
impairment
responsiveness)
in
of
consciousness
otherwise
healthy
(reduced
children.
Clinically, absences manifests as an abrupt decrease of consciousness
with cessation of ongoing voluntary activity accompanied by eyes
opening, accelerated breathing and, quite frequently, by automatisms.
The ictal EEG is characterized by the presence of 2.5 - 4 Hz bilateral,
generalized, synchronous, symmetrical spike-wave discharges (SWDs)
of sudden onset and termination (Duncan, 1997). Besides CAE, typical
absences are the key symptoms in the following epilepsy syndromes
recognized by the ILAE: juvenile absence epilepsy (JAE), juvenile
myoclonic epilepsy (JME) and myoclonic absence epilepsy (MEA)
(Commission, 1981; Panayiotopoulos 2001). Differences in the age of
onset, the frequency of SWDs and a prognosis are syndrome-related
(Duncan,
1997).
Proper
diagnosis
and
implementation
of
pharmacological treatment ensure 80% of patients becoming seizuresfree. Ethosuximide and sodium valproate are established level A
medications (efficacious or effective) as an initial monotherapy for
children with newly diagnosed or untreated absence seizures,
according to updated ILAE evidence review of antiepileptic drug
efficacy and effectiveness (Glauser et al., 2013). Medications such as
carbamazepine, phenytoin, tiagabine or vigabatrin, usually prescribed
to attenuate convulsive seizures, have opposite effect: precipitating or
28
even aggravating SWDs (Peeters et al., 1988; Coenen et al., 1995;
Depaulis and van Luijtelaar, 2006; Bouwman et al., 2007; Glauser et
al., 2013).
Generalized absence seizures are neurophysiologically and
pharmacologically unique. The basic underlying mechanism appears
the involvement of the cortico-thalamo-cortical neuronal network. In
the course of investigations, two conflicting theories about the source
of the pathological oscillations emerged. Initial studies implicated the
thalamus as the pacemaker of seizure activity (centrencephalic and
thalamic clock theory), while other experimental work delivered
evidence for the leading role of the cortex (cortical theories) (Futatsugi
and Riviello Jr., 1998; Meeren et al., 2005; Avoli, 2012). Recent studies
performed with the use of well characterized and validated genetic
absence animal models revealed that spike-wave discharges (SWDs),
the electroencephalographic hallmark of the absences, are generated
in the deep layers of the somatosensory cortex (perioral area) which
at the beginning of generalization process, guide the thalamus. After
this initial period, the cortex and thalamus enter a bidirectional
oscillatory cross-talk mode (Meeren at al., 2002; Polack et al., 2007;
Lüttjohann and van Luijtelaar, 2012). Evidence is accumulating that
also in humans with absence seizures the initiating side of SWDs might
be cortical as well (Westmijse et al., 2009; van Luijtelaar et al., 2014).
γ-aminobutyric acid (GABA) and glutamate are the most important
neurotransmitters involved in the pathophysiology of absence epilepsy
governing cortico-thalamo-cortical networks. The activity in these
networks is modulated by cholinergic, dopaminergic and noradrenergic
neurotransmission (Snead, 1995).
Absence seizures are strongly related to the level of vigilance.
The relationship is inversely proportional: the number of SWDs
increases together with decreasing vigilance. SWDs in patients occur
predominantly during drowsiness, initial stages (I and II) of slow-wave
29
sleep and during unstable transitional periods (Halasz et al., 2002).
Active wakefulness and REM sleep, both characterized by high cortical
desynchronization, are the least probable for the generation of SWDs.
This relationship and consolidated character of human sleep, restricted
on average to 8 night hours, may contribute to the circadian
distribution of absence seizures in patients with SWDs (Kellaway et al.,
1980, Martins da Silva et al., 1984; Horita et al., 1991).
WAG/Rij rats: a well characterized and validated
genetic animal model of CAE
To be considered as valid, an animal model of human seizures
or epilepsy should fulfill the following criteria. First, it should have
sufficient “face validity” which implies similar electroencephalographic,
behavioral, etiological and pharmacological characteristics to the
human condition. Second, the model should have “construct validity”
which means shared theoretical etiology with the disease being
modeled. Third, having “predictive validity” implies that the model
should correctly predict treatment effects in patients (Sarkisian, 2001;
Coenen and van Luijtelaar, 2003).
Animal models of epilepsy or epileptic seizures may be generally
divided into two groups: models in which seizures are induced by
electrical or chemical stimulation (acquired epilepsy), and genetic
epilepsy models, in which animals either carry spontaneous mutations
or the mutations are induced by transgenic manipulations and the
seizures are expressed spontaneously (Löscher, 2002; Löscher, 2011).
Chemical induction of absence seizures in previously healthy animals
may be accomplished by systemic or intracerebroventricular injections
of several classes of compounds such as pentylenetetrazole (PTZ) in
low
doses,
penicillin,
γ-hydroxybutyrate
(GHB),
4,5,6,7-
tetrahydroisoxazolo[4,5,-c ]pyridine-3-ol (THIP) or opiates (Snead,
30
1992; Avoli, 1995; Sarkisian, 2001). Among genetic models murine
strains are of a particular use. There is a variety of mice strains with a
well-known mutation restricted to a single gene, encoding usually
different subunits of calcium channel which results in the epileptic
phenotype. Lethargic (lh/lh), stargazer (stg/stg), tottering (tg/tg),
leaner (tgla/tgla) or duky (du/du) mice all exhibit SWDs in the EEG.
However, apart from the presence of absence seizures, they show
additional severe pathological symptoms such as ataxia or motor
disabilities, which make their compatibility with CAE questionable
(Noebels, 2006). Opposite to single locus mutations and
the
concomitance of neurological abnormalities, spontaneous SWDs in rat
models: Wistar Albino Glaxo from Rijswijk (WAG/Rij) and Genetic
Absence Epilepsy Rats from Strasbourg (GAERS), are determined
polygenetically. Additionally to the mice models, their absence epileptic
phenotype is not accompanied by neurological impairments. WAG/Rij
rats and GAERS are full (>30 generations brother x sister mating)
inbred
strains
derived
from
Wistar
stocks,
sharing
similar
electroencephalographic, behavioral and pharmacological features.
Both strains were extensively validated in a large series of behavioral,
electrophysiological
and
pharmacological
experiments
and
are
currently world-wide used as the best described and validated genetic
animal models of human absence epilepsy (Danober et al., 1998;
Coenen and van Luijtelaar, 2003; Depaulis and van Luijtelaar, 2006).
Although best described, they are not the only rat strains characterized
by the presence of spontaneous SWDs in the EEG. Rats, especially of
inbred albino strains such as Wistar or Fisher 344, but also pigmented
outbred lines such as Long-Evans or Brown-Norway develop SWDs
during the process of aging (Inoue et al., 1990; Willoughby and
Mackenzie, 1992; Pearce et al., 2014). However, much less validation
studies were done in these latter strains and therefore their predictive
and constructive validity is questionable.
31
All WAG/Rij rats start showing SWDs in their EEG against a
normal background activity around 2 – 3 months of age; these SWDs
increase in number and duration in the course of animals maturation.
Irrespectively of gender, at 6 months of age each member of the strain
shows about 16 – 20 SWDs per hour lasting on average 5 sec (Coenen
and van Luijtelaar, 2003). Importantly, the behavioral manifestations
of absences in the rats resemble the clinical manifestation as seen in
people with absence epilepsy. Animals and people become immobile
during absence seizures, processing of external stimuli and execution
of voluntary motor tasks are temporarily interrupted, additional mild
motor signs such as facial myoclonic jerks emerges, in rats this is most
clearly expressed by twitching of the vibrissae and/or eyes (van
Luijtelaar and Coenen, 1986; Drinkenburg et al., 1996; Drinkenburg et
al., 2003). The frequency of SWDs in rat models is between 7 – 11 Hz,
starting from about 10-11 Hz at the beginning of the discharge and
lowering slightly towards end (Midzianovskaia et al., 2001; Bosnyakova
et al., 2007). Although bilateral and generalized, SWDs in WAG/Rij rats
show a fronto-parietal domination, moreover the spike component is
more pronounced at the anterior part of the cortex, while the wave has
a larger amplitude at more posterior locations (Midzianovskaia et al.,
2001; Meeren et al., 2002; Sitnikova and van Luijtelaar, 2007).
SWDs in WAG/Rij rats occur most frequently during passive
behavioral
states
such
as
light
slow-wave
sleep
and
passive
wakefulness. Unstable, transitional periods between different states of
vigilance also favor seizure generation, on the contrary, states
characterized by extreme level of cortical synchrony (deep slow-wave
sleep) or desynchronization (active wakefulness, REM sleep) are the
least probable for SWDs to occur. This relationship corresponds well
with the observation made in patients with absence epilepsy (Kellaway
et al., 1980; Coenen et al., 1991; Drinkenburg et al., 1991; Halasz et
al., 2002). The distribution of SWDs across 24 h is not uniform.
32
Absences recur daily in a rhythmic manner with an acrophase at the
early hours of the dark phase and a minimum at the beginning of the
light phase of the 12:12 light-dark cycle (van Luijtelaar and Coenen,
1988). The timing of the minimum of the SWD rhythm coincides with
the timing of the highest amount of deep slow-wave sleep across the
sleep-wake cycle of the 24 h day. Since SWDs preferentially do not
occur during deep slow-wave sleep (Drinkenburg et al., 1991), the
large presence of deep slow-wave sleep might prevent the occurrence
of SWDs. Only the number of SWDs shows a circadian distribution, not
the mean duration of episodes of SWDs.
Epileptic seizures originate from local imbalances between
excitation and inhibition. Convulsive epilepsy is associated with
excessive excitation mediated mainly by glutamate, often in a well
circumscribed part of the hippocampal circuit. Increased local cortical
excitation and enhanced GABA-ergic thalamic inhibition seems to be
the underlying cause of absence seizures. Indeed, not only GABA
agonists increase the number of SWDs, GABA antagonists exert an
opposite effect (Peeters et al., 1989; Kaminski et al., 2001). Recent
work shows that GAERS absence epilepsy is due to an increased tonic
GABA-ergic inhibition in the ventro-basal complex of the thalamus,
while in WAG/Rij rats an enhanced local increase in cortical excitability
has been found (Cope et al., 2009; Crunelli et al., 2011; Lüttjohann et
al., 2012).
A hyperfunction of the glutamatergic system has been found to
facilitate epileptic activity regardless of the type: convulsive versus
absence epilepsy and receptors involved (Peeters et al., 1990;
Ramakers et al., 1991; Peeters et al., 1994). However, potentiation of
two types of metabotropic glutamate receptors, mGlu1 and mGlu5 by
positive allosteric modulators, has been recently described to attenuate
absence seizures in WAG/Rij rats (Ngomba et al., 2011; D’Amore et
al., 2013).
33
Apart from GABA and glutamate, SWDs are also modulated by
catecholamine neurotransmission. Administration of the non-selective
dopamine agonist apomorphine was found to reduce the number of
SWDs, while treatment with the antagonist haloperidol exerted an
opposite
effect
(Midzianovskaia
et
al.,
2001).
Attenuation
of
noradrenergic transmission by low doses of clonidine, an alpha-2
noradrenergic receptor agonist, caused an increase in the occurrence
of absence seizures and altered activity of the cortex and the thalamus
of WAG/Rij rats (Sitnikova and van Luijtelaar, 2005). Also serotonin, a
neurotransmitter
from
the
monoamine
family,
modulates
the
occurrence of absence seizures in a receptor-dependent manner.
Agonist of 5-HT1A receptor (8-OH-DPAT) increased the number of
SWDs, agonist of 5-HT2C receptor and antagonist of 5-HT-7 receptor
decrease SWDs (Filakovszky et al., 1999; Jakus et al., 2003; Graf et
al., 2004).
The phenotype of WAG/Rij rats is also characterized by having
depressive-like characteristics (Sarkisova and van Luijtelaar, 2011).
Also in humans there is a high comorbidity between epilepsy and
depression. Dopaminergic and serotonergic systems, more precisely,
their strain-dependent structural and functional abnormalities are
thought to contribute to the depressive-like behavior of WAG/Rij rats.
In a comparison to non-epileptic control strain WAG/Rij rats show clear
behavioral changes corresponding to key symptoms of human
depression: anhedonia, despair and helplessness. Anhedonia was
manifested by a decreased sucrose intake in the sucrose consumption
test, lower preference for sucrose over tap water in the two bottle
choice test, and decreased sensitivity to reward have been reported in
WAG/Rij rats (Sarkisova et al., 2003). In the forced swim test (FST)
used for assessment of depressive-like behavior (behavioral despair)
and evaluation of anti-depressive medication efficacy, WAG/Rij rats
showed a significant increase in immobility score and a decrease in
34
active behavior, interpreted as a helplessness reaction typical of
humans
suffering
from
depression.
Results
of
the
test
were
subsequently reversed by chronic treatment with antidepressant
medication (Sarkisova et al., 2008) and by drugs that induce
antiepileptogenesis (Sarkisova et al., 2010; van Luijtelaar et al.,
2013).
As mentioned, the rhythmic occurrence of SWDs in WAG/Rij rats
might be related to endogenous cycles, such as sleep-wake and
hormonal cycles. The most obvious is an inversely proportional
relationship between SWDs and deep slow-wave sleep across 24 h. The
acrophase of the SWDs rhythm coincides with the trough of the rhythm
of deep slow-wave sleep and deprivation of sleep including deep slowwave sleep enhances the number of SWDs (Drinkenburg et al., 1995).
The distribution of SWDs may be also determined by a circadian rhythm
of free glucocorticoids circulating in the blood and in the brain. The
highest plasma, brain and also tissue concentration of corticosterone
in the rat is found 1 – 2 h before the dark phase. In the course of the
day, the level of corticosterone declines, reaching a trough just before
the light phase, during which the concentration is building up again
(Qian et al., 2012). Both exogenously administrated corticosterone, as
well as the endogenous form of the hormone secreted in response to
stress, increase the number of SWDs (Schridde and van Luijtelaar,
2004; Tolmacheva et al., 2012). Glucocorticoids are thought to take
part in the synchronization process between central and peripheral
oscillators, therefore, it can’t be excluded that the rhythm of SWDs is
driven, to some extent by cycling corticosterone. Not only might
circadian, but also infradian rhythms influence the occurrence of SWDs.
It has been demonstrated that the ovarian hormone progesterone,
cycling with a period of 4 days in female WAG/Rij rats, increases the
number of SWDs during the dark phase of the proestrus period (van
35
Luijtelaar et al., 2001), a phenomenon that has also been found in
patients (van Luijtelaar et al., 2014).
Aims and outline of the thesis
Considering its etiology, childhood absence epilepsy is classified
as a genetic type of epilepsy implicating that the onset of the disease
results from a presence of specific genotype. However, during
development and once developed, many other factors contribute to the
occurrence of absence seizures, as found in children with CAE and in
the rodent models. In general, the character of the factors contributing
to the fully developed SWDs may be either endogenous or external. A
bidirectional interaction is also possible: endogenous mechanisms
might be modified by external influences; exogenous factors may act
differently depending on the state of an internal environment.
Rhythmic occurrence of SWDs in WAG/Rij rats, especially its
day-to-day consistency, suggests a possible involvement of the
internal time keeping mechanism in the phenomenon observed. The
nature of this contribution however, has not been fully described (van
Luijtelaar and Coenen, 1988). When generated endogenously, the
rhythm of SWDs should persist in an environment without functional
Zeitgebers. In case SWDs are imposed by the presence of at least one
of them, circadian distribution of seizures should no longer be detected.
A light-dark cycle, one of the strongest Zeitgebers in many living
species, including rats, has a great impact on the internal clock.
Sudden changes in this cycle: phase shifts, phase reversal or, the most
extreme: constant conditions or photoperiods that exceed the range of
entrainment, are known to disturb rhythms and their well-defined
phase-relationships (Eastman and Rechtschaffen, 1982; Yamazaki et
al., 2000; Nagano et al., 2003). Internal desynchronization, a result of
external-internal
misalignments,
36
is
found
to
have
negative
consequences for health and well-being (Arendt and Marks, 1982;
Knutsson, 2003). Symptoms of many medical conditions, also epileptic
seizures, occur at specific time of a day (Smolensky and Peppas, 2007).
WAG/Rij rats show enhanced epileptic activity during the dark phase.
However, it is not clear, whether abrupt changes in the photoperiod
would affect the distribution of seizures (regardless of the origin of this
distribution: internal or external). Moreover, it is of potential medical
significance to evaluate the effect of the phase shift on circadian
physiology and seizure activity in this particular epileptic model in order
to propose better hypotheses about the effects in humans.
Apart from rhythmic alternation of light and darkness, the
occurrence of seizures is influenced by many other factors, some of
them, e.g. the sleep-wake cycle, occurring rhythmically as well (van
Luijtelaar et al., 2001; Tolmacheva et al., 2012). The contribution of a
momentary state of vigilance is well established: SWDs occur more
often during passive wakefulness and light slow-wave sleep, while
active wakefulness, deep slow-wave and REM sleep exert an inhibitory
effect (Drinkenburg et al., 1991). However, questions about variability
of this relationship across 24 h, as well as, about its stability in the face
of different states of the circadian timing system have not been
investigated before. The distribution of vigilance states across 24 h
sleep-wake cycle is not uniform. Controlled by the circadian timing
system, each vigilance rhythm peaks at different time of a day
(Borbély, 1975; Ibuka and Kawamura, 1975). Interestingly, daily
rhythmic occurrence of absence seizures in WAG/Rij rats may be
clearly explained by means of a momentary state of vigilance only
when deep slow-wave sleep is considered. The maximal number of
SWDs seen during the dark phase and a minimum of the rhythm seen
at the beginning of the light phase coincides with the lowest and the
highest amount of deep slow-wave sleep, respectively. In case of the
other relationships between SWDs on the one side and the remaining
37
vigilance rhythms on the other such dependencies are rather blurred
or even opposite. Therefore, SWDs-state of vigilance relationship
requires detailed analysis across time.
Chronobiotic (aiming at adjusting the circadian timing system)
properties of exogenous melatonin and its analogues are well
recognized. The hormone is reported to entrain free-running circadian
rhythms in constant conditions, to produce phase shifts and also to
accelerate re-entrainment after such shifts. Melatonin is often
recommended as a remedy for the symptoms accompanying jet lag
(Srinivasan et al., 2008). Besides the circadian timing system,
exogenous melatonin was found to modulate epileptic activity exerting
either a convulsive or antiepileptic effect depending on the type of
seizures (Lapin et al., 1998; Musshoff and Speckmann, 2003; Yildirim
and Marangoz, 2006). However, studies assessing the action of this
chronobiotic on absence seizures are scarce. Melatonin given in high
doses to WAG/Rij rats, entrained to the regular light-dark cycle, was
found to attenuate SWDs (Kldiashvili et al., 2001). However, the study
focused on direct action of the compound rather than its chronobiotic
properties. Therefore, it would be advantageous to evaluate the action
of melatonin or melatonergic agonists on the rhythm of SWDs in
WAG/Rij rats in two experimental conditions: during entrainment and
after the phase shift in the photoperiod resembling jet lag.
The overall aim of the current thesis is to determine the
contribution of both types of factors: endogenous and external, to the
occurrence of absence seizures in WAG/Rij rats. The thesis aims to
answer the following questions:
1. What is the relationship between the occurrence of SWDs
and other internal factors such as the state of vigilance? Is
this relationship constant across 12:12 light-dark cycle? Is
the relationship modified by the state of the circadian timing
system: entrained versus free-running?
38
2. What is the nature of a daily rhythmic occurrence of SWDs?
Is it endogenous, generated and governed by the circadian
timing system or is the occurrence of SWDs masked by a
presence of external factors such as a light-dark cycle?
3. How does the rhythm of SWDs entrain to the main Zeitgeber
(light-dark cycle) and how does it respond to serious
disturbances of the entrainment provided by this particular
Zeitgeber?
4. Are
external
factors
with
documented
chronobiotic
properties able to influence the occurrence of SWDs and
sleep-wake
states,
their
rhythmic
occurrence
during
entrainment and during re-entrainment? Are they able to
accelerate the speed of re-entrainment of these rhythms?
Chapter 1 provides introductory information to the issues
addressed in the thesis.
In Chapter 2 the relationship between SWDs and states of
vigilance is investigated. Two approaches were compared: momentary
and circadian. The former, well-established one, focused on sleep-wake
states directly preceding the occurrence of SWDs, while the latter was
based on correlations and cross-correlations calculated over whole 24
h and also over the light and the dark phase separately. The duration
of particular sleep-wake states scored on 2 sec epoch basis, grouped
subsequently into three different bin sizes, was correlated and crosscorrelated with a total time occupied by SWDs to establish temporal
relationship between these variables across 24 h as well as in the light
and dark period separately.
In Chapter 3 the nature of the daily rhythm of the occurrence
of absence seizures in WAG/Rij rats is investigated. To establish the
character of the SWDs rhythm: endogenous or imposed, rats were
transferred from standard laboratory conditions, in which entrainment
39
was provided by the presence of the 12:12 light-dark cycle, to an
environment in which a regularly alternating level of illumination was
replaced by constant dim light. Continuous recordings of the EEG and
motor activity were made during 10 baseline and the last 10
experimental days of a 20 days period in constant dim light. During the
first 10 days in constant dim light only motor activity was monitored to
assess whether an intrinsic period of investigated rhythms was
stabilized. The occurrence of SWDs, their duration, coupling with motor
activity rhythm and sleep-wake states (dependent variables) were
analyzed and compared between the 10 days with and the last 10 days
without the presence of Zeitgebers (independent variable).
Chapters 4 and 5 focus on the entrainment provided by the
12:12 light-dark cycle and consequences of temporary disturbances in
the timing of this potent Zeitgeber for the occurrence of absence
seizures, their duration and coupling with different sleep-wake states.
In both studies WAG/Rij rats were subjected to an 8 h phase shift in
the light-dark cycle which was produced by extending the dark phase
from 12 to 20 h. Re-entrainment and the speed of re-entrainment to
the new photoperiod was then investigated for the 10 day follow up
period. Simultaneous and continuous recordings of EEG, EMG and
motor activity (body movement detected by passive infrared sensor)
were performed throughout the experiments which lasts 15 and 12
days in chapter 4 and 5, respectively. In Chapter 4, the process of resynchronization was undisturbed while in Chapter 5 the putative effects
of agomelatine, a potent agonist of melatonin receptors and antagonist
of serotonin receptors on sleep-wake parameters, SWDs and speed of
re-entrainment
was
additionally
assessed.
The
compound
was
administered, in a dose-dependent manner, starting in an acute study,
followed by the study of its effects in a chronic administration (10 days)
from the first day after the phase shift and continued throughout the
study.
40
A general discussion, conclusions and a summary of the thesis
is presented in Chapter 6.
References
Abe M, Herzog ED, Yamazaki S, Straume M, Tei H, Sakaki Y, Menaker M, Block GD.
2002. Circadian rhythms in isolated brain regions. J Neurosci. 22: 350-6.
Achermann P, Borbély AA. 1990. Simulation of human sleep: ultradian dynamics of
electroencephalographic slow-wave activity. J Biol Rhythms. 5: 141-57.
Achermann P, Dijk DJ, Brunner DP, Borbély AA. 1993. A model of human sleep
homeostasis based on EEG slow-wave activity: quantitative comparison of data
and simulations. Brain Res Bull. 31: 97-113.
Agostino PV, Golombek DA, Meck WH. 2011. Unwinding the molecular basis of interval
and circadian timing. Front Integr Neurosci. 5: 64.
Arendt J, Marks V. 1982. Physiological changes underlying jet lag. Br Med J (Clin Res
Ed) 284:144–6.
Aschoff J. 1981. A survey on biological rhythms. In Handbook of behavioral
neurobiology. Volume 4. Biological rhythms. J. Aschoff (Editor). Plenum Press,
New York, London. ISBN 0-306-40585-7
Atkinson HC, Waddell BJ. 1997. Circadian variation in basal plasma corticosterone and
adrenocorticotropin in the rat: sexual dimorphism and changes across the
estrous cycle. Endocrinology. 138: 3842-3848.
Avoli M. 1995. Feline generalized penicillin epilepsy. Ital J Neurol Sci. 16: 79-82.
Avoli M. 2012. Herbert H. Jasper and the Basic Mechanisms of the Epilepsies. In:
Jasper's Basic Mechanisms of the Epilepsies. 4th edition. Eds. Noebels JL, Avoli
M, Rogawski MA, Olsen RW, Delgado-Escueta AV. Bethesda (MD): National
Center for Biotechnology Information (US).
Bazil CW, Walczak TS. 1997. Effects of sleep and sleep stage on epileptic and
nonepileptic seizures. Epilepsia. 38: 56-62.
Berg AT, Berkovic SF, Brodie MJ, Buchhalter J, Cross JH, van Emde Boas W, Engel J,
French J, Glauser TA, Mathern GW, Moshé SL, Nordli D, Plouin P, Scheffer IE.
2010. Revised terminology and concepts for organization of seizures and
epilepsies: report of the ILAE Commission on Classification and Terminology,
2005-2009. Epilepsia. 51: 676-685.
Berkovic SF. 1998. In Epilepsy: a Comprehensive Textbook. Eds. Engel J Jr, Pedley TA,
Lippincott–Raven, Philadelphia pp 217–224.
41
Bianchi A, The Italian League Against Epilepsy Collaborative Group in Typical Absences
and Related Epileptic Syndromes. 1885. Eds. Duncan JS, Panayiotopoulos CP.
Churchill Livingstone, London, pp 328-337.
Bingham C, Cornélissen G, Halberg F. 1993. Power of “Phase 0” chronobiologic trials at
different signal-to-noise ratios and sample sizes. Chronobiologia. 20:179–190.
Blask DE. 2009. Melatonin, sleep disturbance and cancer risk. Sleep Medicine Reviews
13: 257-264.
Blom S, Heijbel J, Bergfors PG. 1978. Incidence of epilepsy in children: a follow-up study
three years after the first seizure. Epilepsia. 19: 343-350.
Borbély AA, Huston JP, Waser PG. 1975. Control of sleep states in the rat by short lightdark cycles. Brain Res. 95: 89-101.
Bosnyakova D, Gabova A, Zharikova A, Gnezditski V, Kuznetsova G, van Luijtelaar G.
2007. Some peculiarities of time-frequency dynamics of spike-wave discharges
in humans and rats. Clin Neurophysiol. 118: 1736-1743.
Bouwman BM, Suffczynski P, Midzyanovskaya IS, Maris E, van den Broek PL, van Rijn
CM. 2007. The effects of vigabatrin on spike and wave discharges in WAG/Rij
rats. Epilepsy Res. 76: 34-40.
Bronstein DM, Jacobs GH, Haak KA, Neitz J, Lytle LD. 1987. Action spectrum of the
retinal mechanism mediating nocturnal light-induced suppression of rat pineal
gland N-acetyltransferase. Brain Res. 406: 352-356.
Buijs RM, Kalsbeek A. 2001. Hypothalamic integration of central and peripheral clocks.
Nat Rev Neurosci. 2: 521-6.
Bünning E. 1964. The Physiological Clock. Springer Verlag, Berlin.
Buonomano DV, Karmarkar UR. 2002. How do we tell time? Neuroscientist. 8: 42-51.
Buonomano DV. 2007. The biology of time across different scales. Nat Chem Biol. 3:
594-7.
Chan S, Debono M. 2010. Replication of cortisol circadian rhythm: new advances in
hydrocortisone replacement therapy. Ther Adv Endocrinol Metab. 1: 129-38.
Chiazze L Jr, Brayer FT, Macisco JJ Jr, Parker MP, Duffy BJ. 1968. The length and
variability of the human menstrual cycle. JAMA. 203(6):377-80.
Coenen AM, Drinkenburg WH, Peeters BW, Vossen JM, van Luijtelaar EL. 1991. Absence
epilepsy and the level of vigilance in rats of the WAG/Rij strain. Neurosci
Biobehav Rev. 15: 259-263.
42
Coenen AM, Blezer EH, van Luijtelaar EL. 1995. Effects of the GABA-uptake inhibitor
tiagabine on electroencephalogram, spike-wave discharges and behaviour of
rats. Epilepsy Res. 21: 89-94.
Coenen AM, Van Luijtelaar EL. 2003. Genetic animal models for absence epilepsy: a
review of the WAG/Rij strain of rats. Behav Genet. 33: 635-655.
Cohen MC, Rohtla KM, Lavery CE, Muller JE, Mittleman MA. 1997. Meta-analysis of the
morning excess of acute myocardial infarction and sudden cardiac death. Am J
Cardiol. 79: 1512-1516.
Commission of Classification and Terminology of the International League
Against Epilepsy. Proposal for revised clinical and electroencephalographic
classification of epileptic seizures. 1981. Epilepsia. 22: 489-501.
Cope DW, Di Giovanni G, Fyson SJ, Orbán G, Errington AC, Lorincz ML, Gould TM, Carter
DA, Crunelli V. 2009. Enhanced tonic GABAA inhibition in typical absence
epilepsy. Nat Med. 15: 1392-1398.
Crespel A, Baldy-Moulinier M, Coubes P. 1998. The relationship between sleep and
epilepsy in frontal and temporal lobe epilepsies: practical and physiopathologic
considerations. Epilepsia. 39: 150-157.
Crunelli V, Leresche N. 2002. Childhood absence epilepsy: genes, channels, neurons
and networks. Nat Rev Neurosci. 3: 371-382.
Crunelli V, Cope DW, Terry JR. 2011. Transition to absence seizures and the role of
GABA(A) receptors. Epilepsy Res. 97: 283-289.
Dai J, Swaab DF, Buijs RM. 1997. Distribution of vasopressin and vasoactive intestinal
polypeptide (VIP) fibers in the human hypothalamus with special emphasis on
suprachiasmatic nucleus efferent projections. J Comp Neurol. 383:397-414.
D'Amore V, Santolini I, van Rijn CM, Biagioni F, Molinaro G, Prete A, Conn PJ, Lindsley
CW, Zhou Y, Vinson PN, Rodriguez AL, Jones CK, Stauffer SR, Nicoletti F, van
Luijtelaar G, Ngomba RT. 2013. Potentiation of mGlu5 receptors with the novel
enhancer, VU0360172, reduces spontaneous absence seizures in WAG/Rij rats.
Neuropharmacology. 66: 330-338.
Danober L, Deransart C, Depaulis A, Vergnes M, Marescaux C. 1998. Pathophysiological
mechanisms of genetic absence epilepsy in the rat. Prog Neurobiol. 55: 27-57.
Depaulis A, van Luijtelaar ELJM. 2006. Genetic models of absence epilepsy in the rat.
In Models of seizures and epilepsy. Eds. Pitkänen A, Schwartzkroin PA, Moshe
SL. Elsevier Academic Press, Amsterdam, pp. 233-248.
Drinkenburg WH, Coenen AM, Vossen JM, Van Luijtelaar EL. 1991. Spike-wave
discharges and sleep-wake states in rats with absence epilepsy. Epilepsy Res.
9: 218-224.
43
Drinkenburg WH, Coenen AM, Vossen JM, van Luijtelaar EL. 1995. Sleep deprivation
and spike-wave discharges in epileptic rats. Sleep. 18: 252-256.
Drinkenburg WHIM, van Luijtelaar ELJM, and Coenen AML. 1996. Information
processing during aberrant electroencephalographic activity in rats. In Memory
and awareness in anesthesia IIIEds. Bonke B, Bovill JG, Moerman N. ASSEN:
van Gorcum. pp. 186–196.
Drinkenburg WH, Schuurmans ML, Coenen AM, Vossen JM, van Luijtelaar EL. 2003.
Ictal stimulus processing during spike-wave discharges in genetic epileptic rats.
Behav Brain Res. 143: 141-146.
Duncan JS. 1997. Idiopathic generalized epilepsies with typical absences. J Neurol. 244:
403-411.
Durazzo TS, Spencer SS, Duckrow RB, Novotny EJ, Spencer DD, Zaveri HP. 2008.
Temporal distributions of seizure occurrence from various epileptogenic regions.
Neurology. 70: 1265-1271.
Dvornyk V, Vinogradova O. & Nevo E. 2003. Origin and evolution of circadian clock
genes in prokaryotes. Proc. Natl Acad. Sci. USA 100, 2495–2500.
Eastman C, Rechtschaffen A. 1983. Circadian temperature and wake rhythmsof rats
exposed to prolonged continuous illumination. Physiol. Behav. 31: 417-427.
Elliott WJ. 1998. Circadian variation in the timing of stroke onset: a meta-analysis.
Stroke. 29: 992-996.
Enright JT. 1965. The search for rhythmicity in biological time-series. J Theor
Biol.8:426–468.
Fernández JR, Hermida RC, Mojón A. 2009. Chronobiological analysis techniques.
Application to blood pressure. Philos Trans A Math Phys Eng Sci. 367: 431- 445.
Filakovszky J, Gerber K, Bagdy G. 1999. A serotonin-1A receptor agonist and an Nmethyl-D-aspartate receptor antagonist oppose each others effects in a genetic
rat epilepsy model. Neurosci Lett. 261: 89-92.
Fisher RS, van Emde Boas W, Blume W, Elger C, Genton P, Lee P, Engel J Jr. 2005.
Epileptic seizures and epilepsy: definitions proposed by the International
League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE).
Epilepsia. 46: 470-472.
Fraisse P. 1984. Perception and estimation of time. Annu Rev Psychol. 35:1-36.
Fries P, Nikolić D, Singer W. 2007. The gamma cycle. Trends Neurosci. 30: 309-16.
Futatsugi Y, Riviello JJ Jr. 1998. Mechanisms of generalized absence epilepsy. Brain
Dev. 20: 75-79.
44
Gillette MU. 1991. SCN electrophysiology in vitro: rhythmic activity and endogenous
clock property. In: Klein DC, Moore RY, Reppert SM (Eds.), Suprachiasmatic
nucleus: the mind’s clock. Oxford Univ. Press, New York, pp: 125-143.
Glauser T, Ben-Menachem E, Bourgeois B, Cnaan A, Guerreiro C, Kälviäinen R, Mattson
R, French JA, Perucca E, Tomson T; ILAE Subcommission on AED Guidelines.
2013. Updated ILAE evidence review of antiepileptic drug efficacy and
effectiveness as initial monotherapy for epileptic seizures and syndromes.
Epilepsia. 54: 551-63.
Goldman BD. 2001. Mammalian photoperiodic system: formal properties and
neuroendocrine mechanisms of photoperiodic time measurement. J. Biol.
Rhythms 16, 283–301.
Golombek DA, Cardinali DP. 1993. Melatonin accelerates reentrainment after phase
advance of the light-dark cycle in Syrian hamsters: antagonism by flumazenil.
Chronobiol Int. 10:435-41.
Golombek DA, Rosenstein RE. 2010. Physiology of circadian entrainment. Physiol Rev.
90: 1063-1102.
Gowers W. 1885. Course of epilepsy. In: Epilepsy and Other Chronic Convulsive
Diseases: Their Causes, Symptoms and Treatment. William Wood, New York,
pp. 157-164.
Graf M, Jakus R, Kantor S, Levay G, Bagdy G. 2004. Selective 5-HT1A and 5-HT7
antagonists decrease epileptic activity in the WAG/Rij rat model of absence
epilepsy. Neurosci Lett. 359: 45-48.
Halász P. 1991. Sleep, arousal and electroclinical manifestations of generalized epilepsy
with spike wave pattern. Epilepsy Res. Suppl. 2: 43-48.
Halász P, Terzano MG, Parrino L. 2002. Spike-wave discharge and the microstructure
of sleep-wake continuum in idiopathic generalised epilepsy. Neurophysiol Clin.
32: 38-53.
Halberg F, Katinas GS, Chiba Y, Garcia Sainz M, Kovats TG, Künkel H, Montalbetti N,
Reinberg A, Scharf R, Simpson H. 1973. Chronobiologic glossary of the
International Society for the Study of Biologic Rhythms. Int J Chronobiol. 1:31–
63.
Halberg F, Tong YL, Johnson EA. 1967. Circadian system phase, an aspect of temporal
morphology: procedures and illustrative examples. In: von Mayersbach H.,
editor. The Cellular Aspects of Biorhythms. Berlin: Springer; pp. 20–48.
Hannibal J, Fahrenkrug J. 2006. Neuronal input pathways to the brain's biological clock
and their functional significance. Advances in anatomy, embryology and cell
biology. 182: 1-71.
45
Hastings MH, Duffield GE, Ebling FJ, Kidd A, Maywood ES, Schurov I. 1997. Non-photic
signalling in the suprachiasmatic nucleus. Biol Cell. 89: 495-503.
Herman ST, Walczak TS, Bazil CW. 2001. Distribution of partial seizures during the
sleep–wake cycle: differences by seizure onset site. Neurology. 56: 1453-1459.
Herzog ED, Schwartz WJ. 2002. A neuronal clockwork for encoding circadian time. J.
Appl. Physiol. 92: 401—408.
Hinton SC, Meck WH. 1997. The 'internal clocks' of circadian and interval timing.
Endeavour. 21: 82-7.
Hirai K, Kita M, Ohta H, Nishikawa H, Fujiwara Y, Ohkawa S, Miyamoto M. 2005.
Ramelteon (TAK-375) accelerates reentrainment of circadian rhythm after a
phase advance of the light-dark cycle in rats. J Biol Rhythms. 20: 27-37.
Hofman MA, Zhou JN, Swaab DF. 1996. Suprachiasmatic nucleus of the human brain:
an immunocytochemical and morphometric analysis. Anat Rec. 244: 552-62.
Hofstra WA, Grootemarsink BE, Dieker R, van der Palen J, de Weerd AW. 2009.
Temporal distribution of clinical seizures over the 24-h day: a retrospective
observational study in a tertiary epilepsy clinic. Epilepsia. 50: 2019-2026.
Hofstra WA, Spetgens WP, Leijten FS, van Rijen PC, Gosselaar P, van der Palen J, de
Weerd AW. 2009. Diurnal rhythms in seizures detected by intracranial
electrocorticographic monitoring: an observational study. Epilepsy Behav. 14:
617-621.
Honma S, Kanematsu N, Katsuno Y, Honma K. 1996. Persistence of circadian oscillation
while locomotor activity and plasma melatonin levels became aperiodic under
prolonged continuous light in the rat. Neurosci Lett. 216: 49-52.
Honma S, Shirakawa T, Katsuno Y, Namihira M, Honma K. 1998. Circadian periods of
single suprachiasmatic neurons in rats. Neurosci. Lett. 250, 157–160.
Horita H, Uchida E, Maekawa K. 1991. Circadian rhythm of regular spike-wave
discharges in childhood absence epilepsy. Brain Dev. 13: 200-202.
Hut RA, Beersma DG. 2011. Evolution of time-keeping mechanisms: early emergence
and adaptation to photoperiod. Philos Trans R Soc Lond B Biol Sci. 366: 21412154.
Ibuka N, Kawamura H. 1975. Loss of circadian rhythm in sleep-wakefulness cycle in the
rat by suprachiasmatic nucleus lesions. Brain Res. 96: 76-81.
Illnerová H, Trentini GP, Maslova L. 1989. Melatonin accelerates reentrainment of the
circadian rhythm of its own production after an 8-h advance of the light-dark
cycle. J Comp Physiol A. 166: 97-102.
46
Inoue M, Peeters BW, van Luijtelaar EL, Vossen JM, Coenen AM. 1990. Spontaneous
occurrence of spike-wave discharges in five inbred strains of rats. Physiol
Behav. 48: 199-201.
Jakus R, Graf M, Juhasz G, Gerber K, Levay G, Halasz P, Bagdy G. 2003. 5-HT2C
receptors inhibit and 5-HT1A receptors activate the generation of spike-wave
discharges in a genetic rat model of absence epilepsy. Exp Neurol. 184: 964972.
Kamiński RM, Van Rijn CM, Turski WA, Czuczwar SJ, Van Luijtelaar G. 2001. AMPA and
GABA(B) receptor antagonists and their interaction in rats with a genetic form
of absence epilepsy. Eur J Pharmacol. 430: 251-159.
Karafin M, St Louis EK, Zimmerman MB, Sparks JD, Granner MA. 2010. Bimodal
ultradian seizure periodicity in human mesial temporal lobe epilepsy. Seizure.
19: 347-351.
Kellaway P, Frost Jr JD, Crawley JW. 1980. Time modulation of spike-and-wave activity
in generalized epilepsy. Ann. Neurol. 8: 491-500.
Kldiashvili S, Melechov M, Morenkov E, Coenen AML, van Luijtelaar G, Chepurnov SA.
2001. Effects of the sleep-wake rhythm modulating hormone melatonin on
absence seizures in the EEG of rats. In Sleep-wake research in the Netherlands.
Vol 12.Eds. van Bemmel AL, Beersma DGM, Hofman WF, Ruigt GSF, Vos PJE.
Dutch Society for Sleep-Wake Research, Leiden, pp. 58–61.
Knutsson A. 2003. Health disorders of shift workers. Occup Med (Lond). 53: 103-8.
Ko CH, Takahashi JS. 2006. Molecular components of the mammalian circadian clock.
Hum Mol Genet. 2:R271-7.
Kotsopoulos IA, van Merode T, Kessels FG, de Krom MC, Knottnerus JA. 2002.
Systematic review and meta-analysis of incidence studies of epilepsy and
unprovoked seizures. Epilepsia. 43: 1402-1409.
Kowanko IC, Knapp MS, Pownall R, Swannell AJ. 1982. Domiciliary self-measurement
in the rheumatoid arthritis and the demonstration of circadian rhythmicity. Ann
Rheum Dis. 41: 453-455.
Krout KE, Kawano J, Mettenleiter TC, Loewy AD. 2002. CNS inputs to the
suprachiasmatic nucleus of the rat. Neuroscience. 110: 73-92.
Langdon-Down M, Brain W. 1929. Time of day in relation to convulsions in epilepsy.
Lancet: 1029-1032.
Lapin IP, Mirzaev SM, Ryzov IV, Oxenkrug GF. 1998. Anticonvulsant activity of
melatonin against seizures induced by quinolinate, kainate, glutamate, NMDA,
and pentylenetetrazole in mice. J Pineal Res. 24: 215-218.
47
LaVail MM. 1980. Circadian nature of rod outer segment disc shedding in the rat. Invest
Ophthalmol Vis Sci. 19: 407-411.
Lee ML, Swanson BE, de la Iglesia HO. 2009. Circadian timing of REM sleep is coupled
to an oscillator within the dorsomedial suprachiasmatic nucleus. Curr Biol. 19:
848-52.
Lee TM, Zucker I. 1991. Suprachiasmatic nucleus and photic entrainment of circannual
rhythms in ground squirrels. J Biol Rhythms. 6: 315-30.
Liu C, Weaver DR, Jin X, Shearman LP, Pieschl RL, Gribkoff VK, Reppert SM. 1997.
Molecular dissection of two distinct actions of melatonin on the suprachiasmatic
circadian clock. Neuron. 19:91-102.
Loddenkemper T, Lockley SW, Kaleyias J, Kothare SV. 2011. Chronobiology of
epilepsy: diagnostic and therapeutic implications of chrono-epileptology. J Clin
Neurophysiol. 28: 146-153.
Loddenkemper T, Vendrame M, Zarowski M, Gregas M, Alexopoulos AV, Wyllie E,
Kothare SV. 2011. Circadian patterns of pediatric seizures. Neurology. 76: 145153.
Loiseau J, Loiseau P, Guyot M, Duche B, Dartigues JF, Aublet B. 1990. Survey of seizure
disorders in the French southwest. I. Incidence of epileptic syndromes.
Epilepsia. 31: 391-396.
Löscher W. 2002. Animal models of epilepsy for the development of antiepileptogenic
and disease-modifying drugs. A comparison of the pharmacology of kindling and
post-status epilepticus models of temporal lobe epilepsy. Epilepsy Res. 50: 105123.
Löscher W. 2011. Critical review of current animal models of seizures and epilepsy used
in the discovery and development of new antiepileptic drugs. Seizure. 20: 359368.
Lüttjohann A, van Luijtelaar G. 2012. The dynamics of cortico-thalamo-cortical
interactions at the transition from pre-ictal to ictal LFPs in absence epilepsy.
Neurobiol Dis. 47: 49-60.
Martins da Silva A, Aarts JH, Binnie CD, Laxminarayan R, Lopes da Silva FH, Meijer
JW, Nagelkerke N. 1984. The circadian distribution of interictal epileptiform EEG
activity. Electroencephalogr Clin Neurophysiol. 58: 1-13.
Mason R, Brooks A. 1988. The electrophysiological effects of melatonin and a putative
antagonist (N-acetyltryptamine) on rat suprachiasmatic neurones in vitro.
Neurosci. Lett, 95: 296–301.
Matell MS, Meck WH. 2000. Neuropsychological mechanisms of interval timing behavior.
Bioessays. 22: 94-103.
48
Meeren HK, Pijn JP, Van Luijtelaar EL, Coenen AM, Lopes da Silva FH. 2002. Cortical
focus drives widespread corticothalamic networks during spontaneous absence
seizures in rats. J Neurosci. 22: 1480-1495.
Meeren H, van Luijtelaar G, Lopes da Silva F, Coenen A. 2005. Evolving concepts on
the pathophysiology of absence seizures: the cortical focus theory. Arch Neurol.
62: 371-376.
Megdal SP, Kroenke CH, Laden F, Pukkala E, Schernhammer ES. 2005. Night work and
breast cancer risk: A systematic review and meta-analysis. European Journal of
Cancer 41: 2023–2032.
Meijer JH, Schwartz WJ. 2003. In search of the pathways for light-induced pacemaker
resetting in the suprachiasmatic nucleus. J Biol Rhythms. 18: 235-249.
Midzianovskaia IS, Kuznetsova GD, Coenen AM, Spiridonov AM, van Luijtelaar EL.
2001. Electrophysiological and pharmacological characteristics of two types of
spike-wave discharges in WAG/Rij rats. Brain Res. 911: 62-70.
Mikkelsen JD, Vrang N. 1994. A direct pretectosuprachiasmatic projection in the rat.
Neuroscience. 62: 497-505.
Minecan D, Natarajan A, Marzec M, Malow B. 2002. Relationship of epileptic seizures to
sleep stage and sleep depth. Sleep. 25: 899-904.
Moore JG, Halberg F. 1987. Circadian rhythm of gastric acid secretion in active duodenal
ulcer: chronobiological statistical characteristics and comparison of acid
secretory and plasma gastrin patterns with healthy subjects and postvagotomy
and pyloroplasty patients. Chronobiol Int. 4: 101-110.
Moore RY, Card JP. 1994. Intergeniculate leaflet: an anatomically and functionally
distinct subdivision of the lateral geniculate complex. J Comp Neurol. 344: 40330.
Moore RY, Lenn NJ. 1972. A retinohypothalamic projection in the rat. J Comp Neurol.
146: 1-14.
Morin LP, Allen CN. 2006. The circadian visual system, 2005. Brain Res Rev. 51: 1-60.
Morin LP. 2013. Neuroanatomy of the extended circadian rhythm system. Exp Neurol.
243: 4-20.
Morris C. 1992. Editor. Academic Press Dictionary of Science and Technology. San
Diego: Academic Press.
Musshoff U, Speckmann EJ. 2003. Diurnal actions of melatonin on epileptic activity in
hippocampal slices of rats. Life Sci. 73: 2603-2610.
49
Nagano M, Adachi A, Nakahama K, Nakamura T, Tamada M, Meyer-Bernstein E, Sehgal
A, Shigeyoshi Y. 2003. An abrupt shift in the day/night cycle causes
desynchrony in the mammalian circadian center. J Neurosci. 23: 6141-51.
Naranjo-Rodríguez EB, Prieto-Gómez B, Reyes-Vázquez C. 1991. Melatonin modifies
the spontaneous multiunit activity recorded in several brain nuclei of freely
behaving rats. Brain Res Bull. 27:595-600.
Ngomba RT, Santolini I, Biagioni F, Molinaro G, Simonyi A, van Rijn CM, D'Amore V,
Mastroiacovo F, Olivieri G, Gradini R, Ferraguti F, Battaglia G, Bruno V, Puliti A,
van Luijtelaar G, Nicoletti F. 2011. Protective role for type-1 metabotropic
glutamate receptors against spike and wave discharges in the WAG/Rij rat
model of absence epilepsy. Neuropharmacology. 60: 1281-1291.
Noebels J. 2006. Spontaneous epileptic mutation in the mouse. In Pitkanen A,
Schwartzkroin PS, Moshe S (Eds). Models of seizures and epilepsy, Elsevier
Academic Press, 223-232.
Olsson I. 1988. Epidemiology of absence epilepsy. I. Concept and incidence. Acta
Paediatr Scand. 77: 860-866.
Ouyang Y, Andersson CR, Kondo T, Golden SS, Johnson CH. 1998. Resonating circadian
clocks enhance fitness in cyanobacteria. Proc. Natl Acad. Sci. USA 95, 8660–
8664.
Panayiotopoulos CP. 2001. Treatment of typical absence seizures and related epileptic
syndromes. Paediatr Drugs. 3: 379-403.
Pastore RE, Farrington SM. 1996. Measuring the difference limen for identification of
order of onset for complex auditory stimuli. Percept Psychophys. 58: 510-526.
Pavlova MK, Shea SA, Bromfield EB. 2004. Day/night patterns of focal seizures.
Epilepsy Behav. 5: 44-49.
Pavlova MK, Shea SA, Scheer FA, Bromfield EB. 2009. Is there a circadian variation of
epileptiform abnormalities in idiopathic generalized epilepsy? Epilepsy Behav.
16: 461-467.
Pavlova MK, Woo Lee J, Yilmaz F, Dworetzky BA. 2012. Diurnal pattern of seizures
outside the hospital: is there a time of circadian vulnerability? Neurology. 78:
1488-1492.
Pearce PS, Friedman D, Lafrancois JJ, Iyengar SS, Fenton AA, Maclusky NJ, Scharfman
HE. 2014. Spike-wave discharges in adult Sprague-Dawley rats and their
implications for animal models of temporal lobe epilepsy. Epilepsy Behav. 32:
121-131.
Peeters BWMM, Spooren WPJM, van Luijtelaar ELJM, Coenen AML. 1988. The WAG/Rij
model for absence epilepsy: anticonvulsant drug evaluation. Neurosci. Res.
Commun. 2: 93-97.
50
Peeters BW, van Rijn CM, Vossen JM, Coenen AM. 1989. Effects of GABA-ergic agents
on spontaneous non-convulsive epilepsy, EEG and behaviour, in the WAG/RIJ
inbred strain of rats. Life Sci. 45: 1171-1176.
Peeters BW, van Rijn CM, Vossen JM, Coenen AM. 1990. Involvement of NMDA
receptors in non-convulsive epilepsy in WAG/Rij rats. Life Sci. 47: 523-529.
Peeters BW, Ramakers GM, Ellenbroek BA, Vossen JM, Coenen AM. 1994. Interactions
between NMDA and nonNMDA receptors in nonconvulsive epilepsy in the
WAG/Rij inbred strain. Brain Res Bull. 33: 715-718.
Perlow MJ, Reppert SM, Boyar RM, Klein DC. 1981. Daily rhythms in cortisol and
melatonin in primate cerebrospinal fluid. Effects of constant light and dark.
Neuroendocrinology. 32: 193 – 196.
Perreau-Lenz S, Kalsbeek A, Garidou ML, Wortel J, van der Vliet J, van Heijningen C,
Simonneaux V, Pévet P, Buijs RM. 2003. Suprachiasmatic control of melatonin
synthesis in rats: inhibitory and stimulatory mechanisms. Eur J Neurosci. 17:
221-228.
Perreau-Lenz S, Pévet P, Buijs RM, Kalsbeek A. 2004. The biological clock: the
bodyguard of temporal homeostasis. Chronobiol Int. 21: 1-25.
Petrovsky N, McNair P, Harrison LC. 1998. Diurnal rhythms of pro-inflammatory
cytokines: regulation by plasma cortisol and therapeutic implications. Cytokine.
10: 307-312.
Pfeffer M, Rauch A, Korf HW, von Gall C. 2012. The endogenous melatonin (MT) signal
facilitates reentrainment of the circadian system to light-induced phase
advances by acting upon MT2 receptors. Chronobiol Int. 29:415-429.
Pitrosky B, Kirsch R, Malan A, Mocaer E, Pévet P. 1999. Organization of rat circadian
rhythms during daily infusion of melatonin or S 20098, a melatonin agonist. Am
J Physiol. 277:R812-R828.
Pittendrigh CS. 1960. Circadian rhythms and the circadian organization of living
systems. Cold Spring Harb Symp Quant Biol. 25: 159-184.
Pittendrigh CS. 1981. Circadian systems: entrainment. In Handbook of Behavioral
Neurobiology Vol 4, Biological Rhythms, Eds. Aschoff J, 95–124. Plenum Press,
New York.
Pittendrigh CS. 1993. Temporal organisation: reflections of a Darwinian clock-watcher.
Annu. Rev. Physiol. 55, 17–54.
Polack PO, Guillemain I, Hu E, Deransart C, Depaulis A, Charpier S. 2007. Deep layer
somatosensory cortical neurons initiate spike-and-wave discharges in a genetic
model of absence seizures. J Neurosci. 27: 6590-6599.
51
Pöppel E, Giedke H. 1970. Diurnal variation of time perception. Psychologische
Forschung. 34: 182-198.
Portaluppi F, Tiseo R, Smolensky MH, Hermida RC, Ayala DE, Fabbian F. 2012.
Circadian rhythms and cardiovascular health. Sleep Med Rev. 16: 151-166.
Prendergast BJ, Nelson RJ, and Zucker I. 2002. Mammalian seasonal rhythms:
Behavior and neuroendocrine substrates. In Hormones, Brain and
Behavior, Eds. Pfaff DW. 2, 93-156. Elsevier Science.
Qian X, Droste SK, Lightman SL, Reul JM, Linthorst AC. 2012. Circadian and ultradian
rhythms of free glucocorticoid hormone are highly synchronized between the
blood, the subcutaneous tissue, and the brain. Endocrinology. 153: 4346-4353.
Quigg M, Clayburn H, Straume M, Menaker M, Bertram III EH. 2000. Effects of circadian
regulation and rest-activity state on spontaneous seizures in rat model of limbic
epilepsy. Epilepsia 41: 502-509.
Quigg M, Straume M, Menaker M, Bertram III EH. 1998. Temporal distribution of partial
seizures: comparison of an animal model with human partial epilepsy. Ann.
Neurol. 43: 748-755.
Quigg M, Straume M. 2000. Dual epileptic foci in a single patient express distinct
temporal patterns dependent on limbic versus nonlimbic brain location. Ann
Neurol. 48: 117-120.
Ramakers GM, Peeters BW, Vossen JM, Coenen AM. 1991. CNQX, a new non-NMDA
receptor antagonist, reduces spike wave discharges in the WAG/Rij rat model
of absence epilepsy. Epilepsy Res. 9: 127-131.
Ramgopal S, Vendrame M, Shah A, Gregas M, Zarowski M, Rotenberg A, Alexopoulos
AV, Wyllie E, Kothare SV, Loddenkemper T. 2012. Circadian patterns of
generalized tonic-clonic evolutions in pediatric epilepsy patients. Seizure. 21:
535-539.
Redman J, Armstrong S, Ng KT. 1983. Free-running activity rhythms in the rat:
entrainment by melatonin. Science. 219:1089-1091.
Refinetti R, Lissen GC, and Halberg F. 2007. Procedures for numerical analysis of
circadian rhythms. Biol Rhythm Res. 38: 275 – 325.
Reinberg A, Gervais P, Levi F, Smolensky M, Del Cerro L, Ugolini C. 1988. Circadian and
circannual rhythms of allergic rhinitis: an epidemiologic study involving
chronobiologic methods. J Allergy Clin Immunol. 81: 51-62.
Rensing L, Meyer-Grahle U, Ruoff P. 2001. Biological timing and the clock metaphor:
oscillatory and hourglass mechanisms. Chronobiol Int. 18: 329-69.
52
Reppert SM, Weaver DR, Ebisawa T. 1994. Cloning and characterization of a
mammalian melatonin receptor that mediates reproductive and circadian
responses. Neuron. 13: 1177-1185.
Reppert SM, Weaver DR. 2002. Coordination of circadian timing in mammals. Nature.
418: 935-941.
Rigas B, Torosis J, McDougall CJ, Vener KJ, Spiro HM. 1990. The circadian rhythm of
biliary colic. J Clin Gastroenterol. 12: 409-414.
Roenneberg T, Merrow M. 2002. Light reception: discovering the clock-eye in
mammals. Curr Biol. 12: R163-165.
Schernhammer ES, Laden F, Speizer FE, Willett WC, Hunter DJ, Kawachi I, Colditz GA.
2001. Rotating Night Shifts and Risk of Breast Cancer in Women Participating
in the Nurses' Health Study. J Natl Cancer Inst. 98: 1563-1568.
Schernhammer ES, Laden F, Speizer FE, Willett WC, Hunter DJ, Kawachi I, Fuchs CS,
Colditz GA. 2003. Night-shift work and risk of colorectal cancer in the nurses'
health study. J Natl Cancer Inst. 95: 825-828.
Sharkey KM, Eastman CI. 2002. Melatonin phase shifts human circadian rhythms in a
placebo-controlled simulated night-work study. Am J Physiol Regul Integr Comp
Physiol. 282: R454-463.
Sarkisian MR. 2001. Overview of the Current Animal Models for Human Seizure and
Epileptic Disorders. Epilepsy Behav. 2: 201-216.
Sarkisova KY, Midzianovskaia IS, Kulikov MA. 2003. Depressive-like behavioral
alterations and c-fos expression in the dopaminergic brain regions in WAG/Rij
rats with genetic absence epilepsy. Behav Brain Res. 144: 211-226.
Sarkisova KY, Kulikov MA, Midzyanovskaya IS, Folomkina AA. 2008. Dopaminedependent nature of depression-like behavior in WAG/Rij rats with genetic
absence epilepsy. Neurosci Behav Physiol. 38: 119-128.
Sarkisova KY, Kuznetsova GD, Kulikov MA, van Luijtelaar G. 2010. Spike-wave
discharges are necessary for the expression of behavioral depression-like
symptoms. Epilepsia. 51: 146-160.
Sarkisova K, van Luijtelaar G. 2011. The WAG/Rij strain: a genetic animal model of
absence epilepsy with comorbidity of depression [corrected]. Prog
Neuropsychopharmacol Biol Psychiatry. 35: 854-876.
Schridde U, van Luijtelaar G. 2004. Corticosterone increases spike-wave discharges in
a dose- and time-dependent manner in WAG/Rij rats. Pharmacol Biochem
Behav. 78: 369-375.
53
Sharma VK, Singaravel M, Subbaraj R, Chandrashekaran MK. 1999. Timely
administration of melatonin accelerates reentrainment to phase-shifted lightdark cycles in the field mouse Mus booduga. Chronobiol Int. 16:163-70.
Shechter A, Boivin DB. 2010. Sleep, Hormones, and Circadian Rhythms throughout the
Menstrual Cycle in Healthy Women and Women with Premenstrual Dysphoric
Disorder. Int Journal of Endocrinology. 2010: 259345.
Shorvon SD. 2011. The etiologic classification of epilepsy. Epilepsia. 52: 1052-1057.
Sinha S, Brady M, Scott CA, Walker MC. 2006. Do seizures in patients with refractory
epilepsy vary between wakefulness and sleep? J Neurol Neurosurg Psychiatry.
77: 1076-1078.
Sitnikova E, van Luijtelaar G. 2005. Reduction of adrenergic neurotransmission with
clonidine aggravates spike-wave seizures and alters activity in the cortex and
the thalamus in WAG/Rij rats. Brain Res Bull. 64: 533-540.
Sitnikova E, van Luijtelaar G. 2007. Electroencephalographic characterization of spikewave discharges in cortex and thalamus in WAG/Rij rats. Epilepsia. 48: 22963311.
Slotten HA, Pitrosky B, Pévet P. 1999. Influence of the mode of daily melatonin
administration on entrainment of rat circadian rhythms. J Biol Rhythms.
14:347-353.
Smolensky MH, Peppas NA. 2007. Chronobiology,
chronotherapeutics. Adv Drug Deliv Rev. 59: 828-51.
drug
delivery,
and
Smolensky MH, Reinberg A, Labrecque G. 1995. Twenty-four hour pattern in symptom
intensity of viral and allergic rhinitis: treatment implications. J Allergy Clin
Immunol. 95: 1084-1096.
Snead OC 3rd. 1992. Pharmacological models of generalized absence seizures in
rodents. J Neural Transm Suppl. 35:7-19.
Snead OC 3rd. 1995. Basic mechanisms of generalized absence seizures. Ann Neurol.
37: 146-157.
Snyder F, Hobson JA, Morrison DF, Goldfrank F. 1964. Changes in respiration, heart
rate, and systolic blood pressure in human sleep. J Appl Physiol. 19: 417-22.
Solomon GD. 1992. Circadian rhythms and migraine. Cleve Clin J Med. 59: 326-329.
Spadoni G, Bedini A, Rivara S, Mor M. 2011. Melatonin receptor agonists: new options
for insomnia and depression treatment. CNS Neurosci Ther. 17: 733-741.
Srinivasan V, Singh J, Pandi-Perumal SR, Brown GM, Spence DW, Cardinali DP. 2010.
Jet lag, circadian rhythm sleep disturbances, and depression: the role of
melatonin and its analogs. Adv Ther. 27: 796-813.
54
Srinivasan V, Spence DW, Pandi-Perumal SR, Trakht I, Cardinali DP. 2008. Jet lag:
therapeutic use of melatonin and possible application of melatonin analogs.
Travel Med Infect Dis. 6: 17-28.
Stankov B, Fraschini F, Reiter RJ. 1991. Melatonin binding sites in the central nervous
system.Brain Res Brain Res Rev. 16: 245-256.
Tapia-Osorio A, Salgado-Delgado R, Angeles-Castellanos M, Escobar C. 2013.
Disruption of circadian rhythms due to chronic constant light leads to depressive
and anxiety-like behaviors in the rat. Behav Brain Res. 252: 1-9.
Tolmacheva EA, Oitzl MS, van Luijtelaar G. 2012. Stress, glucocorticoids and absences
in a genetic epilepsy model. Horm Behav. 61: 706-710.
Vanecek J.1998. Cellular mechanisms of melatonin action. Physiol Rev. 78: 687-721.
van Luijtelaar EL, Coenen AM. 1986. Two types of electrocortical paroxysms in an
inbred strain of rats. Neurosci Lett. 70: 393-397.
van Luijtelaar EL, Coenen AM. 1988. Circadian rhythmicity in absence epilepsy in rats.
Epilepsy Res. 2: 331-336.
van Luijtelaar G, Budziszewska B, Jaworska-Feil L, Ellis J, Coenen A, Lasoń W. 2001.
The ovarian hormones and absence epilepsy: a long-term EEG study and
pharmacological effects in a genetic absence epilepsy model. Epilepsy Res. 46:
225-239.
van
Luijtelaar G, Mishra AM, Edelbroek P, Coman D, Frankenmolen N,
Schaapsmeerders P, Covolato G, Danielson N, Niermann H, Janeczko K,
Kiemeneij A, Burinov J, Bashyal C, Coquillette M, Lüttjohann A, Hyder F,
Blumenfeld H, van Rijn CM. 2013. Anti-epileptogenesis: Electrophysiology,
diffusion tensor imaging and behavior in a genetic absence model. Neurobiol
Dis. 60: 126-138.
van Luijtelaar G, Behr C, Avoli M. 2014. Is there such a thing as "generalized" epilepsy?
Adv Exp Med Biol. 813: 81-91.
van Luijtelaar G, Onat FY, Gallagher MJ . 2014. Animal models of absence epilepsies:
what do they model and do sex and sex hormones matter? Neurobiol Dis. 72:
167-179.
von Steinbüchel N. 1995. Temporal system states in speech processing. In:
Supercomputing in brain research: from tomography to neural networks. Eds.
Herrmann HJ, Wolf DE, Pöppel E. World Scientific, Singapore, 75-81.
Waterhouse J, Drust B, Weinert D, Edwards B, Gregson W, Atkinson G, Kao S, Aizawa
S, Reilly T. 2005. The circadian rhythm of core temperature: origin and some
implications for exercise performance. Chronobiol Int. 22: 207-25.
55
Weaver DR, Rivkees SA, Reppert SM. 1989. Localization and characterization of
melatonin receptors in rodent brain by in vitro autoradiography. J Neurosci. 9:
2581-2590.
Wehr TA. 1982. Circadian rhythm disturbances in depression and mania, in: Rhythmic
Aspects of Behavior, Eds. Brown FM, Graeber RC. Lawrence Erlbaum Ass., New
Jersey, 399–428.
Welsh DK, Logothetis DE, Meister M, Reppert SM. 1995. Individual neurons dissociated
from rat suprachiasmatic nucleus express independently phased circadian firing
rhythms. Neuron 14, 697–706.
Westmijse I, Ossenblok P, Gunning B, van Luijtelaar G. 2009. Onset and propagation
of spike and slow wave discharges in human absence epilepsy: A MEG study.
Epilepsia. 50: 2538-2548.
Willoughby JO, Mackenzie L. 1992. Nonconvulsive electrocorticographic paroxysms
(absence epilepsy) in rat strains. Lab Anim Sci. 42: 551-554.
Wittmann M. 1991. Time perception and temporal processing levels of the brain.
Chronobiol Int. 16: 17-32.
Woelfle MA, Ouyang Y, Phanvijhitsiri K, Johnson CH. 2004. The adaptive value of
circadian clocks: an experimental assessment in cyanobacteria. Curr. Biol. 14,
1481–1486.
Woodfill CJ, Wayne NL, Moenter SM, Karsch FJ. 1994. Photoperiodic synchronization of
a circannual reproductive rhythm in sheep: identification of season-specific time
cues. Biol Reprod. 50: 965-976.
Yamazaki S, Numano R, Abe M, Hida A, Takahashi R, Ueda M, Block GD, Sakaki Y,
Menaker M, Tei H. 2000. Resetting central and peripheral circadian oscillators
in transgenic rats. Science. 288: 682-685.
Yildirim M, Marangoz C. 2006. Anticonvulsant effects of melatonin on penicillin-induced
epileptiform activity in rats. Brain Res. 1099: 183-188.
Zarowski M, Loddenkemper T, Vendrame M, et al. 2010. Relationship of day/night and
sleep/wake patterns to seizure semiology and epilepsy localization. Neurology.
74 (suppl 2): A105 (abstract).
Zordan MA, Rosato E, Piccin A, Foster R. 2001. Photic entrainment of the circadian
clock: from Drosophila to mammals. Semin Cell Dev Biol. 12: 317-28.
56
CHAPTER 2
Differential temporal organization of sleep-wake
states and absence seizures in light versus dark
phase in a genetic animal model
Submitted to Epilepsy Research
Abstract
The goal of the present study was to investigate how spikewave discharges and different sleep-wake states are temporally
related.
Continuous
electroencephalographic
(EEG)
and
electromyographic (EMG) recordings across 24 h were made in adult
male WAG/Rij rats kept in a 12:12 light-dark cycle (LD 12:12). Three
different resolutions of the data: 1 h, 6 min and 0.5 min were used.
Total time of a particular state of vigilance and total time occupied by
spike-wave discharges was correlated and cross-correlated over 24 h
and for the light and dark phase separately. Strong positive correlation
between spike-wave discharges and active wakefulness, and strong
negative ones with light and deep slow-wave sleep for 1 h bin size were
found. Correlations were lower with the increase in resolution. Large
phase-related differences in the temporal organization of spike-wave
discharges and sleep-wake states were seen: unidirectional in the light
and bidirectional in the dark phase. It was thought that this is most
likely due to the entraining power of the light onset.
Keywords:
vigilance/sleep-wake
absence
state,
epilepsy,
light
and
WAG/Rij
dark
rats,
phase,
state
of
spike-wave
discharges, circadian rhythm
1. Introduction
Childhood Absence Epilepsy (CAE) is an epileptic syndrome of
generalized, nonconvulsive seizures, which are manifested as short
moments of decreased responsiveness coinciding with 2 – 4 Hz SpikeWave Discharges (SWDs) in the EEG (Duncan, 1997; Engel Jr, 2006;
Niedermeyer, 1972). In WAG/Rij rats, an often used genetic absence
epilepsy rodent model, 7 – 11 Hz SWDs are considered as the EEG
59
equivalents of absence seizures (Depaulis and van Luijtelaar, 2006;
Sitnikova and van Luijtelaar, 2007). During seizures animals are
immobile and unresponsive; pharmacological therapeutics against
absence epilepsy decrease the number and duration of SWDs (Depaulis
and van Luijtelaar, 2006).
Absence seizures in patients occur typically during drowsiness,
non-rapid-eye movement sleep (non-REM sleep, stages I and II) and
also around transitions between sleep-wake states (Halasz et al.,
2002). Similarly, in WAG/Rij rats, SWDs are preceded mostly by
passive wakefulness and light slow-wave sleep. More specifically,
analysis of short lasting (5 s) EEG epochs directly preceding and
following SWDs (this is called the momentary approach) has confirmed
that animals have seizures mostly during unstable, transitional periods
across the sleep-wake cycle. A highly desynchronized EEG typical for
active wakefulness or rapid-eye movement sleep (REM), as well as
highly synchronized one, typical for deep slow-wave sleep, are not a
favorable temporal context for the occurrence of SWDs (Drinkenburg
et al., 1991; Lüttjohann et al., 2012). This implies a large negative
relation between the occurrence of active wakefulness and deep slowwave sleep on the one hand and SWDs on the other.
A 24 h rhythm in the occurrence of SWDs has been detected
under entrainment to the LD 12:12: absences reach a maximum during
the dark phase, while they are almost absent during the first few hours
of the light phase (Smyk et al., 2011; Smyk et al., 2012; van Luijtelaar
and Coenen, 1988). A 24 h periodicity is also observed for the timing
of the different states of vigilance, which taken together, constitute the
sleep-wake cycle (Borbély et al., 1975; Daan et al., 1984). The
circadian rhythm and momentary factors controlling the occurrence of
SWDs interact: this is illustrated during the timing of the minimum of
the rhythm. The beginning of the light phase, when SWDs rarely occur,
is the period with the highest amount of deep slow-wave sleep
60
(Borbély, 1975; Drinkenburg et al., 1991; Friedman et al., 1979; van
Luijtelaar and Coenen, 1984). However, active wakefulness, which did
not precede SWDs when momentary factors were taking into
consideration, predominates and peaks during the dark period. This
demonstrates that active wakefulness and SWDs coincide on the 24 h
scale but dissociate when momentary factors are taking into account.
The goal of the present study is to (re)investigate the temporal
relationship between SWDs and the different sleep-wake states across
the 24 h cycle by means of correlation and cross correlation functions.
These methods were applied on 24 h EEG data at different resolutions
(1 h, 6 min and 30 s) which together could capture both: a description
and temporal organization on a resolution commonly used in circadian
research and on a much shorter time scale, suited for considering
momentary factors determining the occurrence of SWD. This was done
also separately for the light and dark phase.
2. Materials and methods
2.1. Animals
Twelve adult, male WAG/Rij rats (Harlan, the Netherlands)
weighting 315 g ± 12g (eight months of age) were used. Animals were
singly-housed in individually ventilated Plexiglas cages (25 x 22 x 18
cm) in a sound-attenuated room and controlled environmental
conditions (temperature: 22 ± 2 oC, humidity: 60%, 12:12 light-dark
cycle, white lights on at 05:00 am off at 17:00 pm, light intensity: ≈
100 lux) throughout the experiment as well as during the recovery
period after the surgery. Standard laboratory chow and tap water were
available ad libitum. All experimental procedures were approved by the
animal use and care committee of Janssen Research and Development,
61
Pharmaceutical Companies of Johnson & Johnson and the local ethical
committee.
2.2. Surgery
Under deep isoflurane anesthesia seven stainless steel fixing
screws (diameter 1 mm) were inserted bilaterally in the left and right
hemisphere
along
the
antero-posterior
axes
in
the
following
coordinates: AP +2 mm, L +/– 2 mm; AP -2 mm, L +/-2 mm and AP 6.6 mm, L +/- 2 mm from Bregma; and referenced to the same ground
electrode place midline above the cerebellum. The incisor bar was –5
mm under the center of the ear bar (Paxinos and Watson, 1998). In
addition, stainless steel wire EMG electrodes were placed in the
muscles of the neck. Electrodes (stainless steel wire, 7N51465T5TLT,
51/46 Teflon Bilaney, Germany) were connected to a pin (Future
Electronics: 0672-2-15-15-30-27-10-0) with a small insert (track pins;
Dataflex: TRP-1558-0000) and were fit into a 10-hole connector, then
the whole assembly was fixed with dental cement to the cranium. Rats
were allowed to recover for 10 days after surgery.
2.3. EEG and EMG recordings
Rats, while staying in their home cages, were placed in the
sound-attenuated, recording boxes and connected by a cable to
rotating swivels, allowing free movement during recording procedures.
Rats were allowed to habituate to the recording set-up for 24 h
preceding the experiment. Continuous EEG and EMG signals were
acquired at a 2 kHz sample rate with an input range of +/- 500 mV
through a Biosemi ActiveTwo system (Biosemi, Amsterdam, the
Netherlands) referenced to the CMS-DRL ground (common mode
reference for online data acquisition and impedance measures, which
62
is a feedback loop driving the average potential across the montage
close to the amplifier zero). The signals were amplified, band-pass
filtered between 1 and 100 Hz, and digitized with 24 bit resolution.
2.4. Experimental design
Data used in the present study were from a larger study in which
the putative action of agomelatine, a newly introduced antidepressant
agent with chronobiological properties, on SWDs and sleep-wake
states, was investigated (Chapter 5). Twenty four hours EEG and EMG
recordings were performed during 3 consecutive days. On the first day,
animals received a peri oral (p.o.) injection of saline 1 hour before the
onset of darkness. Only data from the first day (24 h of EEG and EMG
recordings in LD 12:12 with p.o. saline injection 1 h before the onset
of darkness) were analyzed here.
2.5. Data analysis
5 vigilance states: active wakefulness, passive wakefulness,
light and deep slow-wave sleep, REM sleep, as well as a number of
transitions between them, were identified automatically for 2 s epochs
based on EEG, EMG and body movements (passive infrared, PIR,
detection) following validated criteria (Ahnaou and Drinkenburg,
2011).
The occurrence and duration of SWDs across 24 h was scored
automatically based on the EEG signal from a frontal electrode
according to well-established criteria, next they were verified visually
(Ovchinnikov et al., 2010; van Luijtelaar and Coenen, 1986).
Data from 24 h: time spent in a particular state of vigilance
(expressed in s) and total duration of SWDs (also expressed in s) were
subsequently analyzed with 3 different bin sizes: 1 h, as is commonly
63
done in chronobiology, 6 min and 30 s, allowing a sufficient resolution
considering the duration of the sleep cycle, non-REM and REM duration
and the clustering of SWDs in a minute range in WAG/Rij rats
(Midzianovskaya et al., 2006; van Luijtelaar and Bikbaev, 2007).
Pearson’s correlations and cross-correlations were calculated to
estimate relationships between total duration of SWDs on the one side,
and time spent in a particular sleep-wake state and transitions between
different states of vigilance on the other across the 24 h period for each
individual subject. Cross-correlations for 1 h resolution (bin size) of the
data were calculated for 24 time lags, while for 6 min and 0.5 min
resolutions of 60 time lags were analyzed. These calculations were
made for the 24 h data, as well as for the 12 h light and 12 h dark
phase, respectively. Autocorrelation and spectral density (single
spectrum Fourier analysis) of individual data of total duration of SWDs
and sleep-wake states in the light and dark period separately
(resolution of the data: 6 min) were used to analyze the temporal
organization
and
rhythmicity
of
SWDs
and
sleep-wake
states
separately.
2.6. Statistical analysis
The statistical analyses were done with Statistica (StatSoft Inc.,
Tulsa, Oklahoma, USA). Pearson’s correlation coefficients were used to
correlate across time (hours) the relationships between the various
vigilance states and SWDs (R, p < 0.05) and the mean and SEM of the
correlation coefficients across 12 animals were calculated, and
confidence limits (> than 2 times their respective standard errors) were
used, the differences between light and dark were tested with Student
t-test, p < 0.05.
64
3. Results
3.1. Pearson’s correlations for 24 hours
The distribution of the sleep-wake states and SWDs over the 24
h period in the 1 h resolution bin size is presented in Figure 1 A. The
rhythms of active wakefulness and SWDs were positively correlated
over the 24 h period (R = 0.75 ± 0.04, p < 0.05), and those of deep
and light slow wave sleep and SWDs were negatively correlated over
time (R = -0.70 ± 0.05, p < 0.05 and R = -0.55 ± 0.05, p < 0.05,
respectively: Fig. 1 C). Pearson’s correlation coefficients became small
and insignificant for the 6 and 0.5 min bins. The total time of SWDs
was positively correlated with transitions from active to passive
wakefulness and from passive to active wakefulness (R = 0.59 ± 0.10,
p < 0.05 and R = 0.59 ± 0.10, p < 0.05, respectively), and negatively
correlated with transitions from light to deep slow-wave sleep and from
deep to light slow-wave sleep (R = -0.63 ± 0.07, p < 0.05 and R = 0.62 ± 0.07, p < 0.05, respectively; a 24 h plot of the number of
transitions and SWDs is presented in Figure 1 B). The decrease of the
bin size from 1h to 6 and 0.5 min was accompanied with a decrease of
the values of Pearson’s correlation coefficients, which became
insignificant.
65
Figure 1. A) Mean of total time of sleep-wake states and total time of SWDs
across 24 h in a resolution of 1 h (upper graph) and 6 min (lower graph) (n =
12). AW – orange line – active wakefulness, PW – red line - passive
wakefulness, SWS L – light green line - light slow-wave sleep, SWS D – dark
green line - deep slow-wave sleep, REM – blue line - REM sleep. The dark phase
of the LD 12:12 is marked by shaded background. B) Mean of the number of
transitions between sleep-wake states across 24 h in a resolution of 1 h. AW –
PW – transitions from active to passive wakefulness, PW – AW – transitions
from passive to active wakefulness, SWS L – SWS D – transitions from light to
deep slow-wave sleep, SWS D – SWS L – transitions from deep to light slowwave sleep. The dark phase of the LD 12:12 is marked by grey rectangle. C)
Pearson’s correlation coefficients (mean ± SEM) obtained for the total time of
sleep-wake states and the total time of SWDs for 1 h, 6 and 0.5 min resolution
of the data. * p < 0.05 according to the critical value table for Pearson’s
correlation coefficients
66
3.2. Cross-correlations for 24 hours
Significant cross-correlations were found between SWDs on the
one side and active wakefulness, light and deep slow-wave sleep on
the other for the 1 h resolution data (Figure 2 A). The occurrence of
the maximum at lag 0 for active wakefulness demonstrates the cooccurrence of active wakefulness with SWDs. The negative value of the
maximum at lag 0 for deep slow-wave sleep suggests that SWDs and
deep slow-wave sleep do not co-occur. The highest cross-correlation
coefficient for light slow-wave sleep, seen at lag -2 (-4 to -1), suggests
that the duration of SWDs may predict the duration of light slow-wave
sleep in the consecutive 1 to 4 hours. SWDs and sleep-wake states
were best cross-correlated at positive time lag 1 for the 6 min
resolution of the data; only for REM sleep none of the cross-correlation
coefficients exceeded the confidence limits (Figure 2 B). Apart from
time lag 1, small significant cross-correlations were also observed at
other positive, as well as negative time lags. This suggests a
bidirectional relation between SWDs and these vigilance states. Not
only may the duration of particular sleep-wake states predict a higher
probability of an increase of the time occupied by SWDs, but also the
time occupied by SWDs influences the amount of vigilance states in the
consecutive minutes. Data are presented in Figure 2 B.
Significant cross-correlation coefficients between SWDs and
sleep-wake states expressed in 0.5 min resolution were seen in all
states predominantly with positive time lags (Figure 2 C). REM sleep
was an exception: it was significantly cross-correlated with SWDs
almost only at negative time lags. Some positive cross-correlation
coefficients were observed for active wakefulness and passive
wakefulness and lower cross-correlations and less time lags for passive
wakefulness, negative for light and deep slow-wave sleep and REM
sleep. Two features of cross-correlation coefficients in the 0.5 min
67
resolution of the data are striking. First, they were relatively small.
Second, there was no single, sharp peak in a range of significant crosscorrelation coefficients. Instead, there was a large number (up to 50 60, 25 – 30 min) of significant time lags, which, together with their
small values suggest that the strength of the temporal relationship
between SWDs and these states is small, stable and that the
occurrence of SWDs is determined by the vigilance states over a period
of 25 to 30 min.
68
Figure 2. Cross-correlations of total time of sleep-wake states and total time
of SWDs for 1 h (A), 6 min (B) and 0.5 min resolution of the data (C) (n = 12).
AW – orange line - active wakefulness, PW – red line - passive wakefulness,
SWS L – light green line - light slow-wave sleep, SWS D – dark green line 69
deep slow-wave sleep, REM – blue line - REM sleep. Cross-correlation
coefficients are given in mean ± SEM. The time lag 0 is marked by the black
dashed line, confidence limits (> than 2 times respective standard errors) in
red dashed line
3.3. Pearson’s correlation for the dark and the light phase
The strength of the temporal relationship between SWDs and
sleep-wake states, as expressed by the correlation coefficients, was
almost always stronger in the light than in the dark phase,
irrespectively of the bin size. Correlation coefficients for SWDs and
active wakefulness in 1 h and 6 min resolution were both positive and
larger in the light than in the dark phase for 1 h (0.64 ± 0.04 vs. 0.40
± 0.07, p < 0.05) and for the 6 min bin size (0.30 ± 0.09 vs. 0.05 ±
0.08, p < 0.05). Also in the 0.5 min resolution stronger temporal
relationship was found for the light than for the dark phase (-0.13 ±
0.01 vs. 0.06 ± 0.01, p < 0.05).
Similar differences between light and dark were found for the
relationship
between
SWDs
and
passive
wakefulness,
again
irrespective of bin size (all p’s < 0.05) and as well as between SWDs
and deep slow wave sleep. Deep slow-wave sleep was always
negatively correlated with SWDs. This correlation coefficient was also
always higher in the light than in the dark phase (-0.69 ± 0.08 vs 0.39 ± 0.19, p < 0.05 for 1 h; -0.33 ± 0.04 vs 0.10 ± 0.11, p < 0.05
for 6 min and - 0.14 ± 0.01 vs. -0.02 ± 0.01, p < 0.05 for 0.5 min
resolution of the data). The correlation between SWDs and REM sleep
was higher and negative in the dark and positive and lower in the light
phase irrespective of bin size, however, none of the correlation
coefficients reached significance.
70
3.4. Cross-correlations during the dark and the light
phase
The 1 h resolution of the data showed clear peaks for crosscorrelations of SWDs with active wakefulness, deep slow-wave sleep
and REM sleep at the time lag 0 in the dark period, nonetheless they
did not exceed confidence limits (Figure 3 A). In contrast, sharp peaks
at the time lag 0 for active wakefulness, deep slow-wave sleep and
passive wakefulness were seen in the light phase, these for active
wakefulness and deep slow wave sleep were significant (Figure 3 B).
Clear light-dark related differences were also seen in the other two bin
sizes. The direction of cross-correlations in the light phase for 6 and
0.5 min resolutions (Figure 4 A and B, respectively) was the same as
obtained for 1h: positive for active and passive wakefulness, negative
for light and deep slow-wave sleep. However, in the dark phase, next
to being significant at the same lag and in the same direction as those
observed for the light phase, an additional peak(s) with significant
cross-correlations for active wakefulness, light and deep slow-wave
sleep emerged. These additional peaks were observed at negative time
lag -1 (cross-correlation’s value -0.2 ± 0.06 for active wakefulness,
0.28 ± 0.05 for light slow-wave sleep and 0.21 ± 0.08 for deep slowwave sleep in 6 min resolution of the data) and these cross-correlations
were negative. In the highest resolution of the data (0.5 min) these
additional peaks were again observed in the dark phase. However,
these peaks were not sharp. Instead, more than one cross-correlation
coefficient exceeded confidence limits, and their values were smaller in
comparison to 6 min resolution.
Apart from a characteristic decrease in the value of crosscorrelation coefficients across the data resolution, which might suggest
that the strength of the relationship between SWDs and sleep wake
states becomes smaller with a smaller bin size, clear differences
71
between phases were noticed. In the light phase, behavior of particular
sleep-wake state might be seen as a predictor of the occurrence of
SWDs. Presence of additional significant cross-correlations at negative
time lags in the dark phase suggests that the relationship between
SWDs and vigilance states in this phase is bidirectional. Not only can
sleep-wake states predict the probability of the occurrence of SWDs
but also the occurrence of SWDs may predict the probability of the
occurrence of consecutive sleep-wake states.
Figure 3. Cross-correlations of total time of sleep-wake states and total time
of SWDs for 1 h resolution of the data in the dark (A) and the light phase (B)
72
(n = 12). AW – orange line – active wakefulness, PW – red line - passive
wakefulness, SWS L – light green line - light slow-wave sleep, SWS D – dark
green line - deep slow-wave sleep, REM – blue line - REM sleep. Crosscorrelation coefficients are given in mean and SEM. The time lag 0 is marked
by the black dashed line, confidence limits (> than 2 times respective standard
errors) in red dashed line
3.5. Autocorrelations and spectral density for the dark
and the light phase
Clear light-dark differences were found for both autocorrelation
and spectral density functions. Significant autocorrelation coefficients
were found mainly for the first (SWDs and passive wakefulness), first
and second (light slow-wave sleep) and 1st to 5th time lags (deep slowwave sleep) in the light phase, while during the dark phase
autocorrelations were either insignificant (SWDs, passive wakefulness)
or restricted to the first time lag (light and deep slow-wave sleep).
There were no differences between the dark and the light phase for
active wakefulness and REM sleep. In general, higher spectral density,
for practically all periods, was observed in the dark phase for SWDs,
active wakefulness, and REM sleep. Time series of SWDs were
characterized by high spectral density in a range of short periodicities
(12 to 42 min) with two highest peaks at 42 min and also in 6 h in the
dark phase. In the light phase, spectral density was lower, with
maxima at 1.5 and 2.4 h. Similarly, active wakefulness showed higher
spectral density in the dark phase with three dominant peaks at 48
min, 1 h and 1.5 h, while in the light phase spectral density was lower,
peaking at 42 min, 1.2 and 1.7 h. Dominant periodicities in REM sleep
in the dark phase were: 48 min, 1.5 and 4 h; in the light phase: 24
min, 54 min and 1.7 h. Spectral density in the dark phase was found
to be higher in comparison to the light phase. Contrary to SWDs and
sleep-wake states mentioned above, passive wakefulness, light and
deep slow-wave sleep showed greater values of spectral density in the
73
light phase. The occurrence of passive wakefulness in the dark phase
was characterized by many short periodicities within a range of 12 min
to 1.3 h with a low spectral density in comparison to the light phase
with one dominant peak at 1.7 h. Similarly, light slow-wave sleep
showed higher spectral density in the light phase (dominant peaks:
1.2; 1.7 and 6 h), in the dark phase those peaks were shifted towards
shorter periodicities, implicating higher frequencies. The occurrence of
deep slow-wave sleep in the dark period was dominated by three
periodicities: 48 min, 1 and 1.3 h. In the light phase, apart from greater
values of spectral density, a shift in maximal value (1.7 h and 6 h) was
observed. An example of autocorrelation and spectral density functions
of SWDs and deep slow-wave sleep of a single animal in the dark and
light period is presented in Figure 5.
74
75
Figure 4. Cross-correlations of total time of sleep-wake states and total time
of SWDs in the light and the dark phase for 6 min (A) and 0.5 min (B) resolution
of the data (n = 12). AW – orange line - active wakefulness, PW – red line passive wakefulness, SWS L – light green line - light slow-wave sleep, SWS D
– dark green line - deep slow-wave sleep, REM – blue line - REM sleep. Crosscorrelation coefficients are given in mean and SEM. The time lag 0 is marked
by the black dashed line, confidence limits (> than 2 times respective standard
errors) in red dashed line
Figure 5. An example of autocorrelation and spectral density functions of
SWDs (A and C, respectively) and deep slow-wave sleep (B and D,
respectively) in the light and the dark phase for 6 min resolution of the data of
a single subject. Confidence limits (> than 2 times respective standard errors)
for the autocorrelations are marked by the red dashed lines
76
4. Discussion
The temporal relationship between SWDs and sleep-wake states
was investigated by means of correlation and cross-correlation
functions applied across the 24 h period with different resolutions.
Firstly, strong correlations were found between the total time of SWDs
and active wakefulness, and deep slow-wave sleep for the 1 h
resolution data. Secondly, detailed cross-correlation analysis of higher
resolutions revealed that SWDs and sleep-wake states predict each
other’s occurrence. Thirdly, the strength of the temporal relationship
between SWDs and sleep-wake states was different between the light
and the dark phase.
A relationship between the occurrence of absence seizures and
vigilance states is commonly investigated by means of the momentary
approach, the identification of sleep-wake state directly preceding
SWDs (Drinkenburg et al., 1991; Lannes et al. 1988). Based on
previously established relationships (Drinkenburg et al., 1991), one
might predict that the direction of both correlations: active wakefulness
- SWDs and deep slow-wave sleep – SWDs, should be negative.
However, active wakefulness appeared to be strongly and positively
correlated with SWDs at the 1 h bin, this means that both functions cooccur over time. Although a bin length of 1 h is a relatively large time
scale in a view of the dynamics of rat sleep-wake organization, it
reveals a close relationship between SWDs and active wakefulness.
Their highly daily rhythmic occurrence of active wakefulness/motor
activity and absence seizures suggest an influence of the circadian
timing system (Ibuka et al., 1975; Smyk et al., 2011; Stephan and
Zucker, 1972). Under LD 12:12, the shape of these rhythms is similar,
as could be seen in Fig. 1. Both functions dominate during the dark
phase, while their minimal level is seen during the first few hours of
the light phase (Smyk et al., 2011, Smyk et al., 2012; van Luijtelaar
77
and Coenen, 1988). The rhythm of slow-wave sleep is a mirror
reflection of the activity rhythm and SWDs: low level during the dark
phase and a sharp peak at the beginning of the light phase (Sei et al.,
1997). This may be the reason why, if analyzed in 1 h resolution, high
negative correlations were observed. Similar high correlations between
SWDs on the one side and deep slow-wave sleep and active
wakefulness (positive) were also seen in a 12 h sleep deprivation study
during the light period in WAG/Rij rats (Drinkenburg et al., 1995).
Data, in 2 h bins, showed that during sleep deprivation bins with the
highest amount of wakefulness and, with the lowest amount of deep
slow-wave sleep, were at the same times the bins with the highest
number of SWDs. During the recovery period, characterized by a
rebound of deep slow-wave sleep, bins with the highest amount of this
type of sleep were bins with the lowest number of SWDs (Drinkenburg
et al., 1995). The intimate co-occurrence between active wakefulness
and SWDs as found in our present study might be due to the polyphasic
character of the rat sleep and the short time lag between a period of
active wakefulness, the transition to passive wakefulness and a drowsy
state. The latter is the state at which SWDs are most prevalent. It is
therefore also expected that changes in cortical excitability associated
with the rhythm of active wakefulness might shape the rhythm of SWDs
and be responsible for a close relationship between these two.
Although the description of the temporal relationship of SWDs
and sleep-wake states in 1 h bins is suited for the study of slow
processes, it is quite long when considering the duration of SWDs or
the duration of the sleep-wake cycle of the rat (Coenen and van
Luijtelaar, 2003; Trachsel et al., 1991; van Luijtelaar and Bikbaev,
2007). In 6 min resolution, all states, except REM sleep, showed the
highest cross-correlation coefficients at positive time lag 1, which
means that the total time of a particular state in this bin might serve
as a significant predictor of the time of SWDs in the following 6 min
78
epoch. Both active and passive wakefulness states, were positively
cross-correlated with SWDs suggesting that their high amount will be
followed by a relatively long total duration of SWDs, while negative
cross-correlations between SWD with light and deep slow-wave sleep
suggests the opposite. Interestingly, there was no sharp, dominant
peak in cross-correlation coefficients with a resolution of 0.5 min,
instead many cross-correlations were outside the confidence limits,
indicating that the temporal relationship between SWDs on the one
side and active wakefulness, light and deep slow wave sleep on the
other is determined by their occurrence in 40 to 60 30-s epochs.
Similarly as for 6 min resolution, both wakefulness states were crosscorrelated
positively,
both
slow-wave
sleep
states
negatively.
Interestingly, small significant cross-correlation coefficients were
additionally found for negative, more distant time lags suggesting an
influence of SWDs themselves on the amount of sleep-wake states.
Indeed, a shorter sleep cycle has been found in symptomatic WAG/Rij
rats compared to age matched non-epileptic controls at the end of the
light period suggesting a circadian vulnerability for mainly slow-wave
sleep to be interrupted, in addition, the epileptic strain had shorter REM
sleep episodes (Gandolfo et al., 1990; van Luijtelaar and Bikbaev,
2007). Clinical and experimental data yield also evidence for a
reciprocal relationship between sleep and epilepsy. Not only may
different sleep states alert the occurrence of seizures, but also seizures
themselves disrupt the organization of the sleep-wake cycle (Baretto
et al., 2002; Bazil et al., 2000; Maganti et al., 2005; Yi et al., 2012).
The third main finding of the present study was a different
strength of correlation and the temporal relationship between SWDs
and vigilance states in the dark and light part of the 24 h day. Pearson’s
correlation coefficients were higher in the light than in the dark phase
for wakefulness (active and passive) and deep slow-wave sleep,
irrespective of the resolutions. Also autocorrelation coefficients of
79
SWDs, passive wakefulness and slow-wave sleep states were higher or
sometimes significant only in the light period. There were also
noticeable differences between the light and dark period in spectral
density, for some states the peaks were higher in the light than in the
dark phase (passive wakefulness, light slow-wave sleep, deep slowwave sleep), for others it was reversed (SWDs, active wakefulness and
REM sleep). Although there are clear differences in the percentage of
time of sleep-wake states and in SWDs between light and dark, the
amount of a certain sleep related variable does not determine the
amplitude of the spectral density functions. Therefore, there must be
different or additional mechanisms responsible for the differences in
temporal organization between light and dark phase.
In rats entrained to LD 12:12, 24 h rhythmicity in sleep-wake
states is strong. However, in the absence of circadian influences, it is
dominated by ultradian periodicities (Eastman et al., 1984; Ibuka et
al., 1977). Stephenson et al. (2012) revealed that the period of an
ultradian rhythm in wake, slow-wave and REM sleep in rats kept in
12:12 photoperiod varies randomly across a day within a range of 3 to
6 h. A phase-related difference was noticed: there was a more
prominent rhythm during the light than during the dark phase. This
was explained by the authors by a progressive desynchronization of
ultradian oscillations throughout the circadian cycle (Stephenson et al.,
2012). Our autocorrelation data also suggest that the occurrence of
SWDs and almost all sleep-wake states have a stronger temporal
control and that these temporal relationships are more stable during
the light period compared to the dark period. Interestingly, higher
spectral density functions that implies stronger rhythms were found in
the dark period. The concept of the light onset entraining circadian
timing system, that is gradually desynchronizing during the latter hours
of the light phase and the subsequent dark period and giving more and
more room across the evolving time for ultradian rhythms of sleep80
wake states to develop, might also serve as an explanation for the
results obtained in the dark period: weaker and bidirectional temporal
relationships between SWDs and sleep-wake states and more room
for larger independent ultradian rhythms. It can be hypothesized that,
if the presence of light at the circadian time 0 (CT 0) entrains two (or
more) rhythms with different intrinsic period length each day to 24 h,
then the dark phase would be the period during which the differences
in temporal relationship between them will be most pronounced.
The present study has some limitations. A major one is the
arbitrary choice of time frames for analysis of temporal relationships.
The number and strength of correlations and cross-correlations might
depend on the bin size. However, preliminary analysis of the results
included also 0.5 h and 10 min bins. The same tendencies were
observed: an increase in data resolution was accompanied by a
decrease of a value of coefficients. Another limitation might be the
length of an epoch, based on which sleep-wake states were scored by
the automated system. The program used 2 s epochs, different length
e.g. 15 s might yield different results. On the other hand, the length of
the smallest bin size in a final analysis was 30 s.
The goal of the study was to investigate the temporal
relationship between absence seizures and states of vigilance. We
demonstrated that the temporal relation of SWDs with the different
sleep-wake states depend on the bin size chosen. 1 h resolution of the
data, typical for circadian research, gives opposite results as 30-s
resolution but this regards only active wakefulness. The relationship
between SWDs and deep slow-wave sleep is always negative,
irrespective of the resolution. This suggests that deep slow wave sleep
is a reliable factor determining the (non-)occurrence of absence
seizures. The present study revealed also clear phase related
differences in temporal relationship between variables investigated and
their different organization in time and frequency domain. The light
81
phase of 12:12 photoperiod appeared to be the time during which
stronger temporal relationship between SWDs and sleep-wake states
took place. It is also the phase of higher stability of SWDs and vigilance
states over time. The dark phase, however, is a period during which
the relationship between absence seizures and sleep-wake states was
weaker and bidirectional. The occurrence of SWDs and active
wakefulness in that phase was dominated by many strong rhythmic
oscillations in a range of short periodicities, while the spectral density
of sleep-related states such as passive wakefulness, light and deep
slow-wave sleep was lower. The results of their study suggest different
mechanisms
influencing
the
temporal
organization
of
sleep,
wakefulness and absence seizures in the light and the dark period.
However, to reveal these mechanisms further investigations are
needed.
5. Acknowledgements
The authors would like to thank Heidi Huysmans and Dirk Nuyts
for their excellent technical assistance and express the gratitude to
Janssen Research & Development, a Division of Janssen Pharmaceutica
NV, Beerse, Belgium for supplying excellent facilities to perform the
study.
6. Declaration of Interest
The authors report no known conflict of interest.
7. References
Ahnaou, A., Drinkenburg, W.H., 2011. Neuromedin U(2) receptor signaling mediates
alteration of sleep-wake architecture in rats. Neuropeptides. 45, 165-174.
82
Badawy, R.A., Macdonell, R.A., Jackson, G.D., Berkovic, S.F., 2009. Why do seizures in
generalized epilepsy often occur in the morning? Neurology. 73, 218-222.
Barreto, J.R., Fernandes, R.M., Sakamoto, A.C., 2002. Correlation of sleep
macrostructure parameters and idiopathic epilepsies. Arq Neuropsiquiatr. 60,
353-357.
Bazil, C.W., Castro, L.H., Walczak, T.S., 2000. Reduction of rapid eye movement sleep
by diurnal and nocturnal seizures in temporal lobe epilepsy. Arch. Neurol. 57,
363-368.
Borbély, A.A., 1975. Circadian rhythm of vigilance in rat: Modulation by short light-dark
cycles. Neurosci. Lett. 1, 67-71.
Borbély, A.A., 1982. A two process model of sleep regulation. I Physiological basis and
outline. Human Neurobiol. 1, 195-204.
Coenen, A.M.L., van Luijtelaar, E.L.J.M., 2003. Genetic animal model for absence
epilepsy: a review of the WAG/Rij strain of rats. Behav. Genet. 33, 635-55.
Daan, S., Beersma, D.G., Borbély, A.A., 1984. Timing of human sleep: recovery process
gated by a circadian pacemaker. Am. J. Physiol. 246, 161-183.
Depaulis, A., van Luijtelaar, G., 2006. Genetic models of absence epilepsy in the rat,
in: Pitkänen, A., Schwartzkroin, P.A., Moshe, S.L., (Eds.), Models of Seizures
and Epilepsy. Elsevier Academic Press, San Diego, pp. 233-248.
Drinkenburg, W.H., Coenen, A.M., Vossen, J.M., van Luijtelaar, E.L., 1991. Spike-wave
discharges and sleep-wake states in rats with absence epilepsy. Epilepsy Res.
9, 218-224.
Drinkenburg, W.H., Coenen, A.M., Vossen, J.M., van Luijtelaar, E.L., 1995. Sleep
deprivation and spike-wave discharges in epileptic rats. Sleep 18, 252-256.
Duncan, J.S., 1997. Idiopathic generalized epilepsy with typical absences. J Neurol.
244, 403-411.
Eastman, C.I., Mistlberger, R.E., Rechtschaffen, A., 1984. Suprachiasmatic nuclei
lesions eliminate circadian temperature and sleep rhythms in the rat. Physiol.
Behav. 32, 357–368.
Friedman, L., Bergmann, B.M., Rechtschaffen, A., 1979. Effects of sleep deprivation on
sleepiness, sleep intensity and subsequent sleep in the rat. Sleep. 1, 369-391.
Gandolfo, G., Romettino, S., Gottesmann, C., van Luijtelaar, G., Coenen, A.. 1990.
Genetically epileptic rats show a pronounced intermediate stage of sleep.
Physiol. Behav. 47, 213-215.
83
Halasz, P., Terzano, M.G., Parrino, L., 2002. Spike-wave discharge and the
microstructure of sleep-wake continuum in idiopathic generalized epilepsy.
Neurophysiol. Clin. 32, 38-53.
Ibuka, N., Inouye, S.I., Kawamura, H., 1977. Analysis of sleep-wakefulness rhythms in
male rats after suprachiasmatic nucleus lesions and ocular enucleation. Brain
Res. 122, 33-47.
Ibuka, N., Kawamura, H., 1975. Loss of circadian rhythm in sleep-wakefulness cycle in
the rat by suprachiasmatic nucleus lesions. Brain Res. 96, 76-81.
Karpova, A.V., Bikbaev, A.F., Coenen, A.M., van Luijtelaar, G., 2005. Morphometric
Golgi study of cortical locations in WAG/Rij rats: the cortical focus theory.
Neurosci. Res. 51, 119-128.
Lannes, B., Micheletti, G., Vergnes, M., Marescaux, C., Depaulis, A., Warter, J.M., 1988.
Relationship between spike-wave discharges and vigilance levels in rats with
spontaneous petit mallike epilepsy. Neurosci. Letters. 94, 187-191.
Lüttjohann, A., Zhang, S., de Peijper, R., van Luijtelaar, G., 2011. Electrical stimulation
of the epileptic focus in absence epileptic WAG/Rij rats: assessment of local and
network excitability. Neuroscience. 188, 125-134.
Maganti, R., Sheth, R.D., Hermann, B.P., Weber, S., Gidal, B.E., Fine, J., 2005. Sleep
architecture in children with idiopathic generalized epilepsy. Epilepsia. 46, 104109.
Matzen, J., Buchheim, K., Holtkamp, M., 2012. Circadian dentate gyrus excitability in a
rat model of temporal lobe epilepsy. Exp. Neurol. 234, 105-111.
Meeren, H., van Luijtelaar, G., Lopes da Silva, F., Coenen, A., 2005. Evolving concepts
on the pathophysiology of absence seizures: the cortical focus theory. Arch.
Neurol. 62, 371-376.
Midzyanovskaya I., Strelkov, V., van Rijn, C., Budziszewska, B., van Luijtelaar, E.,
Kuznetsova, G.,2006. Measuring clusters of spontaneous spike-wave discharges
in absence epileptic rats. J. Neurosci. Methods. 154, 183-189.
Ovchinnikov, A., Lüttjohann, A., Hramov, A., van Luijtelaar, G., 2010. An algorithm for
realtime detection of spike-wave discharges in rodents. J. Neurosci. Methods.
194, 172-178.
Paxinos, G., Watson, C., 1998. The rat brain in stereotaxic coordinates. Academic Press,
San Diego.
Polack, P.O., Guillemain, I., Hu, E., Deransart, C., Depaulis, A., Charpier, S., 2007.
Deep layer somatosensory cortical neurons initiate spike-and-wave discharges
in a genetic model of absence seizures. J. Neurosci. 27, 6590-6599.
84
Sei, H., Furuno, N., Morita, Y., 1997. Diurnal changes of blood pressure, heart rate and
body temperature during sleep in the rat. J. Sleep Res. 6, 113-119.
Simasko, S.M., Mukherjee, S., 2009. Novel analysis of sleep patterns in rats separates
periods of vigilance cycling from long long durationwake events. Behav. Brain
Res. 196, 228-236.
Sitnikova, E., van Luijtelaar, G., 2007. Electroencephalographic characterization of
spikewave discharges in cortex and thalamus in WAG/Rij rats. Epilepsia. 48,
2296-2311.
Smyk, M.K., Coenen, A.M., Lewandowski, M.H., van Luijtelaar, G., 2011. Endogenous
rhythm of absence epilepsy: relationship with general motor activity and sleepwake states. Epilepsy Res. 93, 120-127.
Smyk, M.K., Coenen, A.M., Lewandowski, M.H., van Luijtelaar, G., 2012. Internal
desynchronization facilitates seizures. Epilepsia. 53, 1511-1518.
Stephan, F.K., Zucker, I., 1972. Circadian rhythms in drinking behavior and locomotor
activity of rats are eliminated by hypothalamic lesions. Proc. Natl. Acad. Sci.
USA. 69, 1583-1586.
Stephenson, R., Lim, J., Famina, S., Caron, A.M., Dowse, H.B., 2012. Sleep-wake
behavior in the rat: Ultradian rhythms in a light-dark cycle and continuous bright
light. J. Biol. Rhythms. 27, 490-501.
Suntsova, N., Kumar, S., Guzman-Marin, R., Alam, M.N., Szymusiak, R., McGinty, D.,
2009. A role for the preoptic sleep-promoting system in absence epilepsy.
Neurobiol. Dis. 36, 126-141.
van Luijtelaar, E.L., Coenen, A.M., 1986. Two types of electrocortical paroxysms in an
inbred strain of rats. Neurosci. Lett. 70, 393-397.
van Luijtelaar, E.L.J.M., Coenen, A.M.L., 1988. Circadian rhythmicity in absence
epilepsy in rats. Epilepsy Res. 2, 331-336.
van Luijtelaar, E.L., van der Werf, S.J., Vossen, J.M., Coenen, A.M., 1991. Arousal,
performance and absence seizures in rats. Electroecephalogr. Clin.
Neurophysiol. 79, 430-434.
van Luijtelaar, G., Coenen, A., 1993. The behavioural pharmacology of sleep, in van
Haaren, F. (Eds.), Methods in behavioural pharmacology. Elsevier Science
Publishers B.V., Amsterdam, pp 575-602.
van Luijtelaar, E.L., Drinkenburg, W.H., van Rijn, C.M., Coenen, A.M., 2002. Rat
models of genetic absence epilepsy: what do EEG spike-wave discharges tell us
about drug effects? Methods Find. Exp. Clin. Pharmacol. 24, 65-70.
85
van Luijtelaar, G., Bikbaev, A., 2007. Midfrequency cortico-thalamic oscillations and
the sleep cycle: genetic, time of day and age effects. Epilepsy Res. 73, 259265.
van Twyver, H., 1969. Sleep patterns of five rodent species. Physiol. Behav. 4, 901905.
Westmijse, I., Ossenblok, P., Gunning, B., van Luijtelaar, G., 2009. Onset and
propagation of spike and slow wave discharges in human absence epilepsy: A
MEG study. Epilepsia. 50, 2538-2548.
Yi, P.L., Chen, Y.J., Lin, C.T., Chang, F.C., 2012. Occurrence of epilepsy at different
zeitgeber times alters sleep homeostasis differently in rats. Sleep. 35, 16511665.
86
CHAPTER 3
Endogenous rhythm of absence epilepsy:
relationship with general motor activity
and sleep-wake states
Published as Smyk MK, Coenen A, Lewandowski MH, van Luijtelaar G.
2011. Epilepsy Research. 2011. 93: 120-127.
Abstract
The rhythms of spontaneously occurring seizures (spike-wave
discharges, SWD) and motor activity, as well as the relationship
between SWD and sleep–wake states were investigated in the WAG/Rij
rat model of absence epilepsy. In order to establish whether SWD are
controlled by external (Zeitgebers) or by endogenous factors such as
circadian influences or the state of vigilance, the study was performed
in entrained and constant dim light conditions.
EEG and motor activity were recorded in the 12:12 light–dark
cycle and in constant dim light conditions. Circadian rhythmicity was
found both for motor activity and the occurrence of SWD in conditions
of entrainment. In constant dim light conditions also circadian rhythms
emerged, however, the change in circadian parameters was opposite
for the rhythm of SWD and motor activity. SWD were preceded mostly
by passive wakefulness and by slow-wave sleep in both experimental
conditions.
It can be concluded that the rhythm of SWD seems to be
generated and controlled by an endogenous mechanism distinct from
that which controls the rhythm of motor activity. The relationship
between SWD and sleep–wake states preceding their occurrences
appeared to be unchanged, suggesting that the mechanism of
generation of SWD is independent of the circadian timing system.
Keywords: absence seizures; spike-wave discharges; WAG/Rij
rats; circadian rhythmicity; motor activity; sleep–wake states
1. Introduction
The majority of mammalian physiological and behavioural
processes are controlled by a circadian timing system. The light-dark
89
cycle, the most prominent synchronizer of biological rhythms, entrains
a circadian master clock, the hypothalamic suprachiasmatic nuclei
(SCN), to a 24-h day (Reppert and Weaver, 2001). The SCN control
other clocks through multiple neuronal connections and humoral
factors, and these clocks express overt circadian rhythms (Buijs and
Kalsbeek, 2001; Morin and Allen, 2006). These various oscillators or
rhythms show a phase-relationship under entrained conditions.
However, the SCN generates a rhythm with a period length
different from 24 h in absence of time cues (Bünning, 1964; Herzog
and Schwarts, 2002). Not only the period may change, but also
parameters such as amplitude and mean as well as the relationships
between rhythms and even uncoupling of distinct circadian rhythms
may occur. This phenomenon is called “internal desynchronization” and
has been reported in humans and animals (Weaver, 1979; Aschoff and
Weaver,
1981;
Erkert,
2000;
Aguzzi
et
al.,
2006).
Internal
desynchronization is thought to deliver evidence supporting a “multiple
oscillatory theory” of the mammalian circadian timing system.
Also pathological processes such as the timing of epileptic
seizures show daily rhythmic fluctuations. Investigations initiated by
Gowers (1885), who classified seizures as diurnal, nocturnal and
diffuse, led to the conclusion that distinct circadian patterns of the
occurrence of epileptic discharges are determined by the type of
epilepsy syndrome, both in humans and in animal models (Gowers,
1885; Langdon-Down and Brain, 1929; Pavlova et al., 2004; Durazzo
et al., 2008; Hofstra et al., 2009; Quigg et al., 1998). It seems that
the sleep-wake or rest-activity cycle plays a major role among the
different circadian factors that ultimately influence the timing of
seizures (Halasz, 1991). Changes in neuronal excitability associated
with these rhythms determine the susceptibility for seizures to occur
at specific or preferred states of vigilance.
90
Idiopathic generalized epilepsy (IGE) with typical absences
belongs to a group of epilepsy syndromes that is strongly affected by
the sleep-wake and rest-activity cycle. Indeed a 24-h rhythm has been
identified for the occurrence of absence seizures (Kellaway et al.,
1980). Absences are characterized by the presence of bilateral,
synchronous, generalized spike-wave discharges (SWD) in the EEG
against a normal background activity. The frequency of the discharges
is about 3 Hz and differs slightly among particular syndromes
(Panayiotopoulos, 1994; Duncan, 1997). Slow-wave sleep, quiet
wakefulness (e.g. physical and mental inactivity associated with
drowsiness), light slow-wave sleep and transitions, mainly awakenings
from slow-wave sleep are the most favourable states for SWD to occur,
both in humans and in animal models (Coenen et al., 1991;
Drinkenburg et al., 1991; van Luijtelaar and Bikbaev, 2007). SWD
hardly occur during the highly desynchronized EEG activity such as
active wakefulness and REM sleep (Dinner, 2002; Halasz et al., 2002).
Rats of the WAG/Rij strain are a well known, validated, genetic
animal model of absence epilepsy sharing common behvioural,
electroencephalographic and pharmacological characteristics with the
human disorder (Coenen and van Luijtelaar, 2003; Depaulis and van
Luijtelaar, 2006). The distribution of the 7 – 11 Hz SWD show a clear
circadian variation (van Luijtelaar and Coenen, 1988; van Luijtelaar et
al., 2001), with a maximum during the early hours of the dark phase
and a minimum at the beginning of the light phase, when rats have the
highest amount of deep slow-wave sleep. The latter state is not
favourable for the occurrence of SWD (Drinkenburg et al., 1991).
Therefore, it is likely that the distribution of SWD is a direct
consequence of the distribution of the vigilance states and the presence
of Zeitgebers.
The purpose of the present study was to verify the putative
endogenous character of the rhythm of SWD. Considering that the
91
probability of the occurrence of SWD is higher during states of physical
inactivity, emphasizing a role of vigilance in the generation of
absences, the relationship between sleep-wake states including motor
activity and the occurrence of SWD was additionally investigated in
order to verify whether the absence of Zeitgeber light on and off
interferes
with
the
dependency
observed
under
conditions
of
entrainment.
2. Material and methods
2.1.
Animals
Seven adult (eleven months old), male WAG/Rij rats, born and
raised in the laboratory of the Donders Centre for Cognition of the
Radboud University Nijmegen, served as subjects. Animals were
maintained on a 12:12 light-dark cycle (lights on at 6:00 am) with food
and water available ad libitum. All experimental procedures were
approved by the Animal Ethical Committee of the Radboud University
Nijmegen.
2.2.
Surgery
Rats were implanted with a standard three-channel electrode
set (MS 333/1-A, Plastic One Inc., Roanoke, VA, USA) under isoflurane
anaesthesia. Two active electrodes were put on the surface of primary
motor and visual cortex. Coordinates of the electrodes (with skull
surface flat and bregma zero-zero) were: AP +2.0, ML 3.5 and AP -6.0,
ML 4.0, respectively. The reference electrode was put over the
cerebellum. Animals were allowed to recovery for 14 days after the
surgery.
92
2.3.
EEG and activity recordings
Each animal was individually placed in sound-attenuated,
ventilated recording cage and connected with a computer-based data
acquisition system (Dataq Instruments, Akron, OH, USA). Rats were
connected to a swivel which allowed them to move freely in their cages.
The EEG signals were amplified, band pass filtered between 1 and 100
Hz, and sampled at 200 Hz. General motor activity was measured by a
passive infrared sensor (PIR) mounted 40 cm above the bottom of the
animal’s cage and connected to the data acquisition system. This
system, which has been used earlier and validated against visual
observation, measures the total body movements over a period of
time, showing a large amplitude during intense body movements such
as digging, rearing, grooming and exploration, and an almost zero
amplitude during behavioural immobility. Animals were allowed to
habituate to the recording cages for at least 2 h before the recordings
started.
2.4.
Experimental design
Animals were kept on the 12:12 light-dark cycle (light intensity:
60 lux) which constitutes the condition of entrainment. An overview of
the design of the experiment is presented in Fig. 1. The light regime
was changed after 10 days into constant dim light (5-6 lux), on which
rats were maintained for the following 20 days. EEG recordings were
made continuously during the last four days of the entrained condition
and during the last seven days of the constant dim light condition (from
day 23 to day 30). Motor activity recordings were made throughout the
whole experiment (Fig. 1). Food and water were always available ad
libitum, the presentation of fresh water and the addition of new rat
chow in the constant dim light condition was done at irregular time
93
points, just as the cleaning of cages. This was done only once a week
to minimize intervention and prevent synchronization.
Figure 1. Experimental design
2.5.
Data analysis
Preliminary PIR recordings were visually inspected to define an
activity threshold value. The number of episodes above the threshold
(= active behavior) was counted automatically from the PIR recordings.
Recordings were divided into 6 min bins. Each bin with a predominance
of (> 50%) above the threshold was scored as 1. The value 0 was given
to bins with the predominance of passive behavior. Activity data were
divided into 10 day segments: the first of which represents 10 days
spent in the entrained condition, the second representing the last 10
days of 20 days spent in constant dim light conditions. The period,
mean and amplitude of the activity rhythms of both segments were
analysed by means of the Cosinor method, which yields also a measure
of the robustness of the rhythm (the percentage of variance accounted
for by the rhythm).
94
In order to compare sleep, motor activity, SWD in the active
and passive phases of the entrained and constant dim light periods,
the beginning and the end of the active period in the entrained and
constant dim light conditions was established. The onset of darkness
was taken as the beginning of the active phase for the entrained
condition. The onset of the activity rhythm in the constant dim light
condition was based on the outcomes of the Cosinor method which
gives the accurate estimation of the period length. In addition, visual
inspection of the activity rhythm over days showed marked and long
lasting activity bouts after a period of behavioral inactivity. The
beginning of the activity bouts over days was used to estimate the
beginning of the active period. Next the circadian day was split in two
equal parts (the active and passive phase). It was noticed that the
beginning of the passive period always coincided with long lasting
period of behavioral inactivity.
EEGs were recorded during 4 days in the entrained condition
and during 7 days of the constant dim light condition. SWD were
identified off-line according to well-known criteria (van Luijtelaar and
Coenen, 1986). The Cosinor analysis was applied on 4 and 7 day
segments of the data collected in the entrained and constant dim light
conditions, respectively (data resolution: 6 min, number of SWD per 6
min block), to estimate the circadian period, mean, amplitude and
robustness of the SWD rhythm.
Sleep-wake states were identified off-line by visual inspection
of the EEG and PIR recordings from the last day of the entrained and
the last day of constant dim light condition, commonly used criteria
were used (van Twyver, 1969; van Luijtelaar and Coenen, 1993). The
second and penultimate hour of the dark and the light phase of the
12;12 light-dark cycle and corresponding hours of the active and the
passive phase of the constant dim light condition were analysed. Four
sleep-wake
states
were
scored:
95
active
wakefulness,
passive
wakefulness, slow-wave sleep and REM sleep. To determine the
preference of the occurrence of SWD for a particular state of vigilance,
every 5 s preceding and following each SWD were analysed. Results
were subsequently corrected with regards to the distribution of the
sleep-wake
states
over
the
analysed
hours,
as
proposed
by
Drinkenburg et al. (1991).
2.6.
Statistical analysis
Statistical analysis was performed in Statistica (StatSoft, Inc.,
USA). Multivariate analysis of variance was first used to estimate
differences in parameters between light regimes, only in case of
significant condition effect, Student’s t-tests for dependent samples
were used as post hoc tests. Differences were considered as significant
at p < 0.05.
3. Results
In the entrained condition (24 h, 12:12 light-dark), a rhythm of
motor activity was found with a period length of 24.03 ± 0.04 h.
Animals were more active during the dark phase than during the light
phase of the 12:12 light-dark cycle (p < 0.001).
Multivariate analysis of variance showed significant differences
between experimental condition for both motor activity and SWD
rhythm (F = 84.84, df = 4.3, p < 0.01 and F = 10.91, df = 4.3, p <
0.05, respectively). Subsequent post hoc tests showed that the period
of motor activity rhythm increased to 24.63 ± 0.08 h (p < 0.01), while
the mean, amplitude and robustness of the rhythm decreased (all p’s
< 0.05) in the constant dim light. The number of activity counts was
higher for the active than for the passive phase of the constant dim
light condition (p < 0.001). A comparison between the entrained and
96
the constant dim light condition revealed that animals were less active
in the active phase and more active in the passive phase of the
constant dim light condition (both p’s < 0.05). Data are presented in
Table 1.
Entrained
Period [h]
Meana
Amplitude
Robustnessb
Constant dim light
24.03 ± 0.04
24.63 ± 0.08*
0.20 ± 0.03
0.16 ± 0.06*
0.18 ±0.01
0.11 ± 0.01*
8.58 ± 1.59
4.28 ± 0.64*
342.43 ± 46.40
241.86 ± 33.18*
85.86 ± 17.30
109.43 ± 17.37*
Activity counts
Dark/active
Light/passive
Table 1. Mean and SEM of the various parameters of the motor activity rhythm
in the entrained and constant dim light condition. a An estimate of central
tendency of the distribution of values of an oscillating variable. b The
percentage of variance accounted for by the rhythm. * Entrained vs dim light
condition, p < 0.05
The occurrence of SWD in the entrained phase showed also a
clear, circadian rhythm. The minimum occurred during the first 2 h of
the light phase. Then the number gradually increased to reach a
maximum at the end of that phase. At the beginning of the dark phase
a small drop occurred followed by an increase and then again a
continuous decrease (Fig. 2). There were no differences in the number
of SWD between the light and the dark phase of the 24-h rhythm, as
well as between the active and the passive phase of the SWD rhythm
recorded in the constant dim light condition. However, the number of
SWD was higher in the active phase in comparison with the dark phase
97
of the entrained condition (p < 0.05). The period of the SWD rhythm
in constant dim light condition was shorter than 24 h (22.93 ± 0.53),
although the difference just failed to reach significance. However, each
animal showed a significant (it means that a significant amount of
variation across time points was explained by the fitted cosinus),
circadian rhythm in the occurrence of SWD under the constant dim light
condition. Significant changes were also found for the mean and
amplitude of the rhythm, which were larger in the constant dim light
condition (p < 0.05; p < 0.001, respectively), as well as for the
robustness, which was smaller than in the entrained condition (p <
0.05). Results are presented in Table 2.
Entrained
Period [h]
Meana
24.01 ± 0.04
Constant dim light
22.93 ± 0.53
1.41 ± 0.18
1.82 ± 0.15*
5.45 ±0.48
8.32 ± 0.68*
4.44 ± 0.70
1.57 ± 0.29*
Dark/active
631.43 ± 81.08
919.28 ± 65.89*
Light/passive
738.43 ± 77.38
865.71 ± 90.38*
Amplitude
Robustnessb
SWD number
Table 2. Mean and SEM of the various parameters of the SWD rhythm in the
entrained and constant dim light condition. a An estimate of central tendency
of the distribution of values of an oscillating variable. b The percentage of
variance accounted for by the rhythm. * Entrained vs dim light condition, p <
0.05
98
Figure 2. Double plotted graph of 4 days of SWD (mean number and SEM of
SWD per hour) activity (one X-axis represents two days [48 h], the activity of
the second day is plotted again as the first day in the next X-axis). The dark
phase of the 12:12 light-dark cycle is marked in grey
99
The comparison of parameters of the activity and SWD rhythm
in the constant dim light condition revealed significant differences. The
period length of the SWD rhythm was shorter and the rhythm less
robust (p’s < 0.05), while the amplitude and the mean of the SWD
rhythm was higher in comparison to the activity rhythm (both p’s <
0.001).
The distribution of particular states of vigilance in the entrained
condition is presented in Table 3. Active wakefulness, dominating at
the beginning of the dark phase, decreased gradually till the beginning
of the light phase, in which it reached its lowest level. At the end of the
light phase the amount of active wakefulness slightly increased. The
same pattern was also observed for passive wakefulness, while the
course of the slow-wave sleep was opposite. The lowest amount of
slow-wave sleep was observed at the beginning of the dark phase,
subsequently followed by a gradual increase till the beginning of the
light phase when it reaches its maximum. Contrary to active and
passive wakefulness, the amount of slow-wave sleep decreased at the
end of the light phase. The percentage of REM sleep gradually
increased during the investigated hours.
Active wakefulness in the constant dim light condition was found
to be the only state of vigilance, which preserved its pattern observed
in the entrained condition. The course of the remaining sleep-wake
states was altered. The lowest amount of passive wakefulness was
found at the end of the active phase, followed by an increase at the
beginning of the passive phase and a subsequent decrease at the end.
A minimal amount of slow-wave sleep was recorded, the same as in
the entrained condition, at the beginning of the active phase. The
maximum was shifted from the beginning of the passive phase to the
end of the active phase, after which a gradual decrease was observed.
A slight alteration was also found for REM sleep. The same pattern for
three investigated periods (the entire active phase and the beginning
100
of the passive phase) was preserved, with the exception of the end of
the passive phase in which a decrease was observed. The distribution
of the particular states of vigilance recorded in the constant dim light
condition is presented in Table 3.
Dark phase
Beginning
Light phase
End
Beginning
End
Entrained condition
Active
wakefulness
60.22 ± 3.61
38.21 ± 9.22
12.13 ± 4.18
15.56 ± 3.84
Passive
wakefulness
18.26 ± 1.96
13.08 ± 3.38
5.00 ± 1.24
10.45 ± 1.66
Slow-wave
sleep
19.10 ± 3.64
40.70 ± 8.54
73.33 ± 5.37
61.47 ± 2.82
REM sleep
2.42 ± 1.22
8.01 ± 3.21
9.53 ± 1.75
12.52 ± 1.72
Constant dim light condition
Active
wakefulness
54.16 ± 7.41
26.54 ± 8.88
23.02 ± 6.16
39.56 ± 5.53
Passive
wakefulness
12.41 ± 1.14
6.77 ± 1.63
15.61 ± 6.93
10.95 ± 2.60
Slow-wave
sleep
30.26 ± 5.92
59.65 ± 7.23
49.69 ± 7.86
40.58 ± 6.33
REM sleep
3.17 ± 2.01
7.04 ± 2.10
11.68 ± 2.55
8.90 ± 1.30
Table 3. Mean and SEM of the percentage of particular states of vigilance
during investigated hours in the entrained and constant dim light conditions
A comparison made between the entrained and the constant
dim light condition revealed that in the active phase of the constant
dim light condition the percentage of passive wakefulness was lower (p
< 0.05). In the passive phase of the constant dim light condition the
101
percentage of active wakefulness was higher (p < 0.05), whereas the
percentage of slow-wave sleep was lower (p < 0.05).
Passive wakefulness and slow-wave sleep have been found to
dominate among states of vigilance preceding the occurrence of SWD
in the entrained condition. 39% and 35% of SWD recorded were
preceded
by
passive
wakefulness
and
by
slow-wave
sleep,
respectively, active wakefulness (15%) and REM sleep (11%) were the
least probable states. This tendency was preserved in the constant dim
light condition. 47% of SWD were preceded by passive wakefulness,
32% by slow-wave sleep, 17% by active wakefulness and 4% by REM
sleep. No significant differences between the entrained and the
constant dim light condition were found. Data are presented in Fig. 3.
Passive wakefulness in the entrained condition followed the
occurrence of SWD most frequently (71%), active wakefulness, with a
probability of 17%, was the second state. Slow-wave sleep and REM
sleep were recorded after SWD in 9% and 3% of cases, respectively.
The same relationship was found in the constant dim light condition for
two sleep-wake states: passive wakefulness and REM sleep. Passive
wakefulness was the most and REM sleep the least probable to occur
after SWD (62% and 9%, respectively). Opposite to the entrained
condition, slow-wave sleep was found to be the second most frequent
state following the occurrence of SWD (18%), active wakefulness was
the third (11%). Data are presented in Fig. 3.
Statistical analysis revealed that the probability that SWD was
followed by slow-wave sleep is significantly higher in the constant dim
light condition than in the entrained condition (p < 0.05).
102
Figure 3. States of vigilance preceding (A and C) and following (B and D) the
occurrence of SWD in the entrained (A and B) and constant dim light conditions
(C and D). AW = active wakefulness, PW = passive wakefulness, SWS = slowwave sleep, REM = REM sleep. SWS after SWD occurred more often in the
constant dim light condition, * p < 0.05
4. Discussion
In the present study the rhythm of absence seizures, the
rhythm of general motor activity and the relationship between absence
seizures and states of vigilance in WAG/Rij rats were investigated
under
two
different
experimental
conditions:
the
condition
of
entrainment to the 12:12 light-dark cycle, and the condition of
constant dim light. Here it is shown for the first time that there is an
endogenous rhythm of absence seizures, given that the circadian
rhythmicity in the occurrence of SWD is still present in the absence of
the most important Zeitgeber. Moreover, under the constant dim light
103
condition significant changes in various parameter of the rhythm of
SWD and in general motor activity are observed, while additionally
internal desynchronization between these rhythms is observed.
Contrary to these changes, the well established relationship between
the occurrence of SWD and states of vigilance in not altered by the
constant environment. Regardless of environmental conditions, SWD
are most frequently occurring during passive wakefulness and slowwave sleep.
The present results confirm previous observations of circadian
rhythmicity in rats’ motor activity (Büttner and Wollnik, 1984; Ray et
al., 2004; Refinetti, 2006). Animals are more active during the dark
than during the light phase in the entrained condition. The constant
dim light alters parameters of the motor activity rhythm. The period of
the rhythm becomes longer than 24 h, in agreement with “Aschoff’s
rule” stating that, in nocturnal animals maintained on constant light,
an increase in light intensity corresponds with an increase in the period
length of the rhythm. It can be concluded that a slight, but significant,
increase in the period length observed in the present experiment is
caused by the presence of constant low intensity light on which animals
are maintained (Aschoff, 1960; Daan and Pittendrigh, 1976; Fukuhara
et al., 2005). The remaining parameters such as the mean, amplitude
and robustness of the rhythm are found to be decreased in comparison
with the condition of entrainment. An inhibitory effect of light on the
activity of nocturnal animals is well established (Redlin, 2001). A longlasting, continuous exposure to dim light has been reported to reduce
the amplitude and the mean of activity rhythm with a possible
disruption of the circadian pattern (Borbély and Neuhaus, 1978; Albers
et al., 1981; Eastman and Rechtschaffen, 1983; Ikeda and Inoue,
1998).
A circadian rhythmicity in the occurrence of SWD in WAG/Rij
rats is found in the entrained condition. The observed pattern shares
104
common characteristics with previous reports (van Luijtelaar and
Coenen, 1988). The circadian distribution is a direct consequence of
the changes in sleep-wake states over the course of the 24-h day
(Drinkenburg et al., 1991). Light slow-wave sleep, passive wakefulness
and transitions between different states of vigilance have been shown
to promote the occurrence of SWD, while deep slow-wave sleep, REM
sleep and active wakefulness are states exerting opposite effects
(Lannes et al., 1988; Coenen et al., 1991; Drinkenburg et al., 1991).
In both studies the nadir of the rhythm was found to be located during
the first hours of the light phase, which correlates with a high amount
of deep slow-wave sleep at that time of the light-dark cycle (Coenen
et al., 1991; Drinkenburg et al., 1991). A slight difference was noted
for the maximum of the rhythm: it occurred at the end of the light
phase, while van Luijtelaar and Coenen (1986) pointed out that this
maximum emerged at the early hours of the dark phase. Despite the
high number of SWD in the dark phase, a decline was observed just at
the beginning of it. For nocturnal animals kept in laboratory conditions,
turning off the light is a strong signal to become behaviorally active.
Therefore, the characteristic peak of activity at the beginning of the
dark period could be responsible for such a decline, as active
wakefulness suppresses the occurrence of SWD (Coenen et al., 1991;
Drinkenburg et al., 1991).
The changes in the SWD rhythm that took place in the constant
dim light condition show an opposite direction in comparison with
changes in the rhythm of motor activity. The period length of the
absence epilepsy rhythm becomes shorter than 24 h. Although this
change is not significant according to parametric tests for the group as
unity, each animal showed a clear circadian rhythm with a new, shorter
period length in the range of 20.6 – 23.9 h. A non-parametric sign test
confirmed that the period length was indeed shorter in constant dim
light. A circadian rhythm of limbic seizures was first described by Quigg
105
et al. (2000) in a model for temporal lobe epilepsy: the seizures
occurred in a phasic nonrandom pattern both under entrainment and
under constant darkness with a period length of about 24 h. Similar to
the present results, the number of seizures, as well as the amplitude
of the rhythm increased in an environment lacking external time cues.
Contrary to a decrease in the amplitude and in the mean of the
motor activity rhythm, the corresponding parameters of the rhythm of
SWD increased. Motor activity and waking are suppressed in the
condition of continuous light (Borbély and Neuhaus, 1978). This
suppression can be a putative cause for the increase in the number of
SWD considering the intimate negative relationship between vigilance
and arousal, and the occurrence of SWD. Moreover, it has been shown
that the condition of constant light disrupts the sleep-wake pattern. A
significant increase of slow-wave and REM sleep is found under
constant light, together with an elevated number of REM sleep episodes
(Borbély and Neuhaus, 1978). Similar results were obtained by Ikeda
et al. (2000), additionally pointing towards a redistribution of the
amount of particular sleep stages in the active and the passive phase.
In the present study, dim light alters the course, as well as the amount
of sleep-wake states.
The divergence of the period length under constant dim light
between motor activity, with a significant increase, and the SWD
rhythm, with a tendency to decrease, suggests distinct oscillators
responsible for their regulation. Others have found dissociation
between circadian rhythms in continuous dim light in monkeys and rats
(Erket, 2000; Aguzzi et al., 2006). Studies devoted to the process of
desynchronization of oscillators are considered as important evidence
in multiple oscillatory theory of mammalian circadian timing system.
These oscillators may reside in or outside the SCN (Pittendrigh and
Daan, 1976; Shinohara et al., 1995; Jagota et al., 2000).
106
A common finding for both the motor activity and the SWD
rhythm, is a gradually decreasing robustness after the transfer to the
constant dim light condition. Both rhythms show the same tendency,
however, the motor activity rhythm was found to be significantly more
robust throughout the whole experiment than the SWD rhythm. This
suggest that the motor activity rhythm is governed by a stronger
oscillator. Differences in robustness of the rhythms of wake and body
temperature under long-term continuous illumination were earlier
reported: the wake rhythm, thought to be driven by a weaker
oscillator, lost its organization earlier than the body temperature
rhythm (Eastman and Rechtschaffen, 1983). Although the 22.9 h
rhythm of SWD remained identifiable, it was also noticed that it became
less organized. The motor rhythm kept its organization better. Both
rhythms showed a diminished robustness, reflecting a decrease in the
strength of the circadian timing system. Therefore, it seems that under
entrained conditions, the structure of the SWD rhythm is influenced by
changes in sleep-wake states and in the amount and timing of activity
over 24-h day (Coenen et al., 1991; Drinkenburg et al., 1991). A
disrupted organization of general motor activity, expressed by a
decreased robustness of its rhythm, together with a distinct oscillator
acting independently from the oscillator controlling the timing of SWD,
might be responsible for the gradual change of the structure of the
SWD rhythm under the constant dim light condition.
In the present study, the majority of SWD was preceded by
passive wakefulness and slow-wave sleep, while active wakefulness
and REM sleep were rarely recorded just before SWD, consistent with
previous findings from both animal and human studies (Lannes et al.,
1988; Drinkenburg et al., 1991; Halasz et al., 2002). The relationship
between SWD and states of vigilance is the same in both: the entrained
and constant dim light condition, strongly suggesting that the
107
dependency of generation of SWD on particular state of vigilance is not
influenced by circadian factors.
SWD are often followed by active and passive wakefulness, this
awaking effect of SWD observed in the present experiment supports
earlier findings (Drinkenburg et al., 1991). Here it is found that the
probability that SWD are followed by slow-wave sleep is higher in
constant dim light than in entrained condition, while the sleep-wake
state preceding the onset of SWD is not changed. This implies that the
awaking properties of SWD are diminished in the constant dim light
condition. The sleep-wake cycle of these epileptic rats has been found
to be disrupted: besides shorter and many aborted REM sleep periods
(Suntsova et al., 2009; Gandolfo et al., 1991), the sleep cycle is
shortened. However, the sleep cycle disturbances are significant only
at a specific time of day, pointing towards a circadian factor in sleep
disturbances (van Luijtelaar and Bikbaev, 2007). It seems that the
tendency of SWD to exert awaking effect is reduced by uncoupling the
SWD and motor activity rhythms and by a decrease in the robustness
of these rhythms.
In conclusion, this study demonstrates that the timing of SWD
in the entrained conditions is due to photic synchronizing stimuli and it
is shaped by the motor activity rhythm and the momentary state of
vigilance. It also confirmed a true circadian nature of the rhythm of
absence
seizures.
Considering
an
opposite
tendency
in
the
characteristics of rhythms under the constant dim light condition, it can
be concluded that the SWD rhythm might be driven by a circadian
oscillator distinct form that of the general motor activity rhythm. The
relationship between states of vigilance and the mechanisms of the
generation of SWD is preserved in the constant dim light condition
suggesting, that this mechanism is independent from circadian
modulation. However, the awaking effect of SWD became weaker. The
uncoupling of SWD and motor activity rhythms during the constant dim
108
light condition allows that the SWD rhythm loses some of its
robustness.
5. Acknowledgements
The study is supported by a fellowship from Radboud University
Nijmegen. The authors would like to thank Gerard van Oijen, Pascal de
Water for technical and Saskia Hermeling and Hans Krijnen for
biotechnical assistance.
6. References
Aguzzi J, Bulloc, N.M., Tosini, G. 2006. Spontaneuos internal desynchronization of
locomotor activity and body temperature rhythms from plasma melatonin
rhythm in rats exposed to constant dim light. J Circadian Rhythms 4, 4-6.
Albers HE, Gerall AA, Axelson JF. 1981. Circadian rhythm dissociation in the rat: effect
of long-term constant illumination. Neurosci Lett 25, 89-94.
Aschoff J. 1960. Exogenous and endogenous components in circadian rhythms. Cold
Spring Harbor. Symp. Quant. Biol. 25, 11-28.
Aschoff J, Wever R. 1981. The circadian system of man. (Eds.), Handbook on
Behavioural Neurobiology. Biological Rhythms. London: Plenum, 311-331.
Borbély AA, Neuhaus HU. 1978. Circadian rhythm of sleep and motor activity in the rat
during skeleton photoperiod, continuous darkness and continuous light. J Comp
Physiol 128, 37-46.
Buijs RM, Kalsbeek A. 2001. Hypothalamic integration of central and peripheral clocks.
Nat Rev Neurosci 2, 521-6.
Bünning E. 1964. The Physiological Clock. Springer Verlag, Berlin.
Büttner D, Wollnik F. 1984. Strain-differentiated circadian and ultradian rhythms in
locomotor activity of the laboratory rat. Behav Genet 14, 2.
Coenen AM, Drinkenburg WH, Peeters BW, Vossen JM, van Luijtlaar EL. 1991. Absence
epilepsy and the level of vigilance in rats of the WAG/Rij strain. Neurosci
Biobehav Rev 15, 259-63.
Coenen AML, van Luijtelaar ELJM. 2003. Genetic animal model for absence epilepsy: a
review of tha WAG/Rij strain of rats. Behav Genet 33, 635-55.
109
Daan S, Pittendrigh CS. 1976. A functional analysis of circadian pacemakers in nocturnal
rodents. III Heavy water and constant light: homeostasis of frequency? J Comp
Physiol 106, 267-290.
Depaulis A, van Luijtelaar ELJM. 2006. Genetic models of absence epilepsy in the rat.
In A. Pitkänen, P.A. Schwartzkroin, & S.L. Moshe (Eds.), Models of Seizures and
Epilepsy, 233-248. Amsterdam: Elsevier Academic Press.
Dinner DS. 2002. Effect of sleep on epilepsy. J Clin Neurophysiol 19, 504-13.
Drinkenburg WH, Coenen AM, Vossen JM, van Luijtelaar EL. 1991. Spike-wave
discharges and sleep-wake states in rats with absence epilepsy. Epilepsy Res.
9, 218-24.
Duncan JS. 1997. Idiopathic generalized epilepsieswith typical absences. J Neurol 244,
403-411.
Durazzo TS, Spencer SS, Duckrow RB, Novotny EJ, Spencer DD, Zaveri HP. 2008.
Temporal distribution of seizure occurrence from various epileptogenic regions.
Neurology 70, 1265-1271.
Eastman C, Rechtschaffen A. 1983. Circadian temperature and wake rhythmsof rats
exposed to prolonged continuous illumination. Physiol Behav 31, 417-427.
Erket HG. 2000. Internal desynchronization of the circadian activity and feeding rhythm
in an owl monkey (Aotus lemurinus griseimembra): a case study. Chronobiol
Int 17, 147-53.
Fukuhara C, Aguzzi J, Bullock N, Tosini G. 2005. Effect of long-term exposure to
constant dim light on the circadian system of rats. Neurosignals 14, 117-125.
Gowers W. 1885. Course of epilepsy. (Eds.), Epilepsy and Other Chronic Convulsive
Diseases: Their Causes, Symptoms and Treatment. New York: William Wood,
157–164.
Halasz P. 1991. Sleep, arousal and electroclinical manifestations of generalized epilepsy
with spike wave pattern. Epilepsy Res. Suppl. 2, 43-8.
Halasz P, Terzano MG, Parrino L. 2002. Spike-wave discharge and the microstructure
of sleep-wake continuum in idiopathic generalized epilepsy. Neurophysiol Clin
32, 38-53.
Herzog ED, Schwartz WJ. 2002. A neuronal clockwork for encoding circadian time. J
Appl Physiol 92, 401-8.
Hofstra WA, Spetgens WPJ, Leijten FSS, van Rijen PC, Gosselaar P, van der Palen J, de
Weerd AW. 2009. Diurnal rhythms in seizures detected by intracranial
electrocorticographic monitoring: An observational study. Epilepsy Behav 14,
617-621.
110
Ikeda M, Inoue S. 1998. Simultaneous recording of circadian rhythms of brain and
intraperitoneal temperatures and locomotor and drinking activities in the rat.
Biol. Rhythm. Res. 29, 142-150.
Ikeda M, Sagara M, Inoue S. 2000. Continuous exposure to dim illumination uncouples
temporal pattern of sleep, body temperature, locomotion and drinking
behaviour in the rat. Neurosi. Lett 279, 185-189.
Jagota A, de la Iglesia HO, Schwartz WJ. 2000. Morning and evening circadian
oscillations in the suprachiasmatic nucleus in vitro. Nat Neurosci 3, 372-376.
Kellaway P, Frost Jr JD, Crawley JW. 1980. Time modulation of spike-and-wave activity
in generalized epilepsy. Ann Neurol 8, 491-500.
Langdon-Down M. Brain W. 1929. Time of day in relation to convulsions in epilepsy.
Lancet 1029-1032.
Lannes B, Micheletti G, Vergnes M, Marescaux Ch, Depaulis A, Warter JM. 1988.
Relationship between spike-wave discharges and vigilance levels in rats with
spontaneous petit mal-like epilepsy. Neurosci. Letters 94, 187-191.
Morin LP, Allen CN. 2006. The circadian visual system. Brain Res Rev 51, 1-60.
Panayiotopoulos CP. 1994. The clinical spectrum of typical absence seizures and
absence epilepsies. (Eds.), Idiopathic generalized epilepsies: clinical,
experimental and genetic aspects. Malafosse, A., Genton, P., Hirsch, E.,
Marescaux, C., Broglin, D., Bernasconi, R. John Libbey & Company Ltd. 75-85.
Pavlova MK, Shea SA, Bromfield EB. 2004. Day/night patterns of focal seizures.
Epilepsy Behav 5, 44-49.
Pittendrigh CS, Daan S. 1976. A functional analysis of circadian pacemakers in
nocturnal rodents. V. Pacemaker structure: a clock for all seasons. J. comp.
Physiol. 106, 333-355.
Quigg M, Straume M, Menaker M, Bertram EH III. 1998. Temporal distribution of partial
seizures: comparison of an animal model with human partial epilepsy. Ann
Neurol 43, 748-755.
Quigg M, Clayburn H, Straume M, Menaker M, Bertram III EH. 2000. Effects of circadian
regulation and rest-activity state on spontaneous seizures in rat model of limbic
epilepsy. Epilepsia 41, 502-509.
Ray B, Mallick HN, Kumar VM. 2004. Changes in thermal preference, sleep-wakefulness,
body temperature and locomotor activity of rats during continuous recording for
24 hours. Behav Brain Res. 154, 519-26.
Redlin U. 2001. Neural basis and biological function of masking by light in mammals:
suppression of melatonin and locomotor activity. Chronobiol Int 18, 737-758.
111
Refinetti R. 2006. Variability of diurnality in laboratory rodents. J Comp Physiol A 192,
701-714.
Reppert SM, Weaver DR. 2001. Molecular analysis of mammalian circadian rhythms.
Annu Rev Physiol 63, 647-76.
Shinohara K, Honma S, Katsuno Y, Abe H, Honma K. 1995. Two distinct oscillators in
the rat suprachiasmatic nucleus in vitro. Proc. Natl. Acad. Sci. 92, 7396-7400.
Suntsova N, Kumar S, Guzman-Marin R, Alam MN, Szymusiak R, McGinty D. 2009. A
role for the preoptic sleep-promoting system in absence epilepsy. Neurobiol Dis
36, 126-141.
van Luijtelaar EL, Coenen AM. 1986. Two types of electrocortical paroxysms in an
inbred strain of rats. Neurosci Lett. 70, 393-397.
van Luijtelaar ELJM, Coenen AML. 1988. Circadian rhythmicity in absence epilepsy in
rats. Epilepsy Res. 2, 331-336.
van Luijtelaar G, Coenen A. 1993. The behavioural pharmacology of sleep. In van
Haaren F. (Ed.), Methods in behavioural pharmacology, 575-602, Amsterdam:
Elsevier Science Publishers B.V.
van Luijtelaar G, Budziszewska B, Jaworska-Feil L, Ellis J, Coenen A, Lasoń W. 2001.
The ovarian hormones and absence epilepsy: a long-term EEG study
and pharmacological effects in a genetic absence epilepsy model. Epilepsy Res.
46, 225-39.
van Luijtelaar G, Bikbaev A. 2007. Midfrequency cortico-thalamic oscillations and the
sleep cycle: genetic, time of day and age effects. Epilepsy Res. 73, 259-265.
van Twyver H. 1969. Sleep patterns of five rodent species. Physiol Behav 4, 901-905.
Wever RA. 1979. The circadian system of man. Springer Verlag 276, New York.
112
CHAPTER 4
Internal desynchronization facilitates seizures
Published as Smyk MK, Coenen A, Lewandowski MH, van Luijtelaar G.
Epilepsia. 2012. 53: 1511-8.
Abstract
Purpose: The occurrence of spike-wave discharges (SWDs) in
WAG/Rij rats is modulated by the circadian timing system and is
shaped by the presence of a light–dark cycle, motor activity, and state
of vigilance. Here it is investigated whether the response to a phase
shift is different between the SWDs and general motor activity rhythm.
The process of re-entrainment of both rhythms and its effect on
number of absences was compared after a phase shift in the light–dark
cycle, a condition known to induce internal desynchronization in the
circadian timing system.
Methods: Chronic electroencephalographic and motor activity
recordings were made in adult WAG/Rij rats, kept in the 12:12 h light–
dark cycle. After four baseline days, rats were exposed to an 8-h phase
delay by shifting the light onset. Recordings were continuously made
for another 10 consecutive days.
Key Findings: An immediate effect of the phase shift on both
rhythms was observed: the acrophases were 7.5 h advanced. Next,
they gradually returned to the baseline level, however, with a different
speed. The more robust motor activity rhythm stabilizes first, whereas
the weaker rhythm of SWDs adapted more slowly. The phase shift
caused a prolonged aggravation of epileptic activity, observed mostly
during the light phase.
Significance: Different speed and character of re-entrainment
suggests that the occurrence of seizures and motor activity are
controlled by distinct circadian oscillators. The prolonged increase in
absences after the phase shift has immediate practical consequences.
Keywords: absence epilepsy; WAG/Rij rats; genetic model;
spike-wave discharges; phase shift; circadian rhythm
115
1. Introduction
The rhythmic expression of physiologic functions is the result of
complex interactions between the external and internal environment
and the circadian timing system. A direct connection with the retina
allows the master circadian pacemaker, the suprachiasmatic nucleus
of the hypothalamus (SCN), to respond to cyclic changes in
environmental illumination, one of the most potent synchronizers of
biological rhythms (Morin & Allen, 2006). The SCN entrained by a lightdark cycle and supported by other time cues, governs dependent clocks
so that a stable phase relationship between various oscillators is
reached (Buijs & Kalsbeek, 2001). However, sudden phase shifts in the
light-dark cycle are able to disturb this synchrony. The speed of
entrainment to a new photoperiod is different for various oscillators; it
is the ventrolateral, photosensitive part of the SCN that synchronizes
first and other follow (Yamazaki et al., 2000; Nagano et al., 2003; Lee
et al., 2009). Internal desynchronization during the process of
reentrainment is also thought to be direct cause of jet lag: a state of
fatigue, poor performance, and sleep disturbances reported by
travelers, who rapidly cross several time zones (Haimov & Arendt,
1999; Waterhouse et al., 2007).
Circadian control over physiologic and behavioral processes in
mammals is well-documented (Bünning, 1964). In addition, some
pathologic phenomena such as epileptic seizures tend to occur daily, in
a nonrandom fashion (Gowers, 1885; Langdon-Down & Brain, 1929;
Quigg et al., 1998). The distribution of seizures across a 24-h day
depends on the type of epileptic syndrome, and can be also influenced
by other circadian variables such as the sleep-wake or rest-activity
cycle, both in humans and animal models (Kellaway et al., 1980; van
Luijtelaar & Coenen, 1988; Coenen et al., 1991; Drinkenburg et al.,
116
1991; Halasz, 1991; Halasz et al., 2002; Pavlova et al, 2004; Durazzo
et al., 2008; Hofstra et al., 2009).
Absence seizures are manifested by the presence of bilateral,
synchronous, generalized spike-wave discharges (SWDs) in the
electroencephalogram (EEG), accompanied by behavioral arrest,
reduced consciousness, and decreased responsiveness (Depaulis & van
Luijtelaar, 2006).
The distribution of SWDs across 24-h period in WAG/Rij rats, a
well-known and validated genetic model of absence epilepsy (Coenen
& van Luijtelaar, 2003; van Luijtelaar & Sitnikova, 2006), is
rhythmically organized, with a maximum at the beginning of the dark
phase and a minimum localized during the first 2 hours of the light
phase (van Luijtelaar & Coenen, 1988; Drinkenburg et al., 1995; Smyk
et al., 2011). It is assumed that at least three main factors contribute
to this structure: photic entrainment, momentary state of vigilance,
and motor activity. Previous findings have demonstrated that the SWD
rhythm is generated endogenously, since it is still present in rats
maintained in a condition of constant dim light, in which the circadian
timing system lacks time cues. In such a condition, the number of
seizures was elevated, however, the relationship between the
occurrence of SWDs and the state of vigilance was not affected. In
addition, desynchronization between the rhythms of SWDs and general
motor activity was observed, which suggests the existence of distinct
oscillators for both rhythms (Smyk et al., 2011).
The present study aimed to investigate whether two oscillators
with different circadian properties also differ in the entrainment to an
8-h phase shift. More specifically, circadian characteristics of SWDs
including number and duration and motor activity rhythm, and their
speed of entrainment were determined before, during and 10 days
after a sudden phase shift, which was induced by extending the dark
period.
117
2. Methods
2.1.
Animals
Eight adult WAG/Rij rats (age 18 months, weight 336 ± 37 g
[mean + SEM]), born and raised in the laboratory of the Department
of Biological Psychology, Donders Centre for Cognition, Radboud
University Nijmegen, were used in the experiment. Animals were
housed in pairs in standard polycarbonate cages and maintained in a
12:12 h light-dark cycle (lights on at 8 a.m.) with food and water
available ad libitum. All experimental procedures were approved by the
Radboud University Nijmegen Animal Ethical Committee.
2.2.
Surgery
Stereotactic surgery was performed under general isoflurane
anesthesia. Each rat was implanted with a standard three-channel
electrode set (MS 333/1-A; Plastic One Inc., Roanoke, VA, U.S.A.). Two
active electrodes were placed on the surface of primary motor and
visual cortex (coordinates with the skull flat and bregma zero-zero: AP
+2.5, ML 3.5 and AP – 6.0, ML 4.0, respectively). The reference
electrode was placed over the cerebellum. Rats were housed
individually during 14 days of a recovery period after the surgery, as
well as throughout the entire experiment.
2.3.
EEG and motor activity recordings
Rats were placed in sound-attenuated, individually ventilated
recording cages and connected with a computer-based data acquisition
system (Dataq Instruments, Akron, OH, U.S.A.), by means of cables
and swivels, which allowed animals to move freely around their cages.
118
The EEG signals (differential recordings) were amplified, band pass
filtered between 1 – 100 Hz, and sampled at 256 Hz. General motor
activity was recorded by a passive infrared sensor (PIR), mounted 40
cm above the bottom of cages and connected to the data acquisition
system. A signal recorded showed high amplitude in response to large
body movements, such as locomotion, food intake, or grooming and
almost zero amplitude during behavioral immobility. Animals had been
habituated to cages and cables 24 h before the experiment started.
2.4.
Experimental design
Baseline EEG and general motor activity recordings were made
for 4 days in the 12:12 h light-dark cycle, with lights on at 8 a.m. and
lights off at 8 p.m. White light with the intensity of 60 lux at the bottom
of animals cage was provided during the light phase, whereas the dark
phase constituted of total darkness. The dark phase of the fifth day
was extended from 12 to 20 h (lights off at 8 p.m., lights on at 4 p.m.,
instead of 8 a.m., next day), in order to produce a light phase delay.
During and after the phase delay, recordings were continuously made
for 10 days in the 12:12 h light-dark cycle, with lights on at 4 p.m. and
lights off at 4 a.m. (postshift days).
2.5.
Data analysis
The analyses were identical as in our previous experiment
(Smyk et al., 2011). In brief, PIR recordings were divided into 6 min.
bins. The mean of an absolute value of the raw signal was calculated
for each bin and expressed subsequently as a percentage of the mean
of all recorded days. A cosine wave with a fixed period length of 24 h
was fitted to each baseline and postshift day (each day = 240 data
points, one point = values for one 6-min bin), to estimate circadian
119
parameters of the rhythm of general motor activity: the mean,
amplitude, acrophase, and robustness (the percentage of the variance
accounted for by the rhythm) (program: Cosinor, available at
http:/www.circadian.org/softwar.html), next to whether a significant
amount of variation over time was obtained. Values obtained for four
baseline days were subsequently averaged.
SWDs were identified offline according to well-known criteria
(van Luijtelaar & Coenen, 1986; Ovchinnikov et al., 2010). The total
number and mean duration of SWDs were calculated for each
experimental day. Circadian parameters of the rhythm of SWDs were
estimated independently from the rhythm of general motor activity,
however, the procedure was the same. A cosine wave with a fixed
period length of 24 h was fitted to each baseline and post-shift day
(each day = 240 data points, one point = the number of SWDs for one
6-min bin). The number, mean duration and circadian parameters
obtained for the baseline days were averaged.
The rate of the entrainment of the rhythms of general motor
activity and SWDs was determined by inspection of individual
acrophase data, and expressed as the number of days after which the
acrophase was the closest to the baseline level.
In addition, the extended dark period (8 h) preceding the light
phase shift was analyzed. Each hour of the extended darkness was
compared with corresponding hours of the beginning of the light and
the dark phase from the baseline. Parameters compared were the
number of SWDs and the level of activity.
2.6.
Statistical analysis
Statistical analysis was performed in SPSS (18th edition; SPSS
Inc., Chicago, IL, U.S.A.). Repeated measures analyses of variance
(ANOVA) were used including orthogonal trend analysis to estimate day
120
effects in circadian parameters, in SWD number and mean duration,
and the level of activity between baseline and postshift days and
between the light and dark period. The same repeated measures was
used to estimate differences in the number of SWDs and level of
activity in the extended dark period, the beginning of the light phase
and the beginning of the dark phase. Least significant difference (LSD)
and Student t-tests were used as post hoc tests to delineate differences
between days and to estimate differences between light and dark
periods and in the rate of the entrainment of the rhythms of general
motor activity and SWDs. Differences were considered to be significant
at p-level < 0.05.
3. Results
3.1.
General motor activity
A clear, 24-h circadian rhythmicity was found in general motor
activity in the baseline condition. Animals were more active during the
dark than during the light phase of the 12:12 h light-dark cycle (149.6
± 8.8 vs. 67.3 ± 3.4 %, respectively, representing the mean level of
activity of all experimental days (MLA) of the dark and light periods, p
< 0.001).
The two-way repeated measures ANOVA for the total activity
score in the dark and light period over all recorded days (baseline and
10 days after the phase shift) showed a significant day (F = 3.21, d.f.
10,70, p < 0.01), a light-dark (F = 249.89 d.f. 1,7, p < 0.0001) and
an interaction effect (F = 23.43, d.f. 10,70, p < 0.001). Post hoc tests
showed that the phase-related level of activity of the baseline was
reversed on the first postshift day, since the rats were now more active
during the light phase (100.6 ± 6.2 % vs. 88.2 ± 8.3 % of the MLA, p
< 0.05). The commonly found activity difference between light and
121
dark was restored on the third postshift day and remained present from
that moment onward (all p < 0.05 or < 0.001).
Comparisons between days showed that the level of nocturnal
activity was reduced for 8 days (days 1 - 6, 8: all p < 0.001, day 7: p
< 0.05), whereas the level of diurnal activity was elevated for 4 days
(day 1, 2, 4: all p < 0.001, day 3: p < 0.05).
Circadian parameters of the rhythm of general motor activity
were also changed over time (F = 60.66, d.f. 10, 70, p < 0.001). The
acrophase of the rhythm was advanced immediately after the phase
shift (from 259.9 ± 8.7 to 157.0 ± 10.8o, p < 0.01). The return to the
baseline level took 6 days (days 1 - 5: all p < 0.01, day 6: p < 0.05).
Both the mean and the amplitude of the rhythm changed over time (F
= 2.93, d.f. 10, 70, p < 0.01 and F = 2.51, d.f. 10, 70, p < 0.05,
respectively). The mean decreased from the first till the eight postshift
day (day 1-3, 5: all p < 0.01, day 6 and 8: all p < 0.05) with an
exception of the fourth and seventh day; on these days the effects
were in the same direction but failed to reach significance. The
amplitude was decreased on the first, second and fourth postshift day
(58.4 ± 6.6 vs. 43.1 ± 7.1, 46.0 ± 5.7 and 46.7 ± 9.5, respectively,
day 1: p < 0.01, day 2, 4: all p < 0.05). A slight decrease over days
was observed for the robustness of the rhythm (F = 2.21, d.f. 10, 70,
p < 0.05); the post hoc tests showed a lower robustness on the first
and fourth post-shift day (all p < 0.05). General motor activity data
are presented in Fig. 1.
122
Figure 1. Mean and SEM of the level of general motor activity in the light and
dark phase (A), the mean (B) and the acrophase (C) of the rhythm of general
motor activity. Y axis: (A and B) percentage of the mean level of activity of all
days recorded (MLA), degrees (360 degrees = 24 h, 0 – 180 degrees – the
light phase, 181 – 360 degrees – the dark phase); X axis: experimental days
divided into baseline period (4 days) and postshift period (10 days), day on
123
which the phase shift was produced is not included. * p < 0.05, LSD post hoc
test after ANOVA, mean baseline level versus postshift day
3.2.
Spike-wave discharges
A 24-h rhythm in the occurrence of SWDs was found in the
baseline condition. The minimum of the rhythm was localized at the
beginning of the light phase, whereas the acrophase occurred during
early hours of the dark phase. A mean number of SWDs 463.2 ± 113.2,
lasting on average 6.6 ± 1.1 s, was recorded. SWDs were more
numerous during the dark than the light phase of the 12:12 h lightdark cycle (296.2 ± 92.9 vs. 186.6 ± 53.9, p < 0.01).
The two-way repeated measures ANOVA on number of SWDs of
all recording days showed a day effect that could be described by a
quadratic trend (Fquad = 8.72, d.f. 1,7, p < 0.01) and a day x time of
day interaction (F = 5.09, d.f .10,70, p < 0.001). Subsequent one-way
ANOVAs for the light and dark period separately, showed a day effect
(F = 4.92, d.f. 10, 70, p < 0.001). The number of seizures was elevated
after the phase shift, starting from the first till the seventh postshift
day (days 1, 4, 6: all p < 0.05, day 2, 3, 5, 7: all p < 0.01). The oneway ANOVA on the number of SWDs in the dark period showed a day
effect (F = 2.27, d.f. 10, 70,
p < 0.05), which could be further
described by the presence of a linear rising trend (Flin = 10.38, d.f. 1,
7, p < 0.05). The number of SWDs on the first postshift day in the dark
phase tended to be decreased, but failed to reach significance (242.6
± 96.8 vs. 296.2 ± 92.9). The SWD data clearly demonstrate that the
overall increase in the number of seizures occurred in the light phase.
The phase shift induced also a phase reversal between the light
and dark period in the number of SWDs. Differences in SWDs between
the light and dark period, present during the baseline day (t = 2.66,
d.f. 7, p < 0.05) were only restored on the eighth, (t = 2.39, p < 0.05),
124
ninth (t = 3.39, p < 0.01) and tenth (t = 1.97, p < 0.10, all d.f. = 7)
postshift days.
The acrophase of the rhythm of SWDs was significantly affected
by the phase shift. An immediate advance was observed on the first
postshift day (221.8 ± 31.6 vs. 108.6 ± 30.2o, p < 0.001). A significant
time effect was found (F=8.34, d.f. 10, 70, p<.001). The return to the
baseline level took 3 days (day 2 and 3, all p < 0.001), however, a
significant difference was also observed on the seventh postshift day
(p < 0.001). SWDs data are presented in Figure 2.
The other circadian parameters were not significantly changed.
Orthogonal trend analyses showed a significant quadratic trend for the
mean (F = 8.89, d.f. 1, 7, p < 0.05) and amplitude (F = 8.60, d.f. 1,
7, p < 0.05) of the SWD rhythm, suggesting that the initial increase
was followed by a decrease. Subsequent post hoc tests failed to find
clear differences between days. This might be due to the fact that in 4
of 8 animals the procedure of fitting the 24-h cosine line to the data
set from some of the postshift days, mostly from the early period after
the phase shift, failed to reach significance. This implies that the
temporal organization of the SWD rhythm, as found during the baseline
period, was less obvious after the phase delay.
The mean duration of SWDs was also affected by the phase
shift: a significant day effect was found (F = 3.13, d.f. 10,70, p <
0.01); post hoc tests showed that the SWDs lasted longer on postshift
days 1, 2 and 4 and 10 compared to base-line (all p < 0.05).
125
Figure 2. Mean and SEM of the number of SWDs in the light and dark phase
(A), the mean duration of SWDs (B), the acrophase of the SWD rhythm (C)
and the rate of entrainment of the rhythms of general motor activity and SWDs
(D). Y axis: (A) number of SWDs, (B) seconds, (C) degrees (360 degrees =
1h, 0 – 180 degrees – the light phase, 181 – 360 degrees – the dark phase),
(D) day; X axis: (A,B and C) experimental days divided into baseline period (4
days) and postshift period (10 days), day on which the phase shift was
produced is not included. * p < 0.05, LSD post hoc tests after ANOVA, mean
baseline level versus postshift day (A, B and C), * p < 0.05, Student’s t-test
(D)
126
3.2.1.
Spike-wave discharges
The same ANOVA on the number of SWDs showed a significant
period effect (F = 13.74, d.f. = 2, 14, p < 0.001). Post hoc tests
showed that there were no significant differences between the
extended dark and the light phase of the baseline condition; however,
the rats had less (all p < 0.05) SWDs during the extended dark phase
and the light phase of the baseline condition compared to the dark
phase of the baseline condition.
4. Discussion
The process of re-entrainment to an 8-h light phase delay was
investigated in a genetic animal model of absence epilepsy. Circadian
parameters of the rhythm of SWDs and the rhythm of general motor
activity, as well as the number of SWDs and the level of activity, were
compared before, during and after the time shift. The most prominent
changes were an advance of the acrophase of both rhythms, followed
by a gradual synchronization to the new photoperiod, as well as an
aggravation
of
epileptic activity. The speed
and
character
of
entrainment of the investigated rhythms were found to be different.
General motor activity entrained faster and steadily, while the rhythm
of SWDs, temporarily synchronized after the third postshift day,
completed the process significantly later.
The effect of an abrupt shift of the light-dark cycle on the
circadian rhythm of motor activity is well established. The magnitude
of changes caused by the shift depends on its amount and direction
(Wever, 1966). Generally, disturbances caused by phase delays are
managed faster than those caused by phase advances. The process of
reentrainment after a 6-, 8- or 10-h phase delay took 2, 4 or 6 days,
respectively, in albino strains of rats (Takamure et al. 1991, Marumoto
127
et al, 1996; Yamazaki et al., 2000; Nagano et al., 2003). In the present
study, the acrophase of general motor activity of the rats reached the
baseline level on the sixth postshift day, which, although 2 days longer
and perhaps due to the complex phenotype (epilepsy and comorbidity
of depression), remains in accordance with the above-cited studies.
Interestingly, there were no differences in the speed of entrainment or
any other circadian parameters between rats with the highest and
lowest number of absences.
Apart from the timing of the acrophase, also the phase-related
distribution of motor activity was changed. For 8 days after the phase
shift, nocturnal activity of WAG/Rij rats was decreased, whereas the
level of daytime activity was elevated for 4 days. Similar results were
obtained in mice after a light-dark cycle reversal: nocturnal activity
was decreased 4 - 8 days after the shift, whereas a significant
difference between nocturnal and diurnal activity was reestablished on
days 4 - 7 dependent on the strain (Kopp et al. 1998). In addition,
Wistar rats subjected to a 10-h phase delay showed an increased
daytime and reduced nocturnal activity. The phase-related distribution
of activity reached the baseline level on sixth day after the shift
(Nagano
2003).
Despite
small
differences,
the
process
of
synchronization of the general motor activity to the shifted photoperiod
in WAG/Rij rats shares characteristics in common with other strains of
nocturnal rodents.
The entrainment of the circadian rhythm of absence seizures to
a phase shift in the light-dark cycle was not previously investigated,
neither in man nor in rats. During the baseline condition, a clear 24-h
rhythm of SWDs was observed in all WAG/Rij rats. The shape of the
rhythm (localization of minimum and maximum, phase related number
of SWDs), together with its circadian parameters, confirmed previous
findings (van Luijtelaar & Coenen, 1988; Smyk et al., 2011). A
response to an 8-h phase delay shared some common features with
128
the process of entrainment of the general motor activity rhythm. In
both rhythms, an advance of the acrophase and a redistribution of the
phase-related motor and epileptic activity took place. However, the
speed and the nature of the entrainment of the motor and SWD rhythm
were rather different.
The acrophase of the rhythm of SWD was immediately advanced
on the first postshift day and was localized in the middle of the light
phase, instead of the beginning of the dark phase. The entrainment to
the
new
photoperiod
took
8
days,
during
which
a
transient
synchronization on the fourth postshift day was observed, followed by
a subsequent shift from the baseline level, and finally, by a completion
of the process. Therefore, the rate of the entrainment and the course
of synchronization were different between the rhythms of SWDs and
general motor activity. In our previous study performed in constant
condition, an internal desynchronization between these two rhythms
was observed, suggesting distinct mechanisms responsible for their
entrainment
(Smyk
et
al.,
2011).
The
phenomenon
of
desynchronization between circadian oscillators and rhythms after the
phase shift has also been described by others. The process of
reentrainment of central and peripheral oscillators in transgenic rats
was completed at first in the SCN, and then in peripheral tissues
(Yamazaki et al., 2000). The SCN of the rat, known to consist of the
photosensitive ventrolateral and nonphotosensitive dorsomedial part,
followed the transient internal desynchronization as a result of the
phase shift (Nagano et al., 2003; Lee et al., 2009). The ventrolateral
SCN shifted immediately, whereas the dorsomedial SCN required 6
days to reach the new phase. Differences in the rate of entrainment
within these two parts of the SCN are reflected in desynchronization
between the rhythms of slow-wave sleep and REM sleep controlled by
these morphologically and functionally distinct parts. The proper phase
angle between these two rhythms of sleep stages is restored on 3 - 5
129
days after the shift (Lee et al., 2009). Different rate of entrainment has
been reported also for the rhythms of core body temperature and
locomotor activity in mice subjected to an 8-h phase advance (Satoh
et al., 2006). The results obtained in the present study support the
hypothesis about independent slave oscillators for the rhythms of
SWDs and motor activity (Smyk et al., 2011). Both rhythms are
controlled by and synchronized to the master circadian pacemaker
under the 12:12 h light-dark cycle that controls the rhythms, whereas
they act independently to reestablish a proper phase relationship with
the SCN and the Zeitgebers after the phase shift.
It was previously observed that these two rhythms/oscillators
differ in their robustness, both, in the entrained and in the constant
condition (Smyk et al., 2011). The rhythm of general motor activity
was found to be more robust than the SWD rhythm. These differences
might contribute to the different course of the entrainment to the phase
shift observed in the present study. It is proposed that a strong, robust
rhythm of motor activity synchronized faster and steadily, whereas the
weaker rhythm of SWDs entrained slower. Moreover, the phase shift
itself was found to disrupt the rhythmic occurrence of seizures as some
animals lost their rhythmicity in SWDs during the early stages of
entrainment, similarly as this was reported for peripheral oscillators
(Yamazaki et al., 2000). In the present study, in contrast to the
outcomes of the analysis of SWDs, a non significant circadian pattern
for motor activity was never observed at any of the stages of
entrainment.
After 8 hr light phase delay, epileptic activity, measured by the
number and mean duration of SWDs, was found to be elevated. The
prolonged increase in the number of seizures was accompanied by a
redistribution of the phase-related number of SWDs, a phenomenon
described above for the rhythm of general motor activity. However,
contrary to motor activity, the phase inversion lasted longer, whereas
130
the significant difference between phases was reestablished later. The
steady increase in SWDs was also found when rats were kept in
constant dim light conditions compared to the baseline 12:12 h lightdark cycle (Smyk et al. 2011). This increase was partially attributed to
a declined motor activity, since SWDs are most numerous during
immobile behavior (Drinkenburg et al., 1991). In the present study,
the increase of the number of SWD was seen mainly in the light phase.
At the same time, the diurnal level of motor activity was above baseline
level, which suggests that other factors contribute to this phenomenon,
since high levels of locomotor activity are incompatible with the
occurrence of absence seizures. Melatonin and corticosterone are
endogenous substances known to affect SWDs (Sandyk, 1992;
Kldiashvili et al., 2001; Schridde & van Luijtelaar, 2004). Both
conditions: constant dim light and phase shift, are known to alter
circadian rhythms and the level of their secretion. The melatonin
rhythm in the rat is disrupted or diminished in constant dim light and
also during a few days after the phase shift (Humlova & Illnerova,
1990; Fukuhara et al., 2005; Liu & Borjigin, 2005; Aguzzi et al., 2006).
In addition, the level of corticosterone in the rat is elevated and the
circadian rhythm of its secretion is changed after an experimentally
induced let lag (Mohawk et al., 2007). Melatonin and corticosterone
have been reported to exert opposite effect on absence seizures in
WAG/Rij rats: attenuating and aggravating, respectively. However, the
role of these hormones in the process of entrainment of the SWD
rhythm to the new photoperiod is not known and should be clarified in
future studies.
The analysis of eight additional hours of darkness preceding the
phase shift, revealed no differences in the level of activity and the
number of SWDs between this period and the corresponding hours of
the light phase. Regardless of the photic information, rats showed
passive behavior. The phase shift day consisted of 32 hours, 12 light
131
and 20 dark hours, which is far outside the range of entrainment for
the rat (Stephan, 1983; Madrid et al., 1998). When the limit is
exceeded, the circadian timing system cannot synchronize to the
Zeitgeber and starts free running (Bünning, 1964; Herzog & Schwartz,
2002). Diurnal behavior in both motor and epileptic activity, during the
extended dark period, is a sign of a free-running state of the circadian
timing system of WAG/Rij rats. After an artificially long, 32-h day, the
12:12 h light-dark cycle has been regularly imposed on the rats till the
end of the experiment. Because the light-dark cycle is one of the
strongest
Zeitgeber,
rats
gradually
synchronize
to
the
new
photoperiod.
In conclusion, the present study demonstrates that the
circadian rhythms of SWDs and general motor activity are controlled
independently by distinct slave oscillators. Rhythms are under the
control of the master circadian pacemaker (SCN) and are coupled to
each other in the entrained condition. The phase shift induces internal
desynchronization
and
the
rhythms
show
different
rates
of
entrainment. The strong robust motor activity rhythm stabilizes first,
while the weaker rhythm of SWD adapts more slowly. Internal
desynchronization is thought to be responsible for complaints
associated with jet lag. Here it is shown that absences increase
especially during the day as a consequence to an experimental
manipulation resembling jet lag. This finding might be important for
people with epilepsy, undergoing rapid time shifts.
5. Acknowledgments
The study is supported by a fellowship from Radboud University
Nijmegen. The authors would like to thank Gerard van Oijen for
technical
and Saskia Menting-Hermeling and Hans Krijnen for
biotechnical assistance.
132
6. Disclosure
None of the authors has any conflict of interest to disclose. We
confirm that we have read the Journal’s position on issues involved in
ethical publication and affirm that this report is consistent with those
guidelines.
7. References
Aguzzi J, Bulloc NM, Tosini G (2006) Spontaneuos internal desynchronization of
locomotor activity and body temperature rhythms from plasma melatonin
rhythm in rats exposed to constant dim light. J Circadian Rhythms 4: 4-6.
Buijs RM, Kalsbeek A (2001) Hypothalamic integration of central and peripheral clocks.
Nat Rev Neurosci. 2: 521-526.
Bünning E (1964) The Physiological Clock. Springer Verlag, Berlin.
Coenen AM, Drinkenburg WH, Peeters BW, Vossen JM, van Luijtlaar EL (1991) Absence
epilepsy and the level of vigilance in rats of the WAG/Rij strain. Neurosci.
Biobehav. Rev. 15: 259-263.
Coenen AML, van Luijtelaar ELJM (2003) Genetic animal model for absence epilepsy: a
review of tha WAG/Rij strain of rats. Behav. Genet. 33: 635-655.
Depaulis A, van Luijtelaar ELJM (2006) Genetic models of absence epilepsy in the rat.
In Pitkänen A, Schwartzkroin PA, Moshe SL (eds) Models of Seizures and
Epilepsy, Elsevier Academic Press, San Diego, pp. 233-248.
Drinkenburg WH, Coenen AM, Vossen JM, van Luijtelaar EL (1991) Spike-wave
discharges and sleep-wake states in rats with absence epilepsy. Epilepsy Res.
9: 218-224.
Drinkenburg WH, Coenen AM, Vossen JM, van Luijtelaar EL (1995) Sleep deprivation
and spike-wave discharges in epileptic rats. Sleep 18: 252-256.
Durazzo TS, Spencer SS, Duckrow RB, Novotny EJ, Spencer DD, Zaveri HP (2008)
Temporal distribution of seizure occurrence from various epileptogenic regions.
Neurology 70: 1265-1271.
Fukuhara C, Aguzzi J, Bullock N, Tosini G (2005) Effect of long-term exposure to
constant dim light on the circadian system of rats. Neurosignals 14: 117-125.
133
Gowers W (1885) Course of epilepsy. In Gowers W (eds) Epilepsy and Other Chronic
Convulsive Diseases: Their Causes, Symptoms and Treatment, William Wood,
New York, pp.157–164.
Haimov I, Arendt J (1999) The prevention and treatment of jet lag. Sleep Med Rev. 3:
229-240.
Halasz P (1991) Sleep, arousal and electroclinical manifestations of generalized epilepsy
with spike wave pattern. Epilepsy Res Suppl. 2: 43-48.
Halasz P, Terzano MG, Parrino L (2002) Spike-wave discharge and the microstructure
of sleep-wake continuum in idiopathic generalized epilepsy. Neurophysiol Clin.
32: 38-53.
Herzog ED, Schwartz WJ (2002) A neuronal clockwork for encoding circadian time. J
Appl Physiol. 92: 401-498.
Hofstra WA, Spetgens WPJ, Leijten FSS, van Rijen PC, Gosselaar P, van der Palen J, de
Weerd AW (2009) Diurnal rhythms in seizures detected by intracranial
electrocorticographic monitoring: An observational study. Epilepsy Behav. 14:
617-621.
Humlova M, Illnerova H (1990) Rate of re-entrainment of circadian rhythms to advances
of light-dark cycles may depend on ways of shifting the cycles. Brain Res. 531:
304-306.
Kellaway P, Frost Jr JD, Crawley JW (1980) Time modulation of spike-and-wave activity
in generalized epilepsy. Ann Neurol. 8: 491-500.
Kldiashvili S, Melechov M, Morenkov E, Coenen AML, van Luijtelaar G, Chepurnov SA
(2001) Effects of the sleep-wake rhythm modulating hormone melatonin on
absence seizures in the EEG of rats. In van Bemmel AL, Beersma DGM, Hofman
WF, Ruigt GSF, Vos PJE (eds) Sleep-wake research in the Netherlands, Vol 12,
Dutch Society for Sleep-Wake Research, Leiden, pp. 58-61.
Kopp C, Vogel E, Rettori MC, Delagrange P, Guardiola-Lemaître B, Misslin R (1998)
Effects of a daylight cycle reversal on locomotor activity in several inbred strains
of mice. Physiol Behav. 63: 577-585.
Langdon-Down M, Brain W (1929) Time of day in relation to convulsions in epilepsy.
Lancet 1: 1029-1032.
Lee ML, Swanson BE, de la Iglesia HO (2009) Circadian timing of REM sleep is coupled
to an oscillator within the dorsomedial suprachiasmatic nucleus. Curr Biol. 19:
848-852.
Liu T, Borjigin J (2005) Reentrainment of the circadian pacemaker through three distinct
stages. J Biol Rhythms. 20: 441-450.
134
Madrid JA, Sánchez-Vázquez FJ, Lax P, Matas P, Cuenca EM, Zamora S (1998) Feeding
behavior and entrainment limits in the circadian system of the rat. Am J Physiol.
275: 372-383.
Marumoto N, Murakami N, Kuroda H, Murakami T (1996) Melatonin accelerates
reentrainment of circadian locomotor activity rhythms to new light-dark cycles
in the rat. Jpn J Physiol. 46: 347-51.
Mohawk JA, Pargament JM, Lee TM (2007) Circadian dependence of corticosterone
release to light exposure in the rat. Physiol Behav. 92: 800-806.
Morin LP, Allen CN (2006) The circadian visual system. Brain Res Rev. 51: 1-60.
Nagano M, Adachi A, Nakahama K, Nakamura T, Tamada M, Meyer-Bernstein E, Sehgal
A, Shigeyoshi Y (2003) An abrupt shift in the day/night cycle causes
desynchrony in the mammalian circadian centre. J Neurosci. 23: 6141-6151.
Ovchinnikov A, Lüttjohann A, Hramov A, van Luijtelaar G (2010) An algorithm for realtime detection of spike-wave discharges in rodents. J Neurosci Methods. 194:
172-178.
Pavlova MK, Shea SA, Bromfield EB (2004) Day/night patterns of focal seizures.
Epilepsy Behav. 5: 44-49.
Quigg M, Straume M, Menaker M, Bertram EH III (1998) Temporal distribution of partial
seizures: comparison of an animal model with human partial epilepsy. Ann
Neurol. 43: 748-755.
Sandyk R (1992) Melatonin and petit-mal epilepsy: an hypothesis. Int J Neurosci. 65:
83-90.
Satoh Y, Kawai H, Kudo N, Kawashima Y, Mitsumoto A (2006) Temperature rhythm
reentrains faster than locomotor rhythm after a light phase shift. Physiol Behav.
88: 404-10.
Schridde U, van Luijtelaar G (2004) Corticosterone increases spike-wave discharges in
a dose- and time-dependent manner in WAG/Rij rats. Pharmacol Biochem
Behav. 78: 369–375.
Smyk MK, Coenen AM, Lewandowski MH, van Luijtelaar G (2011) Endogenous rhythm
of absence epilepsy: relationship with general motor activity and sleep-wake
states. Epilepsy Res. 93: 120-127.
Stephan FK (1983) Circadian rhythms in the rat: constant darkness, entrainment to T
cycles and to skeleton photoperiods. Physiol Behav. 30: 451-462.
Takamure M, Murakami N, Takahashi K, Kuroda H, Etoh T (1991) Rapid reentrainment
of the circadian clock itself, but not the measurable activity rhythms to a new
light-dark cycle in the rat. Physiol Behav. 50: 443-449.
135
van Luijtelaar EL, Coenen AM (1986) Two types of electrocortical paroxysms in an
inbred strain of rats. Neurosci Lett. 70: 393-397.
van Luijtelaar ELJM, Coenen AML (1988) Circadian rhythmicity in absence epilepsy in
rats. Epilepsy Res. 2: 331-336.
van Luijtelaar G, Sitnikova E (2006) Global and focal aspects of absence epilepsy: the
contribution of genetic models. Neurosci Biobehav Rev. 30: 983- 1003.
Waterhouse J, Reilly T, Atkinson G, Edwards B (2007) Jet lag: trends and coping
strategies. Lancet 369: 1117-1129.
Wever R (1966) The duration of re-entrainment of circadian rhythms after phase shifts
of the Zeitgeber. A theoretical investigation. J Theor Biol. 13: 187-201.
Yamazaki S, Numano R, Abe M, Hida A, Takahashi R, Ueda M, Block GD, Sakaki Y,
Menaker M, Tei H (2000) Resetting central and peripheral circadian oscillators
in transgenic rats. Science 288: 682-685.
136
CHAPTER 5
Re-entrainment of sleep-wake states and absence
seizures after a phase delay
Abstract
Rapid changes in photoperiod lead to desynchonization of
circadian rhythms, its consequences require a considerable amount of
time to overcome. Timed administration of melatonin or its agonists
was found beneficial in accelerating re-synchronization. The aim of the
present study was to investigate whether and how the various sleepwake states and absence seizures re-adapt to an 8h phase delay and
whether the melatonin agonist agomelatine affects the speed of reentrainment of the different sleep-wake states.
Two experiments were conducted: an acute pharmacological
study towards the effects of various doses of agomelatine on sleepwake states and absence seizures. Next, chronic administration was
investigated following an 8 h phase delay. Both experiments were done
in symptomatic male WAG/Rij rats, a strain endowed with hundreds
spike-wave
discharges
electroencephalographic
(SWDs)
(EEG)
and
daily.
Simultaneous
electromyographic
(EMG)
recordings were made during 3 days in the acute and 11 days in the
chronic study.
Agomelatine
showed
neither
an
effect
on
sleep-wake
parameters in the acute study, nor affected the SWDs and reentrainment process in the chronic study. Sleep-wake and SWDs
rhythms were advanced immediately as a result of the 8 h delay of the
light phase. The magnitude of an advance and the speed of subsequent
re-entrainment were different for various rhythms. Coupling between
active wakefulness and deep slow-wave sleep, as well as, SWDs and
light slow-wave sleep was observed. A post-shift increase in passive
wakefulness and a reduction in deep slow-wave sleep resulted in an
aggravation of epileptic activity during the light phase.
Different speed of re-entrainment and coupling between various
rhythms suggest that SWDs and light slow-wave sleep are controlled
139
by common circadian mechanism distinct from that for active
wakefulness and deep slow-wave sleep. The increase in the number of
seizures after the phase shift may be of significant importance for
people with epilepsy planning long trans-meridian flight.
Keywords: absence epilepsy, WAG/Rij rats, sleep-wake states,
circadian desynchronization, phase delay
1. Introduction
Rhythmic
occurrence
of
mammalian
physiological
and
behavioral processes results from daily entrainment of the circadian
timing system to environmental changes (Zeitgebers) (Pittendrigh,
1981). True entrainment, a stable phase relationship between the
rhythm and the Zeitgeber cycle, is crucial for organism’s adaptation,
survival and well-being (Dodd et al., 2005). Misalignments caused by
rapid shifts in the timing of the light-dark cycle, lead to circadian
desynchronization, negative consequences of which were described in
people crossing more than two time zones with a jet, shift workers and
also in animal models (Arendt and Marks, 1982; Winget at al., 1984;
Lancel and Kerkhof, 1989; Knutsson, 2013 ). Re-synchronization after
the shift needs considerable amount of time, different for various
functions and rhythms (Yamazaki et al., 2000; Lemmer et al., 2002;
Nagano et al., 2003). One of the most popular coping strategies for
circadian disturbances is the usage of exogenous melatonin; it has
been found beneficial in accelerating a re-entrainment process and
therefore ameliorating symptoms of jet lag syndrome both in human
and animals (Illnerova et al., 1989; Golombek and Cardinali, 1993;
Sharma et al., 1999).
The consequences of internal desynchronization caused by the
transfer to constant dim light condition and by an 8 h phase delay in
140
the light-dark cycle in WAG/Rij rats, a well-known, validated, genetic
animal model of childhood absence epilepsy (CAE) (Depaulis and van
Luijtelaar, 2006) have been described. The occurrence of absence
seizures, marked by the presence of short-lasting, bilateral and
synchronous
spike-wave
discharges
(SWDs)
in
the
electroencephalogram (EEG), is organized into a 24 h rhythm with a
maximum taking place during the dark phase and minimum occurring
at the beginning of the light phase of 12:12 light-dark cycle (van
Luijtelaar and Coenen, 1988). In the absence of entrainment (constant
condition of dim light and the phase-shift mentioned above), the
rhythm of SWDs was found to desynchronize from the rhythm of motor
activity, to which it was tightly coupled under standard laboratory
conditions (Smyk et al. 2011; Smyk et al. 2012). Opposite to a gradual,
stable re-entrainment of the motor activity to the shifted photoperiod
which was completed after 6 days, re-synchronization of the rhythm of
SWDs was longer and irregular. Moreover in both conditions (dim light
and after the phase shift), the lack of entrainment resulted in an
enhancement of epileptic activity (Smyk et al. 2011; Smyk et al. 2012).
Considering a close relationship between the occurrence of absence
seizures and sleep-wake states (Drinkenburg et al., 1991), we aimed
to establish to which sleep-wake states rhythm, if any, SWDs is coupled
during re-synchronization process.
Agomelatine
is
a
newly
introduced
antidepressant
with
chronobiotic properties. This melatonin naphthalene analogue is a
potent agonist of melatonin receptors subtypes 1 and 2 (MT1, MT2)
and antagonist of serotonin receptor subtype 2 (5-HT2C) (de Bodinat
et al., 2010). Antidepressant action of agomelatine has been shown
both in animal studies and clinical trials (Loo et al., 2002; Papp et al.,
2003; Bourin et al., 2004; Bertaina-Anglade et al., 2006; Goodwin et
al., 2009). Chronobiotic properties of the drug have been reported in
rats kept in constant darkness, in which activity rhythm entrained to
141
daily administration of agomelatine coinciding with the activity onset
(Martinet et al., 1996). Phase-advancing effect of agomelatine, similar
to this obtained for melatonin, in an 8 h phase advance paradigm
resembling jet-lag were described (Redman et al., 1995). The
compound reinstates circadian rhythms in aged rodents and normalizes
disrupted sleep-wake cycle in trypanosome-infected rats (Koster-van
Hoffer et al., 1993; Grassi-Zucconi et al., 1996; van Reeth et al.,
2001). The second goal of the present study was to investigate an
effect of chronic administration of various doses of agomelatine on the
course of re-entrainment of the rhythms of SWDs and sleep-wake
states to the 8 h phase delay. Melatonin affects epileptic activity in
various seizure and epilepsy models (Lapin et al., 1998; Musshoff and
Speckmann, 2003; Yildirim and Marangoz, 2006); also agomelatine
showed anticonvulsant activity in PTZ induced seizures and in the
pilocarpine induced status epilepticus model of temporal lobe epilepsy
(Aguiar et al. 2012). Melatonin and agomelatine may alter sleep-wake
architecture in the rat (Fisher et al., 2008; Descamps et al., 2009).
Considering
the
potential
hypnotic
and
antiepileptic
effect
of
agomelatine, our chronic experiment was preceded by an acute study,
which allowed to entangle the putative effects of agomelatine on sleep
and epilepsy and the effects of the light phase delay on re-entrainment.
Here the effect of a single administration of various doses of
agomelatine on the number and mean duration of SWDs, on sleepwake states and circadian parameters of the rhythms of SWDs and
sleep-wake states was investigated.
142
2. Materials and methods
2.1.
Animals
32 male, adult (8 months of age) WAG/Rij rats purchased from
Harlan (Harlan, the Netherlands), 315 g ± 12g of weight, were used.
Animals were singly-housed in individually ventilated Plexiglas cages
(25 x 22 x 18 cm) in a sound-attenuated room and controlled
environmental conditions (temperature: 22 ± 2 C, humidity: 60%,
12:12 light-dark cycle, white lights on at 5 am off at 5 pm , light
intensity: ≈ 100 lux) throughout the experiment as well as during
recovery period after the surgery. The exception was the shifted LD
cycle after the phase delay (see chronic study), then the lights were
on 1 pm and off at 1 am. Standard laboratory chow and tap water were
available ad libitum. All experimental procedures were approved by the
animal use and care committee of Janssen Research and Development,
Pharmaceutical Companies of Johnson & Johnson and the local ethical
committee.
2.2.
Surgery
Animals underwent stereotactic surgery under deep isoflurane
anesthesia. Seven stainless steel fixing screws (diameter 1 mm) were
inserted bilaterally in the left and right hemisphere along the anteroposterior axes in the following coordinates: AP +2 mm, L +/– 2 mm;
AP -2 mm, L +/-2 mm and AP -6.6 mm , L +/- 2 mm from Bregma;
and referenced to the same ground electrode place midline above of
the cerebellum. The incisor bar was –5 mm under the center of the ear
bar, according to the stereotactic atlas of Paxinos and Watson. In
addition, stainless steel wires electrodes were placed in the muscles of
the neck to record EMG activity. Electrodes (stainless steel wire,
143
7N51465T5TLT, 51/46 Teflon Bilaney, Germany) were connected to a
pin (Future Electronics: 0672-2-15-15-30-27-10-0) with a small insert
(track pins; Dataflex: TRP-1558-0000) and were fit into a 10-holes
connector then the whole assembly was fixed with dental cement to
the cranium. Rats were allowed to recover for 10 days after surgery.
2.3.
EEG and EMG recordings
Rats, while staying in their home cages, were placed in soundattenuated, individually ventilated recording boxes and connected by a
cable to rotating swivels, allowing free movement during recording
procedures. Rats were allowed to habituate to the recording set-up for
24hr preceding experiments. Continuous EEG and EMG signals were
acquired at 2 kHz sample rate with an input range of +/- 500 mV
through a Biosemi ActiveTwo system (Biosemi, Amsterdam, the
Netherlands) referenced to the CMS-DRL ground (common mode
reference for online data acquisition and impedance measures, which
is a feedback loop driving the average potential across the montage
close to the amplifier zero). The signals were amplified and analogue
band-pass filtered between 1 and 100 Hz and were digitized with 24
bit resolution.
2.4.
Pharmacological treatment
Saline, agomelatine (Sigma-Aldrich, Belgium) in doses of 2.5, 5
and 10 mg/kg dissolved in 20% cyclodextrin (Sigma-Aldrich, Belgium)
and 20% cyclodextrin alone were administrated orally 1 h before the
onset of darkness.
144
2.5.
Experimental design
The acute study consisted of 3 experimental days. Each day
consisted of 24 h of simultaneous and continuous EEG and EMG
recordings. Recordings started at the last hour of the light phase during
which injections or sham administration took place, then they were
continued by 12 h of the dark phase and 11 h of the light phase.
Animals were randomly assigned to 4 groups (n=8 rats each): control
group, 2.5, 5 and 10 mg/kg group of agomelatine, respectively. On the
first, baseline day, all rats received saline. On the second, treatment
day, animals received agomelatine in 3 doses: 2.5, 5 and 10 mg/kg,
rats of the control group were injected with vehicle. On the third, posttreatment day, oral administration of a compound was mimicked by
picking up animals from their cages without usage of feeding tubes and
pharmaceutical agents.
The chronic study consisted of 11 experimental days and 1
period of extended darkness. Recording schedule was the same as in
the acute study. Again, rats were randomly assigned to 4 groups (n=8
rats each): control group, 2.5, 5 and 10 mg/kg of agomelatine,
respectively. On the first baseline day, all animals received saline (oral
administration) 1 h before the onset of darkness. After that day, an 8
h phase shift in the light-dark cycle was introduced by extending the
subsequent dark period from 12 to 20 hr. Recordings were continued
for 10 consecutive, post-shift days starting from the last hour of the
light phase of the shifted photoperiod. Every day, at the last hour of
the light phase (1 h before the onset of darkness), animals were given
agomelatine, while animals of the control group received vehicle. Due
to a crash of the recording system, the data of the second post-shift
day were lost and are missing from the analysis.
145
2.6.
Data analysis
Five sleep-wake states were automatically identified in 2 sec
epochs: active wakefulness, passive wakefulness, light and deep slowwave sleep and rapid-eye-movement sleep (REM), based on EEG, EMG
and body movements (PIR detection) following validated criteria
(Ahnaou and Drinkenburg, 2011).
The number and duration of SWDs across 24 h was scored
automatically based on the EEG signal from a frontal electrode against
reference according to well-established criteria, next they were verified
visually based on criteria published elsewhere (van Luijtelaar and
Coenen, 1986; Ovchinnikov et al., 2010).
Data from 24 h days of both acute and chronic experiment: time
spent in one of the five sleep-wake states (expressed in min.), number
and mean duration of SWDs (duration expressed in sec.) were grouped
into 1 h bin. Each hour of baseline day was compared with
corresponding hours of the treatment and post-treatment days in the
acute, and 10 post-shift days in the chronic study, respectively.
To estimate circadian parameter acrophase of the rhythms of
sleep-wake states and SWDs, data were grouped into 6 min bin, as
previously described (Smyk et al., 2012). Cosine wave of a fixed period
length of 24 h was fit to each of 3 days of the acute study, baseline
and post-shift days of the chronic study (program Cosinor available at
http://www.circadian.org/softwar.html).
The
phase
relationship
between different rhythms during baseline and post-shift days of the
chronic study was determined as a difference between their acrophases
(in degrees) on a particular experimental day. Significant difference
from the baseline value of the phase relationship between rhythms
during post-shift days was interpreted as desynchronization between
rhythms.
146
Additionally, the period of extended darkness (8 h before the
new light onset) of the chronic study was analyzed. Each hour of the
total time of particular sleep-wake states and number of SWDs in the
extended darkness was compared with corresponding hours of the
beginning of the light and the dark phase of the baseline day.
2.7.
Statistical analysis
Statistical analyses were done in Statistica (StatSoft, Inc.,
Tulsa, Oklahoma, USA). Repeated measures analysis of variance
(ANOVA) were used to estimate dose (4 levels), hour (24 levels) and
day (3 levels: baseline, treatment and post-treatment day) effect of
agomelatine treatment, on total duration of the five sleep-wake states,
mean duration and number of SWDs in the acute study, and dose (4
levels), phase (light and dark) and day (10 levels) effect of the phase
shift and agomelatine treatment in the chronic experiment. Repeated
measures ANOVA were also used to estimate the dose and day effect
of agomelatine treatment on circadian parameters of the rhythms of
sleep-wake states and SWDs in both studies as well as to compare the
period of extended darkness, the beginning of the light and the dark
phase and phase relationship between rhythms. Bonferroni’s test was
used as post-hoc test. Differences were considered to be significant at
a p level < 0.05.
3. Results
3.1.
Acute study
In all sleep-wake states investigated, an hour effect was
observed (active wakefulness: F = 235.32, passive wakefulness: F =
37.53, light slow-wave sleep: F = 385.21, deep slow-wave sleep: F =
147
241.82, REM sleep: F = 72.98 and SWDs: F = 53.864, all df = 23, 644,
all p < 0.05). In all sleep-wake states (except light slow-wave sleep)
and number of SWDs, a day effect was found (active wakefulness: F =
15.48, passive wakefulness: F = 9.96, deep slow-wave sleep: F =
14.11, REM sleep: F = 30.32 and SWDs: F = 9.76, all df = 2, 56, all p
< 0.05). Post-hoc tests following the day effect indicated a small
increase in passive wakefulness on the treatment day in comparison
with the baseline and the post-treatment day (5.95 ± 0.90 min vs 5.62
± 0.86 min and 5.95 ± 0.81 min, treatment vs baseline and posttreatment day, respectively, all p <0.05) and a small elevation of REM
sleep, both on treatment and post-treatment day (7.41 ± 0.74 and
7.81 ± 0.82 min vs 7.17 ± 0.79 min, treatment and post-treatment vs
baseline day, respectively, all p < 0.05). A decrease on the treatment
day was found for deep slow-wave sleep and number of SWDs (15.31
± 0.70 min vs 15.81 ± 0.74 and 15.90 ± 0.80 min, treatment vs
baseline and post-treatment day, respectively for deep slow-wave
sleep, all p < 0.05; 15.01 ± 2.31 vs 17.09 ± 2.32 and 16.90 ± 2.95,
treatment vs baseline and post-treatment day, respectively for the
number of SWDs), while active wakefulness last shorter only on posttreatment day (18.76 ± 1.19 min vs 19.46 ± 1.15 and 19.78 ±1.31
min, post-treatment vs baseline and treatment day, respectively). A
significant day x hour interaction was found for all variables
investigated (active wakefulness: F = 5.18, passive wakefulness: F =
4.80, light slow-wave sleep: F = 4.93, deep slow-wave sleep: F = 4.70,
REM sleep: F = 7.57 and SWDs: F = 8.18, all df’s = 46, 1288; all p <
0.05). Post-hoc comparison between baseline and treatment day
revealed significant differences for single hours only: 6th hour after
administration of agomelatine for active wakefulness (38.06 ± 1.26
min vs 30.73 ± 1.19 min, treatment vs baseline day, p < 0.05), 12th
for passive wakefulness (7.85 ± 0.31 min vs 5.46 ± 0.31 min,
treatment vs baseline day, p < 0.05), 1st, 6th and 24th for light slow148
wave sleep (9.36 ± 0.36 min vs 11.72 ± 0.41; 2.78 ± 0.25 min vs
5.32 ± 0.29 min; 14.51 ± 0.38 min vs 10.30 ± 0.34 min, treatment
vs baseline day for 1st, 6th and 24th hour, respectively, all p < 0.05),
24th for deep-slow wave sleep (15.67 ± 0.40 min vs 10.61 ± 0.58 min,
treatment vs baseline day, p < 0.05) and 2nd hour for REM sleep
(10.43 ± 0.56 min vs 13.45 ± 0.52 min, treatment vs baseline day, p
< 0.05). However, the number of SWDs was decreased for 4
consecutive hours (4th, 6th – 8th) on the treatment day compared to
baseline and post-treatment day (4th hour: 25.56 ± 2.13 vs 15.94 ±
1.17 treatment vs baseline day, p < 0.05; 6th hour: 14.65 ± 1.50 vs
29.16 ± 1.71 and 30.87 ± 2.92, treatment vs baseline and posttreatment day, all p < 0.05; 7th hour: 14.91 ± 1.35 vs 31.28 ± 2.53
and 32.91 ± 2.11, treatment vs baseline and post-treatment day, all p
< 0.05; 18.34 ± 1.81 vs 34.09 ± 2.26 and 29.06 ± 1.98, treatment vs
baseline and post-treatment day, all p < 0.05).
There was neither a dose nor a dose and day interaction, nor
between day, dose and hours, as could have been predicted in case
agomelatine affected sleep-wake states or the number of SWD in a
couple of hours on the treatment day. The data on SWD occurrence
across the 24h period in all the three days are presented in Figure 1.
Analysis of acrophase of the rhythms of sleep-wake states and
SWDs revealed neither a dose effect of agomelatine nor an interaction
between dose and day. Significant results regarded day effects only
(active wakefulness: F = 29. 80, df = 2, 56, p < 0.05; deep slow-wave
sleep: F = 7.09, df = 2, 56, p < 0.05; REM sleep: F = 21.56, df = 2,
56; SWDs = 14.78, df = 2, 56, p < 0.05). In all cases analyzed, with
one exception of light slow-wave sleep, which was not affected by
agomelatine at all, acrophase of the rhythms was slightly advanced on
the treatment day in comparison to baseline (active wakefulness:
84.72 ± 1.19 vs 91.62 ± 1.15 degrees; deep slow-wave sleep: 63.04
± 4.37 vs 84.04 ± 2.77 degrees; REM sleep: 309.19 ± 2.58 vs 329.09
149
± 2.34 degrees; SWDs: 63.04 ± 4.37 vs 84.042.78 degrees, treatment
day vs baseline, all p < 0.05). The effect noticed at the treatment day
was still present at the post-treatment day since acrophases of the
rhythms of active wakefulness, deep slow-wave sleep, REM sleep and
SWDs were still advanced (all p < 0.05). The acrophase of active
wakefulness was even more advanced than on the treatment day
(91.62 ± 1.15 vs 84.72 ± 1.19 vs 80.22 ± 1.18 degrees, baseline vs
treatment vs post-treatment day, all p < 0.05).
Figure 1. Mean and SEM of the number of SWDs across 24 h on (A) baseline,
(B) agomelatine treatment and (C) post-treatment day of the acute study.
Doses: 2.5 mg/kg (n = 8) – green line, 5 mg/kg (n = 8) – blue line, 10 mg/kg
(n = 8) – red line, control group (n = 8, 20% cyclodextrin) – black line.
Resolution of the data: 1 h. Mean and SEM (n = 32) of the number of SWDs
across 24 h during all experimental days of the acute study (D). Baseline day
– black line, agomelatine treatment day – dark grey line, post-treatment day
– light grey line. The dark phase of the 12:12 light-dark cycle is marked by the
grey rectangle
150
3.2.
Chronic study
Circadian rhythms in all sleep-wake states and in the occurrence
of SWDs were found on the baseline day. Rats spent more time in
active wakefulness during the dark period of 12:12 light-dark cycle
(337.95 ± 6.31 min vs 129.34 ± 4.20 min, p < 0.05), they had also
more absence seizures (295.12 ± 31.43 vs 102.38 ±11.72, p < 0.05).
The light phase was dominated by slow-wave and REM sleep (light
slow-wave sleep: 152.94 ± 2.22 vs 65.71 ± 1.53 min, deep slow-wave
sleep: 253.57 ± 3.58 vs 131.18 ± 2.94 min, REM sleep: 93.73 ± 2.62
vs 75.61 ± 2.20 min, light vs dark, respectively, all p < 0.05). The
phase-related difference in the total duration of passive wakefulness
was not significant (72.30 ± 2.64 vs 67.73 ±2.21, dark vs light, p >
0.05).
Repeated measures ANOVA for the total time spent in active
wakefulness in the light and the dark phase over all recorded days
(baseline and 9 post-shift days) showed a significant day (F = 10.28,
df = 9, 504, p < 0.05), phase (F = 743.91, df = 1, 56, p < 0.05) and
an interaction (day x phase) (F = 243.24, df = 9, 504, p < 0.05) effect.
Significant dose effect and interactions with dose such as dose x phase,
dose x day and dose x day x phase were not found. Post-hoc
comparison between days showed that the total duration of active
wakefulness was decreased at 1st, 3rd and 4th day after the phase
shift (222.13 ± 4.13 min, 216.07 ± 3.71 min and 222.81 ± 3.79 min
vs 233.64 ± 4.62 min, all p < 0.05). The phase-shift caused a decrease
in the duration of active wakefulness in the dark phase accompanied
by an increase in the light phase which last from 1st to 7th post-shift
day. Data are presented in Figure 2.
Repeated measures ANOVA for the total time spent in passive
wakefulness in the light and the dark phase over all recorded days
showed a significant day (F = 6.63, df = 9,504, p < 0.05) and an
151
interaction (day x phase) (F = 54.51 df = 9,504, p < 0.05) effect.
Neither phase and dose effects, nor interactions with dose were found.
Total duration of passive wakefulness during almost all post-shift days
(exceptions: 5th, 6th and 8th day) was elevated as revealed by posthoc tests (all p’s < 0.05). Also phase-dependent changes were found.
A robust increase in the duration of passive wakefulness was observed
in the light phase from 1st to 4th post-shift day (significant increase
was still observed on 5th and 7th post-shift day, all p’s < 0.05). A
nocturnal level was decreased on the 1st and 3rd post-shift day and
also during last 3 days of the experiment (Figure 2). Dose effects and
interactions: dose x phase, dose x day and dose x day x phase were
not found.
Significant day (F = 3.18, df = 9, 504, p < 0.05), phase (F =
1423.45, df = 1, 56, p < 0.05) and day x phase (12.66, df = 9, 504, p
< 0.05) effect was found for total duration of light slow-wave sleep
over all experimental days. Changes of the total time spent in light
slow-wave sleep in the light and the dark phase were less pronounced
as in comparison to active and passive wakefulness. Both diurnal and
nocturnal level of light slow wave sleep was increased and reduced,
respectively only for 2 days after the phase shift. Data presented in
Figure 2.
Analysis of variance of total time spent in deep slow-wave sleep
showed significant phase (F = 359.56, df = 1, 56, p < 0.05) and day x
phase interaction effect (F = 472.70, df = 9,504, p < 0.05). Neither
day nor dose effect were found (F = 1.65, df = 9, 504 and F = 0.65,
df = 3, 56, respectively, all p’s > 0.05), nor interactions with dose.
Total duration of deep slow-wave sleep was significantly elevated in
the dark phase during first 5 days after the phase-shift (216.21 ± 2.70
min, 198.03 ± 3.02 min, 176.26 ± 3.19 min, 161.74 ± 3.24 min and
148.85 ± 3.48 min vs 131.18 ± 2.94, 1st, 3rd, 4th, 5th and 6th postshift day vs baseline, all p’s < 0.05) while duration in the light phase
152
was decreased for four days (162.75 ± 2.84 min, 192.85 ± 3.29 min,
209.82 ± 3.32 min and 161.74 ± 3.24 min vs 253.57 ± 3.58, 1st, 3rd,
4th, and 5th post-shift day vs baseline, all p < 0.05). On the first postshift day, typical phase-related difference: longer duration of deep
slow-wave sleep during the dark period was reversed (p < 0.05). The
restoration took place on 4th post-shift day and stayed significant from
that moment onwards (all p < 0.05) (Figure 2).
Repeated measures ANOVA for the total time spent in REM sleep
in the light and the dark phase over all recorded days showed a
significant day (F = 12.71, df = 9,504, p < 0.05), phase (F = 17.35,
df = 1, 56, p < 0.05) and an interaction (day x phase) (F = 28.87 df
= 9,504, p < 0.05) effect. No dose effect was found, nor an interaction
with dose. Post-hoc comparison revealed that the duration of REM
sleep was increased starting from the 1st to 6th post-shift day (all p <
0.05), while between-phases comparison showed that this is a result
of elevated level of REM sleep in the dark phase, since large significant
differences between baseline and post-shift days were found only
during the darkness (1st – 7th post-shift day, all p < 0.05).
153
Figure 2. Mean and SEM (n = 32) of the (A) total duration of sleep-wake states
and (B) total duration of sleep-wake states in the light and the dark phase of
154
the 12:12 light-dark cycle before and after an 8 h light phase delay. AW –
active wakefulness, yellow line; PW – passive wakefulness, red line; SWS L –
light slow-wave sleep, green line, SWS D – deep slow-wave sleep, dark green
line, REM – REM sleep, blue line. The light phase is marked by a dotted line,
the dark phase is marked by a solid line. * p < 0.05, baseline vs post-shift
days, ANOVA for repeated measures design, Bonferroni post-hoc test
Significant phase (F = 15.52, df = 1, 7, p < 0.05) and day x
phase interaction (F = 21.04, df = 9, 36, p < 0.05) for the number of
SWDs, day (F = 3.87, df = 9, 36, p < 0.05) and day x phase interaction
(F = 3.28, df = 9, 36, p < 0.05) effect for mean duration of SWDs were
found. Post-hoc tests revealed that in the light phase rats had more
seizures on first 4 post-shift days in comparison to baseline, while
during same number of post-shift days in the dark phase absences
were less numerous (all p < 0.05). Similarly to deep slow-wave sleep
a phase-related redistribution was seen on the 1st post-shift day,
however it was not significant (data presented in Figure 3). The mean
duration of SWDs was increased from 3rd to 4th and post-shift days (p
< 0.05), with the increase in the light phase (first 3 post-shift days, all
p < 0.05) as the main contributor to this effect (Figure 3). There was
no dose effect or interactions with dose on number and mean duration
of SWDs.
Circadian rhythmicity was detected in all sleep-wake states and
the occurrence of SWDs on the baseline day. The acrophase of active
wakefulness and SWDs was found to occur in the dark phase, while
light slow-wave sleep, deep slow-wave sleep and REM sleep peaked in
the light phase. The acrophase of passive wakefulness rhythm was
found around a light/dark transition (at the end of the light phase in
some rats while at the beginning of the dark phase in others),
therefore, not the acrophase per se but the difference in degrees
between the baseline value and each consecutive day was analyzed.
155
Figure 3. Mean and SEM (n = 8) of the total number and mean duration of
SWDs (A) and the total number and mean duration of SWDs in the light and
the dark phase of the 12:12 light-dark cycle (B) before and after an 8 h light
phase delay. The light phase is marked by a dotted line, the dark phase is
marked by a solid line. * p < 0.05, baseline vs post-shift days, ANOVA for
repeated measures design, Bonferroni post-hoc test
In general, dose effect of agomelatine and its interactions were
not found in acrophases of the rhythms investigated. To avoid that the
outcomes of circadian analyses were overpowered by the large number
of animals (n = 32), only the data from the control group (n = 8) were
used to illustrate the process of re-entrainment of various rhythms to
the phase shift. Analysis of variance (ANOVA, repeated measures
design) for the rhythms of sleep-wake states and SWDs over all
experimental days (baseline and 9 post-shift days) showed that the
position of acrophase changed over days in all states and SWDs with
an exception of passive wakefulness (active wakefulness: F = 118.44,
light slow-wave sleep: F = 67.88, deep slow-wave sleep: F = 99.06,
156
REM sleep: 13.92, SWDs: F = 10.08, all df = 9, 63, all p < 0.05). 8 h
phase shift in the 12:12 light dark cycle caused an advanced of all
acrophases of 72.95 ± 25.07 degrees (active wakefulness: from 81.50
± 2.41 to 12.38 ± 2.90 degrees, light slow-wave sleep: from 274.50
± 1.94 to 228.25 ± 2.28 degrees, deep slow-wave sleep: from 226.87
± 2.96 to 156.12 ± 1.88 degrees, REM sleep: from 315.75 ± 4.03 to
180.12 ± 11.43 degrees and SWDs: from 65.25 ± 6.44 to 19.25 ±
3.65 degrees). Phase-shift magnitude (the difference between the
position of acrophase between the baseline and the 1st phase-shift
day) was different for the different sleep-wake states (one-way ANOVA
with sleep-wake states-SWD as between subjects factor: F = 25.95, df
= 4, 35; p < 0.05). Post-hoc tests revealed that although the
acrophase
of
REM
sleep
was
affected
the
most,
REM
sleep
accomplished the largest daily shifts (REM sleep vs remaining sleepwake states and SWDs, all p < 0.05) and was the fastest to re-entrain:
the acrophase of the REM rhythm was no longer different from baseline
level on the 6th post-shift day. Light slow-wave sleep and SWDs
reached the baseline values on the 7th day after the shift. Active
wakefulness and deep slow-wave sleep showed similar speeds of reentrainment. However, their re-synchronization took one day longer
and was accomplished on the 8th post-shift day. Data are presented in
Figure 4. During the re-synchronization process, circadian rhythmicity,
determined by a significant fit of the cosine curve to the data, was
present in all animals, during all experimental days after the phaseshift in following variables: active wakefulness, light and deep slowwave sleep. Circadian rhythms of the number of SWDs, passive
wakefulness, and REM sleep could not be well fitted by a cosine and
therefore it is suggested that these rhythms showed a poor temporarily
organization. Two latter states showed the greatest abnormalities:
even on the 10th post-shift day, the expression of passive wakefulness
and REM sleep in some rats was not rhythmic.
157
Analysis of variance (ANOVA, repeated measures design) for the
phase relationships between rhythms of sleep-wake states and SWDs
over all experimental days (baseline and 9 post-shift days) showed
significant day effect in all variables investigated (active wakefulness light slow-wave sleep: F = 10.06, active wakefulness - deep slow-wave
sleep: F = 2.75, active wakefulness - REM sleep: F = 4.56; light slowwave sleep - deep slow-wave sleep: F = 8.68, light slow-wave sleep SWDs: F = 8.68, deep slow-wave sleep - REM sleep: F = 8.27, deep
slow-wave sleep - SWDs: F = 5.59; REM sleep - SWDs: F = 6.34; all
df = 9, 63, all p < 0.05) with two exceptions: relationship between
active wakefulness and SWDs, where it just failed to reach significance
(F = 2.03, df = 9, 63; p = 0.05) and relationship between light slowwave and REM sleep (F = 1.63, df = 9, 63; p > 0.05). Post-hoc
comparison revealed that the phase relationship between activewakefulness and light slow-wave sleep changed across the days after
the phase shift: from 1st to 5th, 7th and 9th post-shift day. Difference
between acrophases of light and deep slow-wave sleep was changed
for 3 days after the shift and additionally on the 5th post-shift day,
while alterations in phase relationship between active wakefulness and
REM sleep, light and deep slow-wave sleep, deep slow-wave sleep and
SWDs, REM sleep and SWDs lasted for 3 days after the phase shift.
Post-hoc comparison revealed no changes over days in a phase
relationship between active wakefulness and deep slow-wave sleep, as
well as light slow-wave sleep and SWDs.
158
Figure 4. Mean and SEM (n = 8) of the total duration of sleep-wake states and
number of SWDs during the baseline and all post-shift days (upper panel).
Data resolution: 1 h. Mean and SEM (n = 8) of the acrophase of vigilance and
159
SWDs rhythms during the baseline and after an 8 h light phase delay (lower
panel). Active wakefulness – orange line, passive wakefulness – red line, light
slow-wave sleep – green line, deep slow-wave sleep – dark green line, REM
sleep – blue line, SWDs – black line. The dark phase of the 12:12 light-dark
cycle is marked by the grey rectangle. * p < 0.05, baseline vs post-shift days,
ANOVA for repeated measures design, Bonferroni post-hoc test
Analysis of the period of extended darkness for all animals
revealed a phase, an hour and an hour x phase interaction effect. Posthoc comparison between phases showed that the total time spent in
active and passive wakefulness, deep slow-wave sleep and also
number of SWDs recorded during extended darkness resembles the
light phase of the baseline and is significantly different from the dark
phase of the baseline (all p < 0.05, data presented in Figure 5). The
response of light slow-wave and REM sleep to an unusual extension of
the dark phase was quite different. Total time spent in light slow-wave
sleep during the extended dark period was greater than during the dark
phase (8.65 ± 0.16 vs 5.28 ± 0.16 min, p < 0.05) but smaller than
during the light phase (8.65 ± 0.16 vs 12.63 ± 0.16 min, p < 0.05).
Total time of REM sleep during the extended darkness was significantly
longer in comparison to the light and dark baseline periods (10.29 ±
0.20 vs 7.01 ± 0.20 and 5.84 ± 0.16 min, extended darkness vs light
and the dark phase, respectively, all p < 0.05). Detailed, hour by hour
comparisons between phases are presented in Figure 5.
160
161
Figure 5. Mean and SEM (n = 32) of the total duration of active wakefulness
(A), passive wakefulness (B), light slow-wave sleep (C), deep slow-wave sleep
(D), REM sleep (E) and the number of SWDs (F) (upper panels) during the 8
first hours of the baseline light phase, the 8 first hours of the baseline dark
phase and 8 hours of the extended darkness on the phase-shift day. Mean and
SEM (n = 32) of the total duration of vigilance states and the number of SWDs
in particular hours of the phases (lower panels). The light phase – grey
line/block, the dark phase – black line/block, extended dark period – dark
green line/block. *, # p < 0.05 extended darkness vs dark phase, extended
darkness vs light phase, respectively, ANOVA for repeated measures design,
Bonferroni post-hoc test
4. Discussion
Re-synchronization of various sleep-wake states and absence
seizures to an 8 h light phase delay was investigated in a validated
animal model of CAE. Main questions addressed by the study were:
how fast different sleep-wake states and SWDs re-synchronize to a
shifted photoperiod and whether, given a well-established relationship
between absences and sleep-wake states, the rhythm of SWDs is
coupled to any of the other rhythms. Second goal was to evaluate the
effect of agomelatine, an antidepressant drug with chronobiotic
properties, on the duration of sleep-wake states and the number of
SWDs during stable entrainment (acute study) and after the phase shift
(chronic study).
Different speeds of the re-entrainment of sleep-wake states and
SWDs rhythms, coupling between various rhythms during the resynchronization process, different effect of the prolonged darkness on
the rhythms and a lack of effects of agomelatine neither in the acute,
nor in the chronic experiment comprise the main findings of the present
study.
162
4.1.
Course of re-entrainment of the sleep-wake states
and SWDs rhythms
Physiological
consequences
of
circadian
misalignments
produced by rapid phase shifts in the photoperiod are well documented
both in humans as well as in animal models (Winget et al., 1984; Lancel
and Kerkhof, 1989). The number of hours and the direction of the shift
(advance vs. delay) are critical for a speed of subsequent reentrainment (Wever, 1966). In general, phase delay adjustments
corresponding to westward travels are accomplished faster than phase
advances, an equivalent of eastward travels (Lemmer et al., 2002;
Murphy et al., 1998; Reddy et al., 2002; Yan, 2011). Within the limits
of one direction of the shift, distinct rhythms may re-entrain with
different speed. An internal desynchronization between circadian
oscillators and rhythms is quite common (Nagano et al., 2003; Lee et
al., 2009). In the present study, an 8 h light phase delay was produced
by extending the dark period from 12 to 20 h. As a result, all rhythms
investigated were found to be advanced with respect to the new
photoperiod. Changes in total duration and phase-related distribution
of all sleep-wake states, number and mean duration of SWDs were
found.
4.1.1.
Active wakefulness
Animal studies investigating re-entrainment of all sleep-wake
states after the phase shift are scarce. Usually only active wakefulness
or general activity, measured by means of passive infrared sensors or
telemetrically, or wheel running activity across days are reported. In
general, the time required for re-entrainment of these rhythms (in
days) is smaller than the number of hours by which the phase was
delayed (Takamure et al., 1991; Yamazaki et al., 2000). In rodents,
163
the re-entrainment process was accompanied by a redistribution of
activity between the new light and the dark period (Kopp et al., 1998;
Nagano et al., 2003). In our previous study in which the same phase
shift paradigm in WAG/Rij rats was used, re-synchronization of general
motor activity was complete on the 7th post-shift day, which is one day
earlier in comparison to the present result. A similar redistribution of
activity was observed, however, the decrease of the nocturnal level
lasted one day longer (8 days), while the diurnal level was increased
for 4 post-shift days only (Smyk et al., 2012). Given the lack of any
effect of agomelatine in both the acute and chronic study, differences
between the present and our previous studies in which rats were not
injected, might be attributed to the additional manipulations necessary
for the administration of the solvent or drug. The injections, handling
and transient arousals repeated daily during all 10 post-shift days
might be considered as another, non-photic stimulus for the circadian
timing system. The presence of such stimulus might have affected the
re-synchronization process to some degree. The possibility cannot
excluded that factors such as handing and injections are stressful,
however, in habituated animals these procedures are generally not
considered as stressors.
4.1.2.
Passive wakefulness
A decrease in the total amount of active wakefulness was
followed by an increase in passive wakefulness occurring immediately
after the shift and lasting the same number of days. The decrease of
activity or wake state in general has been described previously. A study
of Beaumont and coworkers (2004) might be linked to some extent to
the decrease in activity reported by us; they investigated effects of
slow-release caffeine and melatonin on sleep and daytime sleepiness
after a seven-time zone eastward flight. Subjects receiving placebo
164
were significantly drowsier until 6th day after the flight in comparison
to baseline as measured by modified EEG sleep latency test (Beaumont
et al., 2004).
4.1.3.
Light and deep slow-wave sleep
The total duration and the phase-related distribution of light
slow-wave sleep were hardly affected by the phase shift. A similar
effect regarding total sleep duration was seen also for deep slow-wave
sleep. However and in contrast, the phase-related changes of deep
slow wave sleep were much more pronounced and long-lasting. Similar
results with respect to the amount and distribution of slow-wave sleep,
however, in a phase advance paradigm, were obtained by Sei and
coworkers (Sei et al., 1992, 1994). The less adverse effect of the phase
delay on slow wave sleep in the present study may account for the lack
of changes in total duration of slow-wave sleep during post-shift days.
Moreover, the study cited did not distinguished between the two stages
of slow-wave sleep. Human polysomnographic studies revealed that an
8 h phase delay had a small effect on slow-wave sleep (only a trend
towards smaller amounts of slow-wave sleep on the first post-shift day
was
observed,
the
distribution
of
slow-wave
sleep
remained
unchanged), while the phase advance resulted in an increase in the
amount of slow-wave sleep up to 5 days (Buxton et al., 2000;
Beaumont et al., 2004).
4.1.4.
REM sleep
REM sleep was elevated for 6 days, this was in contrast to both
light and deep slow-wave sleep. The increase in REM sleep was
restricted to the dark phase. The increase in REM sleep after a phase
shift was reported previously in a phase advance paradigm in rats, as
165
well as, in mice under chronic circadian disruption protocol, during
which animals were exposed weakly to 6 h phase advances of the lightdark cycle (Sei et al., 1992, 1994; Castanon-Cervantes et al., 2010;
Brager et al., 2013). In humans, a study in a laboratory controlled 8 h
light-dark and sleep-wake cycle phase delay, abnormalities in the
distribution of REM sleep up to 3 post-shift days were found (Buxton
et al., 2000).
4.1.5.
SWDs
The phase delay affected also the epileptic activity of the rats:
while the total number of SWDs after the phase shift remained
unchanged, a phase-related redistribution occurred. SWDs were more
prevalent during the light phase and less during the dark phase. SWDs
are tightly related to a momentary state of vigilance: passive
behavioral states such as passive wakefulness and light slow-wave
sleep enhance the occurrence of seizures; active wakefulness, deep
slow-wave and REM sleep have an inhibitory effect (Drinkenburg et al.,
1991). In the present study, the elevation of the number of SWDs
during the light phase is accompanied by the increase in the total
duration of passive wakefulness and a marked reduction of deep slowwave sleep. Therefore, a balance between SWDs enhancing (passive
wakefulness, light slow-wave sleep) and inhibiting (deep slow-wave
sleep) brain states is shifted towards favorable conditions for SWDs to
occur.
Similar results were obtained in our previous study, in which
WAG/Rij rats were exposed to the same light-dark manipulation
however, without pharmacological intervention (Smyk et al., 2012).
Both studies showed the increase in the occurrence of seizures in the
light phase, during which SWDs are usually less frequent. Different
types of seizures e.g. temporal lobe seizures, frontal lobe seizures,
166
tend to occur rhythmically at the particular time of a day (Quigg et al.,
1998; Pavlova et al., 2004; Durazzo et al., 2008; Hofstra et al., 2009).
As a result of circadian desynchronization the timing of seizures may
change. Hypothetically, temporal lobe seizures which occur usually
during the day, after a rapid crossing of considerable number of time
zones may occur during the night (Quigg et al., 1998). This may lead
to additional sleep disturbances and worsening of jet lag symptoms
since convulsive seizures occurring during the night have been shown
to cause sleep fragmentation, reduction of particular sleep stages and
daytime somnolence (Bazil et al., 2000). Absences, known to be
related to sleep-wake states and thus occurring more frequently during
drowsiness, sleep and state transitions (Halasz et al., 2002) may
increase during the day due to enhanced sleepiness and also during
the night due to poor, fragmented sleep. To avoid possible aggravation
of epileptic activity patients should adjust the timing of antiepileptic
medications or prepare themselves to the new time zone by gradual,
everyday shifts before the journey. The possibility that also the dose
needs an adaptation, cannot be excluded at forehand.
4.1.6.
Internal desynchronization
Internal desynchronization after a rapid shift in the photoperiod
has been found both in humans and animal models (Wegman et al.,
1986; Nagano et al., 2003; Lee et al., 2009). In the present study
different speed of re-entrainment of various rhythms and changes in
their phase relationships was observed. The acrophase of REM sleep
was shifted the most; however, it re-synchronized as first, on the 6th
post-shift day. REM sleep propensity is well known to be controlled by
the circadian timing system (Czeisler et al., 1980). However, the
amplitude of the rhythm is small in comparison to, for example, activity
or slow-wave sleep (Borbély, 1975). It is suggested that rhythms with
167
small circadian amplitude accomplished larger acrophase shifts and
their rate of re-synchronization is faster (Reinberg et al., 1979; Winget
et al., 1984). In agreement with this assumption, the rhythm of active
wakefulness and deep slow-wave sleep, both of large circadian
amplitude, completed the re-entrainment process the slowest. Their
acrophases reached the baseline level on 8th post-shift day. Rhythms
of light slow-wave sleep and SWDs, both with average amplitude,
completed the shift one day earlier. REM sleep, characterized by the
lowest circadian amplitude, re-synchronized the fastest.
During an extended dark period preceding the light phase delay,
rhythms which re-synchronized together showed similar behavior.
Active wakefulness, deep slow-wave sleep but also SWDs rhythm
behaved as during corresponding hours of the light phase. Similar
results were obtained for general motor activity and the number of
SWDs in the previous study (Smyk et al., 2012). Light slow-wave and
REM sleep showed different behavior hardly comparable with either the
light or the dark phase.
Desynchronization between different states of sleep-wake
states after the phase shift and under a forced desynchrony protocol
has been described previously in rats (Sei et al., 1992, 1994; Cambras
et al., 2007; Lee et al., 2009). It is thought to be a consequence of
desynchrony between anatomically and functionally distinct subregions
of the SCN, to which particular sleep-wake states are coupled to.
Circadian regulation of sleep, slow-wave sleep and locomotor activity
was found to be associated with the activity of both the ventrolateral
and dorsomedial SCN, while circadian regulation of REM sleep
dependent merely on the activity of the dorsomedial SCN (Cambras et
al., 2007; Lee et al., 2009). In the present study, significant post-shift
changes in phase relationship between active wakefulness and REM
sleep as well as deep slow-wave and REM sleep were found, which
suggests internal desynchronization between these sleep-wake states.
168
On
the
other
hand,
difference
between
acrophases
of
active
wakefulness and deep slow-wave sleep remained unchanged, which
might be interpreted as stable coupling between these rhythms also
during re-entrainment. The response of active wakefulness and deep
slow-wave sleep to the phase shift, their re-entrainment rate and
stable phase relationship may suggest that indeed they are governed
by the same mechanism distinct from REM sleep, which they uncoupled
from. The rhythm of SWDs and light slow-wave sleep shared the same
speed of re-synchronization, their phase relationship was stable over
the post-shift days. However, the response of the rhythms to the
extended period of darkness was quite different. Light slow-wave sleep
is one of the most favorable states of vigilance for the occurrence of
SWDs (Drinkenburg et al., 1991). Same length of the re-entrainment
process may suggest that the rhythms share common circadian
mechanism. Different response to the lengthened period of darkness
may be related to various level of circadian/exogenous component of
the rhythms or differences in the magnitude of masking.
4.2.
Lack of effects of agomelatine on states of sleepwake states and SWDs – acute study
Neither dose-dependent nor drug effects of agomelatine on
sleep-wake states and number of SWDs were found in the acute study,
only a day effect.
Dosage of agomelatine selected for the present study was based
on its circadian synchronizing abilities. According to the study of
Martinet and co-workers (1996), dose-dependent effect of agomelatine
on a free-running rhythm of running wheel activity was observed
between 2.5 to 10 mg/kg, with a dose of 5.7 mg/kg providing true
entrainment (Martinet et al., 1996). The highest dose used in the
present study (10 mg/kg) was found to induce changes in sleep-wake
169
architecture in a period between 4 to 7 hours after the administration
and also during the following day (Descamps et al., 2009). On the other
hand, given shortly before the dark onset, agomelatine failed to affect
sleep-wake states in nocturnal rodents (Tobler et al., 1994; Huber et
al., 1998; Langebartels et al., 2001; Monti et al., 2001). Considering
the effect observed during the treatment day, similar changes in the
total duration of sleep-wake states were found for REM sleep (increase)
and active wakefulness (decrease, but only on the post-treatment
day), while decrease in the duration of slow-wave sleep was opposite
to changes reported by Descamps et al. (2009). Since neither a dose,
nor a drug or interaction with dose was found in the present study,
other effects such as day affects cannot be attributed to the action of
the compound rather they may have been produced by the vehicle.
Cyclodextrin (20%) was used as a vehicle and it was given only
on day 2. Cyclodextrins are cyclic oligosaccharides containing 6 to 8
glucose units connected by α-(1, 4) bonds. This particular formation
(hydrophilic outside, hydrophobic inside) accounts for their ability to
create
inclusion
compounds
with
other
hydrophobic
molecules
increasing their stability, solubility and bioavailability and are widely
used (Duchene et al., 1986; Davis and Brewster, 2004). Considered as
neutral solvent, cyclodextrins were reported to exert neuroactive effect
both in vitro and in vivo (Wang et al., 1997; Shu et al., 2004; Pytel et
al.,
2006).
In
WAG/Rij
rats,
hippocampal
microinjections
of
cyclodextrin (45% solution) had a biological relevant effect (it reduced
the number of SWDs to 1 h after the administration) (Tolmacheva and
van Luijtelaar, 2007). In the present study, the number of SWDs was
decreased during the treatment day between 4 to 8 h after
administration. Differences in time, during which the effect was
manifested may be attributed either to the route of administration:
local vs systemic or, considering a slight passage of cyclodextrin
through blood-brain barrier (Monnaert et al., 2004), behavioral effects
170
of the administration such as arousal, stomach load, increased thirst
may be responsible for the effect observed.
4.3.
Lack of effects of agomelatine on sleep-wake
states and SWDs – chronic study
No drug or dose-dependent effect of agomelatine was found in
the chronic study. Daily oral administration of agomelatine did neither
affect any of the characteristics of the rhythms investigated, nor the
speed of the re-synchronization process. Chronobiotic properties of
melatonin and its analogues were found in a phase advance paradigm,
in which, physiological rhythms are delayed with respect to the new
photoperiod and require to be advanced to reach a stable phaserelationship with the Zeitgeber (Golombek and Cardinali, 1993; van
Reeth et al., 1998). In the phase delay design, melatonin was found
either non effective or delaying the re-synchronization process
(Singaravel et al., 1996). Nonetheless, due to its beneficial effect on
the subjective sleep, alertness and performance, melatonin treatment
is advised to ameliorate symptoms of jet lag also after westward flights
(Deacon and Arendt, 1995; Arendt, 1999).
In comparison to the previous study in which general motor
activity and the rhythm of SWDs were investigated under the same
experimental design, the speed of re-synchronization of both rhythms
was slightly different (Smyk et al., 2012). The rhythm of general motor
activity re-entrained one day faster than the rhythm of active
wakefulness, while the rhythm of SWDs re-entrained one day earlier in
the present study. An absence of dose-dependent effect of agomelatine
and differences between studies in the speed of entrainment suggest
not only that actions accompanying the procedure of peri oral injections
(removal from cage, associated handling) might have affected the
reentrainment process (like in the study of Hastings et al., 1992), but
171
also obscured the putative positive effects of agomelatine on speed or
re-entrainment. Put into other words: the transient arousal produced
by agomelatine administration might have masked the effect of the
drug on the rhythms. Another possibility might be that doses selected
for our study were too small to induce any changes. Martinet and coworkers (1996) reported that a dose of 5.7 mg/kg was effective in
providing
entrainment
(Martinet
et
al.,
1996).
However,
the
experiment was conducted in constant darkness while in the present
study rats were kept in 12:12 light-dark cycle before and also after the
phase shift. It cannot be excluded that the presence of this powerful
Zeitgeber outweighed the effects of agomelatine. Assessment of the
level of melatonin during the study would allow more precise
explanations.
To
conclude,
the
present
study
demonstrates
internal
desynchronization between various sleep-wake states and absence
seizures after a rapid shift in the timing of the light phase of the
photoperiod. Rhythms, thought to be controlled by common circadian
mechanism, such as active wakefulness and deep slow-wave sleep
showed coupling during the process of re-synchronization. The SWDs
rhythm re-entrained together with light slow-wave sleep, a state
frequently preceding the occurrence of absence seizures. This may
suggest a common mechanism for these functions, distinct from the
one
governing
activity
and
deep
slow-wave
sleep.
Circadian
misalignment caused by the phase shift resulted in an aggravation of
epileptic activity especially in the light phase, during which seizures in
WAG/Rij rats are sparse. An increased duration of passive wakefulness
seems to be responsible for the increase in SWD, since, together with
light slow-wave sleep, this sleep-wake state is favorable for the
occurrence of SWDs. Treatment with melatonin agonist was found to
be ineffective in the acute sleep-wake study, as well in the phase delay
paradigm. It had also no effect on epileptic activity. The redistribution
172
of the sleep-wake states across the 24 h during the entrainment
process might be of significant importance for people with epilepsy
planning transmeridian flights and dealing with consequences of jet
lag.
5. Acknowledgements
The authors would like to thank Dirk Nuyts for his excellent
technical assistance and Dr. Abdallah Ahnaou for fruitful scientific
discussions. The authors would like to express the gratitude to Janssen
Research & Development, a Division of Janssen Pharmaceutica NV,
Beerse, Belgium for supplying facilities to perform the study.
6. References
Aguiar CC, Almeida AB, Araújo PV, Vasconcelos GS, Chaves EM, do Vale OC, Macêdo
DS, de Sousa FC, Viana GS, Vasconcelos SM. 2012. Anticonvulsant effects of
agomelatine in mice. Epilepsy Behav. 24: 324-328.
Ahnaou A, Drinkenburg WH. 2011. Neuromedin U(2) receptor signaling mediates
alteration of sleep-wake architecture in rats. Neuropeptides.45: 165-174.
Arendt J, Marks V. 1982. Physiological changes underlying jet lag. Br Med J (Clin Res
Ed). 284: 144–146.
Arendt J. 1999. Jet-lag and shift work: (2). Therapeutic use of melatonin. J R Soc Med.
92: 402-405.
Bazil CW, Castro LH, Walczak TS. 2000. Reduction of rapid eye movement sleep by
diurnal and nocturnal seizures in temporal lobe epilepsy. Arch Neurol. 57: 363368.
Beaumont M, Batéjat D, Piérard C, Van Beers P, Denis JB, Coste O, Doireau P, Chauffard
F, French J, Lagarde D. 2004. Caffeine or melatonin effects on sleep and
sleepiness after rapid eastward transmeridian travel. J Appl Physiol (1985). 96:
50-58.
Bertaina-Anglade V, la Rochelle CD, Boyer PA, Mocaër E. 2006. Antidepressant-like
effects of agomelatine (S20098) in the learned helplessness model. Behav.
Pharmacol. 17: 703–713.
173
Borbély AA, Huston JP, Waser PG. 1975. Control of sleep states in the rat by short lightdark cycles. Brain Res. 95: 89-101.
Bourin M, Mocaër E, Porsolt R. 2004. Antidepressantlike activity of S20098
(agomelatine) in the forced swimming test in rodents: involvement of melatonin
and serotonin receptors. J. Psychiatry Neurosci. 29: 126–133.
Brager AJ, Ehlen JC, Castanon-Cervantes O, Natarajan D, Delisser P, Davidson AJ, Paul
KN. 2013. Sleep loss and the inflammatory response in mice under chronic
environmental circadian disruption. PLoS One. 8: e63752.
Buxton OM, Copinschi G, Van Onderbergen A, Karrison TG, Van Cauter E. 2000. A
benzodiazepine hypnotic facilitates adaptation of circadian rhythms and sleepwake homeostasis to an eight hour delay shift simulating westward jet lag.
Sleep. 23: 915-927.
Cambras T, Weller JR, Anglès-Pujoràs M, Lee ML, Christopher A, Díez-Noguera A,
Krueger JM, de la Iglesia HO. 2007. Circadian desynchronization of core body
temperature and sleep stages in the rat. Proc Natl Acad Sci U S A. 104: 76347639.
Castanon-Cervantes O, Wu M, Ehlen JC, Paul K, Gamble KL, Johnson RL, Besing RC,
Menaker M, Gewirtz AT, Davidson AJ. 2010. Dysregulation of inflammatory
responses by chronic circadian disruption. J Immunol. 185: 5796-5805.
Czeisler CA, Zimmerman JC, Ronda JM, Moore-Ede MC, Weitzman ED. Timing of REM
sleep is coupled to the circadian rhythm of body temperature in man. Sleep. 2:
329-346.
Davis ME, Brewster ME. 2004. Cyclodextrin-based pharmaceutics: past, present and
future. Nat Rev Drug Discov. 3: 1023-1035.
de Bodinat C, Guardiola-Lemaitre B, Mocaër E, Renard P, Muñoz C, Millan MJ. 2010.
Agomelatine, the first melatonergic antidepressant: discovery, characterization
and development. Nat Rev Drug Discov. 9: 628-642.
Deacon S, Arendt J. 1996. Adapting to phase shifts, II. Effects of melatonin and
conflicting light treatment. Physiol Behav. 59: 675-682.
Depaulis A, van Luijtelaar ELJM. 2006. Genetic models of absence epilepsy in the rat.
In Pitkanen A, Schwartzkroin PA, Moshe SL (Eds) Models of seizures and
epilepsy. Elsevier Academic Press, San Diego, CA, pp. 233–248.
Descamps A, Rousset C, Millan MJ, Spedding M, Delagrange P, Cespuglio R. 2009.
Influence of the novel antidepressant and melatonin agonist/serotonin2C
receptor antagonist, agomelatine, on the rat sleep-wake cycle architecture.
Psychopharmacology (Berl). 205: 93-106.
174
Dodd AN, Salathia N, Hall A, Kévei E, Tóth R, Nagy F, Hibberd JM, Millar AJ, Webb AA.
2005. Plant circadian clocks increase photosynthesis, growth, survival, and
competitive advantage. Science. 309: 630-633.
Drinkenburg WH, Coenen AM, Vossen JM, van Luijtelaar EL. 1991. Spike-wave
discharges and sleep-wake states in rats with absence epilepsy. Epilepsy Res 9:
218–224.
Duchene D, Vaution C, Glomot F. 1986. Cyclodextrin, their value in pharmaceutical
technology. Drug Development & Industrial Pharmacy, 12: 2193-2215.
Durazzo TS, Spencer SS, Duckrow RB, Novotny EJ, Spencer DD, Zaveri HP. 2008.
Temporal distribution of seizure occurrence from various epileptogenic regions.
Neurology 70: 1265–1271.
Fisher SP, Davidson K, Kulla A, Sugden D. 2008. Acute sleep-promoting action of the
melatonin agonist, ramelteon, in the rat. J Pineal Res. 45: 125-132.
Golombek DA, Cardinali DP. 1993. Melatonin accelerates reentrainment after phase
advance of the light-dark cycle in Syrian hamsters: antagonism by flumazenil.
Chronobiol Int. 10: 435-41.
Goodwin GM, Emsley R, Rembry S, Rouillon F, Agomelatine Study Group. 2009.
Agomelatine prevents relapse in patients with major depressive disorder
without evidence of a discontinuation syndrome: a 24-week randomized,
double-blind, placebo-controlled trial. J. Clin. Psychiatry 70: 1128–1137.
Grassi-Zucconi G, Semprevivo M, Mocaer E, Kristensson K, Bentivoglio M. 1996.
Melatonin and its new agonist S-20098 restore synchronized sleep fragmented
by experimental trypanosome infection in the rat. Brain Res Bull. 39: 63-68.
Halasz P, Terzano MG, Parrino L. 2002. Spike-wave discharge and the microstructure
of sleep-wake continuum in idiopathic generalized epilepsy. Neurophysiol Clin
32: 38–53.
Hastings MH, Mead SM, Vindlacheruvu RR, Ebling FJ, Maywood ES, Grosse J. 1992.
Non-photic phase shifting of the circadian activity rhythm of Syrian hamsters:
the relative potency of arousal and melatonin. Brain Res. 591: 20-26.
Hofstra WA, Spetgens WPJ, Leijten FSS, van Rijen PC, Gosselaar P, van der Palen J, de
Weerd AW. 2009. Diurnal rhythms in seizures detected by intracranial
electrocorticographic monitoring: an observational study. Epilepsy Behav 14:
617–621.
Huber R, Deboer T, Schwierin B, Tobler I. 1998. Effect of melatonin on sleep and brain
temperature in the Djungarian hamster and the rat. Physiol Behav. 65: 77-82.
Illnerová H, Trentini GP, Maslova L. 1989. Melatonin accelerates reentrainment of the
circadian rhythm of its own production after an 8-h advance of the light-dark
cycle. J Comp Physiol A. 166: 97-102.
175
Knutsson A. 2003. Health disorders of shift workers. Occup Med (Lond). 53: 103-108.
Kopp C, Vogel E, Rettori MC, Delagrange P, Guardiola-Lemaitre B, Misslin R. 1998.
Effects of a daylight cycle reversal on locomotor activity in several inbred strains
of mice. Physiol Behav 63: 577–585.
Koster-van Hoffen GC, Mirmiran M, Bos NP, Witting W, Delagrange P, GuardiolaLemaitre B. 1993. Effects of a novel melatonin analog on circadian rhythms of
body temperature and activity in young, middle-aged, and old rats. Neurobiol
Aging. 14: 565-569.
Lancel M, Kerkhof GA. 1989. Effects of repeated sleep deprivation in the dark- or lightperiod on sleep in rats. Physiol Behav. 45:289-297.
Langebartels A, Mathias S, Lancel M. 2001. Acute effects of melatonin on spontaneous
and picrotoxin-evoked sleep-wake behaviour in the rat. J Sleep Res. 10: 211217.
Lapin IP, Mirzaev SM, Ryzov IV, Oxenkrug GF. 1998. Anticonvulsant activity of
melatonin against seizures induced by quinolinate, kainate, glutamate, NMDA,
and pentylenetetrazole in mice. J Pineal Res. 24: 215-218.
Lee ML, Swanson BE, de la Iglesia HO. 2009. Circadian timing of REM sleep is coupled
to an oscillator within the dorsomedial suprachiasmatic nucleus. Curr Biol 19:
848–852.
Lemmer B, Kern RI, Nold G, Lohrer H. 2002. Jet lag in athletes after eastward and
westward time-zone transition. Chronobiol Int. 19: 743-764.
Loo H, Hale A, D’haenen H. 2002. Determination of the dose of agomelatine, a
melatonergic agonist and selective 5-HT2C antagonist, in the treatment of
major depressive disorder: a placebo-controlled dose range study. Int. Clin.
Psychopharmacol. 17: 239–247.
Martinet L, Guardiola-Lemaitre B, Mocaer E. 1996. Entrainment of circadian rhythms by
S-20098, a melatonin agonist, is dose and plasma concentration dependent.
Pharmacol Biochem Behav. 54: 713-718.
Monnaert V, Tilloy S, Bricout H, Fenart L, Cecchelli R, Monflier E. 2004. Behavior of
alpha-, beta-, and gamma-cyclodextrins and their derivatives on an in vitro
model of blood-brain barrier. J Pharmacol Exp Ther. 310: 745-751.
Mrosovsky N. Masking: history, definitions, and measurement. Chronobiol Int. 16: 415429.
Murphy HM, Wideman CH, Nadzam GR. 1998. The role of vasopressin in modulating
circadian rhythm responses to phase shifts. Peptides. 19: 1191-1208.
Musshoff U, Speckmann EJ. 2003. Diurnal actions of melatonin on epileptic activity in
hippocampal slices of rats. Life Sci. 73: 2603-2610.
176
Nagano M, Adachi A, Nakahama K, Nakamura T, Tamada M, Meyer-Bernstein E, Sehgal
A, Shigeyoshi Y. 2003. An abrupt shift in the day/night cycle causes
desynchrony in the mammalian circadian centre. J Neurosci 23: 6141–6151.
Ovchinnikov A, Lüttjohann A, Hramov A, van Luijtelaar G. (2010). An algorithm for
real-time detection of spike-wave discharges in rodents. J Neurosci Methods.
194: 172-178.
Papp M, Gruca P, Boyer PA, Mocaër E. 2003. Effect of agomelatine in the chronic mild
stress model of depression in the rat. Neuropsychopharmacology 28: 694–703.
Pavlova MK, Shea SA, Bromfield EB. 2004. Day/night patterns of focal seizures.
Epilepsy Behav 5: 44–49.
Pittendrigh CS. 1981. Circadian systems: entrainment. In Handbook of Behavioral
Neurobiology Vol 4, Biological Rhythms, Eds. Aschoff J. Plenum Press, New York.
pp 95-124.
Pytel M, Mercik K, Mozrzymas JW. 2006. Interaction between cyclodextrin and neuronal
membrane results in modulation of GABA(A) receptor conformational
transitions. Br J Pharmacol. 148: 413-422.
Quigg M, Straume M, Menaker M, Bertram EH III. 1998. Temporal distribution of partial
seizures: comparison of an animal model with human partial epilepsy. Ann
Neurol 43: 748–755.
Reddy AB, Field MD, Maywood ES, Hastings MH. 2002. Differential resynchronisation of
circadian clock gene expression within the suprachiasmatic nuclei of mice
subjected to experimental jet lag. J Neurosci 22: 7326–7330.
Redman JR, Guardiola-Lemaitre B, Brown M, Delagrange P, Armstrong SM. 1995. Dose
dependent effects of S-20098, a melatonin agonist, on direction of reentrainment of rat circadian activity rhythms. Psychopharmacology (Berl). 118:
385-390.
Reinberg A, Vieux N, Ghata J, Chaumont AJ, LaPorte A. 1979. Amplitude of the oral
temperature circadian rhythm in relation to the ability to phase shift circadian
rhythms of shift workers. Chronobiol. 6: 57-63.
Sei H, Kiuchi T, Chang HY, Morita Y. 1992. Effects of an eight-hour advance of the lightdark cycle on sleep-wake rhythm in the rat. Neurosci Lett. 137: 161-164.
Sei H, Kiuchi T, Chang HY, Seno H, Sano A, Morita Y. 1994. Response of the sleep-wake
rhythm to an 8-hour advance of the light-dark cycle in the rat. Chronobiol Int.
11: 293-300.
Sharma VK, Singaravel M, Subbaraj R, Chandrashekaran MK. 1999. Timely
administration of melatonin accelerates reentrainment to phase-shifted lightdark cycles in the field mouse Mus booduga. Chronobiol Int. 16: 163-170.
177
Shu HJ, Eisenman LN, Jinadasa D, Covey DF, Zorumski CF, Mennerick S. 2004. Slow
actions of neuroactive steroids at GABAA receptors. J Neurosci. 24: 6667-6675.
Smyk
MK, Coenen A, Lewandowski MH, van Luijtelaar G. 2012.
desynchronization facilitates seizures. Epilepsia. 53: 1511-1518.
Internal
Smyk MK, Coenen AM, Lewandowski MH, van Luijtelaar G. 2011. Endogenous rhythm
of absence epilepsy: relationship with general motor activity and sleep-wake
states. Epilepsy Res. 93: 120-127.
Takamure M, Murakami N, Takahashi K, Kuroda H, Etoh T. 1991. Rapid reentrainment
of the circadian clock itself, but not the measurable activity rhythms to a new
light-dark cycle in the rat. Physiol Behav 50: 443–449.
Tobler I, Jaggi K, Borbély AA. 1994. Effects of melatonin and the melatonin receptor
agonist S-20098 on the vigilance states, EEG spectra, and cortical temperature
in the rat. J Pineal Res. 16: 26-32.
Tolmacheva EA, van Luijtelaar G. 2007. Absence seizures are reduced by the
enhancement of GABA-ergic inhibition in the hippocampus in WAG/Rij rats.
Neurosci Lett. 416: 17-21.
Tresguerres JA, Ariznavarreta C, Granados B, Martín M, Villanúa MA, Golombek DA,
Cardinali DP. 2001. Circadian urinary 6-sulphatoxymelatonin, cortisol excretion
and locomotor activity in airline pilots during transmeridian flights. J Pineal Res.
31: 16-22.
van den Heuvel CJ, Ferguson SA, Macchi MM, Dawson D. 2005. Melatonin as a
hypnotic: con. Sleep Med Rev. 9: 71-80.
van Luijtelaar EL, Coenen AM. 1986. Two types of electrocortical paroxysms in an
inbred strain of rats. Neurosci Lett 70:3 93–397.
van Luijtelaar ELJM, Coenen AML. 1988. Circadian rhythmicity in absence epilepsy in
rats. Epilepsy Res 2: 331–336.
van Reeth O, Olivares E, Turek FW, Granjon L, Mocaer E. 1998. Resynchronisation of a
diurnal rodent circadian clock accelerated by a melatonin agonist. Neuroreport.
9:1901-1905.
van Reeth O, Weibel L, Olivares E, Maccari S, Mocaer E, Turek FW. 2001. Melatonin or
a melatonin agonist corrects age-related changes in circadian response to
environmental stimulus. Am J Physiol Regul Integr Comp Physiol. 280: 582591.
Wang F, Li J, Wu C, Yang J, Xu F, Zhao Q. 2003. The GABA(A) receptor mediates the
hypnotic activity of melatonin in rats. Pharmacol Biochem Behav. 74: 573-578.
178
Wang MD, Wahlström G, Bäckström T. 1997. Pregnenolone sulphate and pregnenolone
do not interact with 5 beta-pregnanolone- and hexobarbitone-induced
anaesthesia in the rat. Br J Anaesth. 78: 328-331.
Waterhouse J, Jones K, Edwards B, Harrison Y, Nevill A, Reilly T. 2004. Lack of evidence
for a marked endogenous component determining food intake in humans during
forced desynchrony. Chronobiol Int. 21: 445-468.
Waterhouse J, Reilly T, Atkinson G, Edwards B. 2007. Jet lag: trends and coping
strategies. Lancet. 369: 1117-1129.
Wegmann HM, Gundel A, Naumann M, Samel A, Schwartz E, Vejvoda M. 1986. Sleep,
sleepiness, and circadian rhythmicity in aircrews operating on transatlantic
routes. Aviat Space Environ Med. 57: B53-64.
Wever R. 1966. The duration of re-entrainment of circadian rhythms after phase shifts
of the Zeitgeber. A theoretical investigation. J Theor Biol 13: 187–201.
Winget CM, DeRoshia CW, Markley CL, Holley DC. 1984. A review of human
physiological and performance changes associated with desynchronosis of
biological rhythms. Aviat Space Environ Med. 55: 1085-1096.
Yamazaki S, Numano R, Abe M, Hida A, Takahashi R, Ueda M, Block GD, Sakaki Y,
Menaker M, Tei H. 2000. Resetting central and peripheral circadian oscillators
in transgenic rats. Science 288: 682–685.
Yan L. 2011. Structural and functional changes in the suprachiasmatic nucleus following
chronic circadian rhythm perturbation. Neuroscience. 183: 99-107.
Yildirim M, Marangoz C. 2006. Anticonvulsant effects of melatonin on penicillin-induced
epileptiform activity in rats. Brain Res. 1099: 183-188.
Zhdanova IV. 2005. Melatonin as a hypnotic: pro. Sleep Med Rev. 9: 51-65.
179
CHAPTER 6
General Discussion
Enhancing/inhibiting influences affecting the occurrence of
absence seizures may be created internally within the organism (state
of vigilance, hormonal environment) or be delivered from the outside
(light stimulation, stress, chemical agents) (Wolf and Goosses, 1986;
Baykan et al., 2005; Drinkenburg et al., 1991; Lannes et al., 1985;
Halász et al., 2002; Grünewald et al., 1992; van Luijtelaar et al., 2001;
Tolmacheva et al., 2012; Somerville, 2009). The aim of the present
thesis was to establish the contribution of endogenous and exogenous
factors to the occurrence of absence seizures. In particular, the
endogenous factors that were investigated were the circadian timing
system and sleep-wake states, the external contributors were
represented by the light-dark cycle and a melatonin agonist.
In Chapter 2, the temporal relationship between the commonly
distinguished sleep-wake states (active and passive wakefulness, light
and deep slow-wave sleep, REM sleep) and the occurrence of SWDs
was investigated for the whole 24 h cycle and for the light and the dark
phase separately. Endogenous circadian control was examined in
Chapter 3, in which WAG/Rij rats were exposed to a constant condition
paradigm.
Chapter
4
describes
consequences
of
circadian
misalignment caused by an abrupt shift in the light-dark cycle. In
Chapter 5, first the effects of various doses of acute administration of
the melatonin agonist agomelatine on sleep were investigated, next it
was attempted to ameliorate the consequences of a similar shift in the
light-dark cycle by daily, timed administration of this melatonin
agonist.
Main findings of the thesis:
I.
SWDs and sleep-wake states are temporally related. Regardless
of the method of data processing (correlations and crosscorrelations across 24 h versus analysis of 5 s preceding the
183
occurrence of SWDs), the relationship between SWDs and deep
slow-wave sleep is always negative, while relationship with
active wakefulness and light slow-wave sleep varies depending
on the approach chosen.
II.
The character of SWDs – sleep-wake states relationships is
determined by the phase of the light-dark cycle. In the light
phase, the relationships are unidirectional, which means that
the duration of a particular sleep-wake state may serve as a
reliable predictor of the duration of SWD activity. In the dark
phase however, the relationships are bidirectional. In addition
to predictive value of sleep-wake states for this duration,
absence seizures themselves influence the duration of sleepwake states.
III.
SWDs – sleep-wake states relationships estimated on a
momentary level (analysis of 5 s EEG preceding and following
the occurrence of SWDs) is independent of the state of the
circadian timing system: entrained versus fee-running.
IV.
Rhythmic occurrence of SWDs observed during entrainment to
the 12:12 light-dark cycle persists also under constant dim light
condition, which strongly suggests that the rhythm is generated
endogenously. In the absence of environmental time cues, the
rhythm of SWDs free-runs with a period length of about 23 h
and desynchronizes from the rhythm of general motor activity.
V.
Rapid change in the timing of the light phase of the 12:12 lightdark cycle results in circadian desynchronization between
rhythms of SWDs, general motor activity and sleep-wake
states. Different rhythms re-entrain with different speed,
although some rhythms seem coupled. Active wakefulness and
deep slow-wave sleep are thought to share a common circadian
oscillator since they re-synchronize together. The same is
thought for SWDs and light slow-wave sleep.
184
VI.
Timed administration of agomelatine, agonist of melatonin MT 1
and MT2 receptors, neither affect the occurrence and duration
of SWDs and sleep-wake states during entrainment to the 12:12
light-dark cycle, nor accelerates the speed of re-entrainment of
these rhythms after the phase shift of the photoperiod.
VII.
Alterations
in
circadian
physiology
such
as
internal
desynchronization are accompanied by exacerbation of epileptic
activity. Changes in the duration of sleep-wake states, deep
slow-wave sleep in particular, seems to be responsible for
increased number and duration of seizures.
Relationship between SWDs and sleep-wake states
The first series of questions addressed by the present thesis
regarded the relationship between absence seizures and sleep-wake
states. It was aimed to establish whether the relationship is constant
across 12:12 light-dark cycle and whether it is modulated by the state
of the circadian timing system: entrained versus free-running.
Vigilance is one of the major factors affecting the incidence of
absence seizures in humans and in animal models (Drinkenburg et al.,
1991; Lannes et al., 1985; Halász et al., 2002). SWDs in WAG/Rij rats
occur mainly during passive wakefulness and light slow-wave sleep,
while they are hardly preceded by deep slow-wave sleep and REM sleep
(Drinkenburg et al., 1991). In addition, cortical electrical stimulation
induces SWD-like afterdischarges mainly during drowsiness, and much
less during active wakefulness and slow wave sleep (Lüttjohann and
van Luijtelaar, 2012). Relationships described above have been
established by inspection of 5 s epochs directly preceding the onset of
SWDs (Drinkenburg et al., 1991). This approach has been called
“momentary”.
185
In Chapter 2, a link between absence seizures and sleep-wake
states was investigated in a different way. Correlations and crosscorrelations between SWDs and all vigilance states were calculated
over the 24 h and separately for the light and the dark phase. While
the outcomes of the study agreed upon the role of deep slow-wave
sleep and, to lesser extent, passive wakefulness and REM sleep, the
results where different regarding active wakefulness and light slowwave sleep. Contrary to inhibitory effect revealed by the momentary
approach, active wakefulness was positively correlated with the
incidence of SWDs. Light slow-wave sleep, found previously to enhance
generation of SWDs, showed negative correlation with the occurrence
of seizures. A similar discrepancy regarding active wakefulness has
been described in inbred Fisher and Brow Norway rats exhibiting
generalized neocortical high voltage spike-wave spindles (HVS), an
equivalent of SWDs (Jandó et al., 1995). Data analyzed in 10 min bins
showed that the peak of HVS incidence was preceded by a peak in
activity occurring 10 min earlier. Spectral analysis of delta band in 2 s
epochs of EEG directly preceding HVS revealed however, that HVS do
not occur during deep slow-wave sleep or a highly aroused state (Jandó
et al., 1995). The different methods of data analysis can be considered
as the source of this contradiction. Cross-correlations provide
information about the global organization of sleep-wake states and the
temporal relationships between the different sleep-wake states, while
momentary factors provide information about the state just before
SWDs. Active wakefulness preceding the occurrence of SWDs in a time
range of 25 – 30 min found in the experiment described in the Chapter
2 is not surprising given the fact that one of the most frequent state
transitions across rat sleep-wake cycle is passive wakefulness → active
wakefulness → passive wakefulness changeover (Gervasoni et al.,
2004). SWDs are positively correlated with this particular type of
transition as it was shown previously and then confirmed in the study
186
described in Chapter 2. Moreover, the change of active wakefulness
into passive wakefulness creates one of the most convenient neuronal
environments for the occurrence of SWDs. The sequence: passive
wakefulness → slow-wave sleep → intermediate stage → REM sleep →
passive wakefulness (Gervasoni et al., 2004) explains also negative
cross-correlations between SWDs and both slow-wave sleep states. As
mentioned previously, SWDs rarely occur during REM sleep, which
usually follows slow-wave sleep. In Chapter 2 differentiation between
two slow-wave sleep stages was also made. Both: light → deep slowwave sleep and deep → light slow-wave sleep changeovers were found
to be negatively correlated with the incidence of absence seizures
across 24 h.
Results obtained in the experiment presented in Chapter 2
revealed also that temporal relationships between SWDs and sleepwake states are modified by the phase of the 12:12 light-dark cycle.
In the light phase, relationships are stronger and unidirectional, the
duration of a particular sleep-wake state predicts the duration of SWDs.
In the dark phase however, the presence of SWDs is influenced by, but
also influences the duration of sleep-wake states. Clinical and
experimental data provide evidence for a reciprocal relationship
between sleep and epilepsy (Shouse et al., 2000; Matos et al., 2010).
Data collected in a population of patients with temporal lobe epilepsy
showed pronounced changes in the amount of sleep states (Bazil et al.,
2000). The degree to which the structure and efficiency of sleep was
changed was dependent on the time of seizures occurrence (day vs.
night). Daytime seizures reduced REM sleep period, while nighttime
seizures, besides REM sleep suppression, decreased stage 2 and 4
slow-wave sleep, sleep efficiency, and increased the amount of stage
1 slow-wave sleep. Further reduction of REM sleep was found when
seizures occurred before the first REM episode (Bazil et al., 2000).
Similar changes have
been
reported
187
for
generalized
epilepsy.
Polysomnographic studies in a group consisted of childhood absence
epilepsy, juvenile absence epilepsy and juvenile myoclonic epilepsy
patients revealed a decrease in sleep efficiency and deep slow-wave
sleep (stage 4, a human equivalent of deep slow-wave sleep), while
wakefulness time, the percentage of stage 1 slow-wave sleep and REM
sleep latency were found to be increased (Baretto et al., 2002; Maganti
et al., 2005). A time-of-a-day effect has been found in amygdalakindled rats (Yi et al., 2012). Whether homeostatic or circadian
mechanisms of the sleep-wake regulation were affected was dependent
of the time across the 12:12 light-dark cycle during which the rats were
kindled. Although the time distance between kindling and changes in
the percentage of sleep states and duration of such changes were
different, a common finding was the decrease in both slow-wave and
REM sleep (Yi et al., 2012).
Common findings for the studies cited above and some of the
studies performed on the WAG/Rij model are: the presence of the
circadian factor determining the timing of aberrations in the sleep cycle
and the reduction of REM sleep. 6-month-old WAG/Rij rats endowed
with more SWDs have a shorter sleep cycle in general, and a shorter
sleep cycle particularly at the end of the light phase in comparison to
4-month-old strain counterpart, which have significantly less SWDs
than older rats (van Luijtelaar and Bikbaev, 2007). It might be
speculated that a presence and persistence of a large number of SWDs
practically during the whole dark phase may influence sleep variables
at some other point of the 24 h cycle. The end of the light phase is the
time when light slow-wave sleep predominates. Moreover, as shown
by Drinkenburg and co-workers (1991) and also in Chapter 3, SWDs
exert an awaking effect since active and passive wakefulness are the
most common states following the occurrence of SWDs. Taken
together, at the end of the light period when sleep is light and SWDs
188
with awakening properties increase in incidence, the sleep-wake cycle
of WAG/Rij rats is rather fragile for interruptions.
While temporal relationships between SWDs and sleep-wake
states were found to be modified by the phase of the light-dark cycle,
momentary factors stayed unchanged regardless of whether the
animals were entrained or not. Chapter 3 describes the experiment
investigating the potential circadian character of the rhythm of absence
seizures. Circadian parameters of the rhythm were assessed in 12:12
light-dark cycle and then compared with those obtained in constant
dim light. With regard to well-documented behavior in the constant
conditions, rhythm of general motor activity was chosen as a reference
for the free-running state of the circadian timing system. Additionally
to rhythms parameters, the momentary approach was used to examine
SWDs – sleep-wake states relationships. The period length, mean,
amplitude and robustness of the free-running SWDs rhythm changed
substantially; however, the relationship with sleep-wake states
remained the same. The rhythms of absence seizures and general
motor activity desynchronized from each other, as indicated by a
different period length in constant conditions. Nonetheless, temporal
desynchronization had no effect on seizure generation. Passive
wakefulness remained the most favorable state for the occurrence of
SWDs, whereas active wakefulness and REM sleep were the least
encouraging. It might be concluded that while the biological clock
governs the timing of the physiological or, as in this case, pathological
processes, it is not able to affect the primary mechanisms of the
generation of SWDs. Although temporal relationships between different
cycles may change as a result of circadian desynchronization, corticothalamo-cortical mechanisms of SWDs generation does not seem to be
affected by such manipulations. To illustrate the point: Musshoff and
Speckmann (2003) investigated time-of-a-day effect on epileptic
activity in hippocampal slices of rats. Epileptiform field potentials (EFP)
189
were elicited by omitting magnesium from the bath solution or by
adding bicuculline, a GABAA receptor antagonist, to the bath control
solution. Despite a difference in the latency of the occurrence of EFP
between slices prepared during the light and the dark phase, an
imbalance between inhibitory GABA and excitatory neurotransmission
mediated by NMDA receptors was a necessary condition for the
generation of EFP (Musshoff and Speckmann, 2003). Similarly in the
present study, sleep-wake states classified by the morphology and
frequency of brain waves captured by the EEG reflect the state of the
cortico-thalamo-cortical circuits. SWDs are generated under conditions
of hyperexcitable cortical focus and the thalamus in an oscillatory
mode, both criteria were fulfilled more frequently during passive
wakefulness, light slow-wave sleep and transitional, unstable periods.
Therefore, the timing of sleep-wake states and the temporal
relationship between them and absence seizures (both variable under
the control of the circadian timing system) may change, however,
seizures will not be generated unless the momentary criteria of the
particular state of the cortico-thalamo-cortical network are satisfied.
Endogenous character of the rhythm of SWDs
The second set of questions raised by the present thesis
regarded the character of the cyclic occurrence of absence seizures.
The possible endogenous origin of the rhythm of SWDs was
investigated by means of a constant condition paradigm. The paradigm
assumes that endogenous character of the rhythm is revealed after
elimination of Zeitgebers. During the protocol, an organism is exposed
to either one of the two lightning conditions: constant darkness or
constant light, with other periodic environmental influences or
periodically occurring factors being abolished (Duffy and Dijk, 2002;
Jud et al., 2005). The method is often used in animal research to
190
quantify circadian phenotypes (e.g. to determine parameters of freerunning circadian rhythms) (Ruby et al., 2002; Bittman et al., 2007).
In the experiment described in Chapter 3, 12:12 light-dark cycle was
replaced by constant dim light. Circadian parameters of the rhythm of
SWDs and general motor activity (serving as an indicator of the state
of the circadian timing system) were assessed for 10 days before and
10 days after removal of the light-dark cycle. The rhythmic occurrence
of SWDs, described previously by van Luijtelaar and Coenen (1988)
and confirmed by the present study, persisted also in the absence of
this powerful Zeitgeber, however the circadian parameters of the SWDs
rhythm changed significantly. The period length decreased to ~ 23 h,
which made the rhythm a free-running one, mean and amplitude
increased and the robustness decreased. These results showed that the
presence of entrainment provided by the light-dark cycle is not
necessary for the cyclic expression of SWDs in WAG/Rij rats, therefore
it might be speculated that the rhythm is generated endogenously. The
presence of light and dark however, enhances this rhythmicity by
giving it a well-defined, distinct shape.
The constant condition paradigm, precisely, the constant
darkness variant, was used in the first study investigating the
endogenous character of the rhythm of epileptic seizures in a rat model
of human mesial temporal lobe epilepsy (MTLE) (Quigg et al., 2000).
The study showed that spontaneous limbic seizures in rats occurred in
a 24 h pattern regardless of the presence of the light-dark cycle.
Moreover, the phase relationship in the distribution of seizures was also
preserved: seizures occurring numerously in the light phase during
entrainment were also dominating in the corresponding, passive phase
of the constant darkness conditions (Quigg et al., 2000).
Outcomes
of
both
studies
are
very
suggestive
of
the
endogenous origin of MTL and absence epilepsy rhythms however; it
should be kept in mind that the light-dark cycle is not the only factor
191
capable of entraining or masking. In human circadian research,
removal of environmental Zeitgebers is thought to be insufficient in the
assessment of the circadian phase. Considering that rhythms are often
masked by behavioral states (e.g. temperature rhythms is masked by
activity/sleep-wake cycle (Weinert and Waterhouse, 1998; Waterhouse
et al., 2000), two methods were developed to remove these influences.
Forced desynchronization and constant routine protocols aim at
unmasking circadian rhythms by distributing sleep episodes evenly
across all circadian phases or by eliminating periodic changes in
behavior, respectively. This is done by scheduling subjects to sleepwake cycle that is far outside the range of entrainment (the former),
or by means of sleep deprivation, constant posture, identical meals
distributed across the circadian cycle additionally to constant external
environment (the latter) (Czeisler et al., 1999; Duffy et al., 2002;
Hofstra and de Weerd, 2008).
A forced desynchronization protocol was used to identify
circadian patterns in the occurrence of epileptic seizures in humans
(Pavlova et al., 2009). The number of days required to fulfill the
demands of the protocol depends on the length of an artificial day
which must be outside the range of entrainment. The longer the day
(e.g. 28 h), the longer the duration of the protocol to obtain even
distribution of sleep-wake states across all circadian phases. The
protocol used by the authors was a short day protocol (5 h 40 min) and
was completed after 3 days. The study suggested a circadian variation
in
the
occurrence
of
interictal
epileptiform
discharges
(IEDs),
independently of the sleep-wake cycle. It also showed that a strict
circadian protocol is feasible and safe in patients with generalized
epilepsies (Pavlova et al., 2009).
The study described in Chapter 2 did not investigate possible
behavioral masking from the side of the sleep-wake states. The
relationship
between
SWDs
and
192
vigilance
is
well
established
(Drinkenburg et al., 1991) but only by the momentary approach and
not from the rhythm point of view. Therefore, a forced desynchrony
protocol, e.g. in the form proposed by de la Iglesia and coworkers
(2004), would be a valuable complement to the study devoted to
circadian nature of the rhythm of absence seizures.
Internal desynchronization and epilepsy
One of the objectives of the present thesis was to investigate
consequences of rapid, change in the 12:12 light-dark cycle, which are
known to cause serious disruptions in the circadian timing system.
Chapters 3 to 5 describe behavior of absence seizures during internal
desynchronization and re-entrainment processes after an 8 h light
phase delay. An increase in the number and mean duration of SWDs
was observed regardless of the cause of desynchronization (constant
dim light or a light phase delay). Based on a well-characterized
relationship between absences and sleep-wake states (Drinkenburg et
al., 1991), this elevation was attributed to changes in the amount of
deep slow-wave sleep (a decrease) and passive wakefulness (an
increase). Results of the experiments described in Chapters 4 and 5
(the phase delay paradigm) point to a phase-related redistribution of
SWDs so that, after the phase shift, they occurred frequently on an
unusual time with respect to their normal 24 h rhythm. The outcomes
support the hypothesis that disturbances in sleep-wake cycle are
responsible for aggravation of epileptic activity.
Signs of circadian disturbances have been identified in a wide
range of medical conditions including neurodegenerative disorders
(Videnovic et al., 2014), affective disorders (Benca et al., 2009) and
cancer (Erren et al., 2008). Moreover, members of 24/7 society
challenge their internal clock by irregular work/leisure schedules and
frequent jet lag. Recurrent disruptions in the circadian organization
193
entail serious health consequences (Knutsson, 2003). There are large
interindividual
differences
regarding
tolerance
to
internal
desynchronization and shift work (Reinberg et al., 2007). Subjects
defined as allochronic report neither complaints, nor detectable
medical symptoms associated with internal desynchronization, this
makes them well-tolerant to non-standard working hours. People
defined as dyschronic suffer from circadian desynchronization and
therefore, show poor tolerance to shift work (Reinberg et al., 2007;
Reinberg and Ashkenazi, 2008). Although a small resistance to internal
desynchronization is associated with a small circadian amplitude and a
period length differing substantially from 24 h, poor tolerance cannot
be assessed a priori (Reinberg and Ashkenazi, 2008). Only exposition
to challenging conditions may answer the question about one’s
susceptibility. However, evidence collected in a population of shift
workers, airline crew members attending transmeridian flights and
patients experiencing difficulties after rapid changes of time zones or
during the course of shift work, may be helpful in predicting possible
health consequences. This knowledge may be also used to create
recommendations for those considering commencement of shift work,
or for patients that are worried whether a particular work schedule
might aggravate seizures, or for distant air travelling that may result
in exacerbating symptoms.
The number of passengers traveling by air is constantly
growing. European Commission reports 842 million people using air
transport within borders of 28 EU member countries in 2013 (Eurostat,
2014). At the same time, WHO estimates that 50 million people
worldwide suffer from epilepsy, including 6 million European citizens
and 300 000 new cases diagnosed each year in this geographical area
(Mitchell et al., 2011). People with relatively high seizure frequency (4
seizures per month) have been described as more likely to avoid air
travel, nevertheless, an increased number of individuals with active
194
epilepsy traveling by air might be expected (Trevorrow et al., 2006).
Seizures are second to dizziness/vertigo among neurological symptoms
reported during flights (Sirven et al., 2002). Alonso-Cánovas and coworkers showed that 41 of 77 patients (53.2%) who developed
neurological symptoms within the flight or maximum 6 h after landing,
experienced seizures (2011), while only 13 passengers from that group
had been diagnosed with epilepsy before entering the study. Possible
triggers for seizure activity were found in 12 of them and the triggers
were missing one or more doses of antiepileptic drugs (AEDs) during
days preceding the flight, sleep deprivation and usage of recreational
drugs (Alonso-Cánovas et al., 2011).
Only one study to date attempted to investigate the effects of
air travel on seizure frequency in a population of people with epilepsy
(Trevorrow, 2006). The author reported increased number of seizures
during post-flight week, with the seizure rate almost doubled and
positively correlated with pre-flight values. The direction and distance
of flight were also investigated, however no significant differences were
found. Although not directly investigated, post-flight sleep disruption
has been hypothesized to be a major cause of the increase of seizures
frequency found in a group of individuals with epilepsy after air travel
(Trevorrow, 2006).
Travel recommendations given to passengers dealing with
epilepsy and travelling across multiple time zones usually comprise
avoiding sleep deprivation, keeping the dosage and timing of
medications approximately the same as before the flight, and taking
two separate supplies of medications: one for the checked and the
other for the hand luggage (Aerospace Medical Association Medical
Guidelines Task Force, 2003; www.britishairways.com). Here it is
shown that also in case of absence epilepsy precautions regarding sleep
should be undertaken or at least considered. Experiments described in
the present thesis were performed in naive WAG/Rij rats, with no
195
pharmacological interventions to reduce absence seizures, thus
speculating about necessity of maintaining pre-shift drug schedule was
not investigated. The issue, possible to be tested experimentally, may
be of a particular significance for patients.
Besides
jet
lag,
negative
consequences
of
circadian
misalignment are a common companion of shift workers. There is no
consistency with regard to people with epilepsy performing shift work.
Some of the sources advise the patients not to work during the night
shift due to possible seizure enhancing effects of sleep deprivation,
violation of AEDs schedules or psychosocial factors (LaDou, 1982;
Koller, 1996; Costa, 2003). The other suggests that there is no
evidence of significant risk for epileptics engaged in non-standard
working hours (Nicholson and Auria, 1999). Data regarding evaluation
of sickness, accident and work records in people with epilepsy hired by
British Steel Corporation compiled by Dasgupta and co-workers (1982)
showed that 20 out of 32 workers with epilepsy were eligible for and
did shift work (Dasgupta et al., 1982). The 12 who did not were not
willing to undertake such a commitment. This was the only significant
difference found between workers suffering from epilepsy and healthy
controls. The study, however, did not investigate the source of the shift
work reluctance in this particular group of employees.
Experiments described in the Chapters 3 to 5 describe
consequences of internal desynchronization for the course of absence
epilepsy in WAG/Rij rats. The results allow some presumptions
regarding the occurrence of seizures during circadian disruption,
however, none of the studies used a repeated phase shifts paradigm.
Forcing epileptic animals to be active during abnormal activity hours or
scheduling repeated phase shifts combined with different schedules
and dosage of AEDs would enrich the present knowledge of the course
of epilepsy during shift work.
196
Melatonin and epilepsy
The last but not least issue investigated in the present thesis
was a response of absence seizures and sleep-wake states to acute
and chronic treatment with the melatonin agonist agomelatine during
stable entrainment and after a phase shift. Agomelatine was chosen
according to its phase-shifting abilities, modifying influence on sleepwake states in rodents and proven antidepressant activity (Redman et
al., 1995; Martinet et al., 1996; Loo et al., 2002; Papp et al., 2003;
Descamps et al., 2009; Goodwin 2009). It was assumed that the
compound may exert a beneficial effect on absence seizures and the
speed of re-synchronization of various sleep-wake and SWDs rhythms
by ameliorating sleep disturbances caused by epileptic phenotype of
WAG/Rij rats and the phase shift, as well as through reduction of
depressive-like symptoms including changes in sleep-wake parameters
associated with epileptic activity in this model (van Luijtelaar and
Bikbaev, 2007, Sarkisova et al., 2010; Sarkisova and van Luijtelaar,
2011; van Luijtelaar et al., 2012). However neither acute nor chronic
treatment with agomelatine appeared effective and none of the
variables investigated in the experiment described in Chapter 5:
number and duration of SWDs, duration of sleep-wake states and
speed of re-entrainment after the phase shift was affected by
agomelatine.
The ability to regulate sleep and to protect cells from free
radicals may be reasons why melatonin might be of interest as a
potential AED. The first attempt to use the hormone in patients was
undertaken by Anton-Tay in 1971. Melatonin in a dose of 2 mg/day
was found to reduce seizure frequency and to normalize EEG of patients
(Anton-Tay et al., 1971; Anton-Tay, 1974). Since then many clinical
trials as well as basic studies towards potential therapeutic use of
melatonin in the treatment of epilepsy were conducted. Results
197
however, were contradictory: melatonin either increased (Sheldon,
1998; Sandyk et al., 1992), had no effect (Ross et al., 2002; Coppola
et al., 2004; Gupta et al., 2005; Elkhayat et al., 2010) or reduced the
number of seizures (Fauteck et al., 1999; Peled et al., 2001; Uberos et
al., 2011; Goldberg-Stern et al., 2012). Regardless of the outcomes,
in the majority of studies cited above melatonin treatment improved
sleep parameters, which, considering the negative impact of seizures
on sleep, appeared beneficial.
Studies
in
humans
with
epilepsy
have
obviously
some
limitations: the small sample size, the open label design (both the
researches
and
participants
know
which
treatment
is
being
administered), a presence of additional neurological deficits, AEDs
mono- or polytherapy. Basic investigations upon potential antiepileptic
effect of melatonin were conducted mainly on rodent models of
epilepsy and seizure models. Similarly to the outcomes of clinical trials,
conflicting results were obtained between and within the various
models. Melatonin was shown to be effective against seizures induced
by a wide range of chemical agents: quinolinic acid, N-methyl-Daspartate
(NMDA),
pentylenetetrazole
glutamate,
(PTZ),
and
kainic
penicillin
acid,
(Lapin
et
pilocarpine,
al.,
1998;
Bikjdaouene et al., 2003; Yildirim and Marangoz, 2006; Lima et al.,
2011; Tchekalarova et al., 2013; Yildirim et al., 2013). An acute,
anticonvulsive action of the hormone was described as: an increase of
the latency of the first seizure, a decrease of the duration and intensity
of the first seizure, attenuation of epileptic activity in general and a
decline of the mortality rate (Bikjdaouene et al., 2003; Yildirim and
Marangoz, 2006; Yildirim et al., 2013). Chronic melatonin treatment
decreased the frequency of seizures occurring spontaneously after
status epilepticus induced by kainic acid and pilocarpine (Lima et al.,
2011; Tchekalarova et al., 2013). Opposite to these findings, evidence
suggesting that melatonin exerts no or even pro-convulsive effect has
198
been found as well. Xu and Stringer (2008) reported no significant
changes of the latency to the forelimb clonus, seizure score or mortality
rate after melatonin pre-treatment in the PTZ model (Xu and Stringer,
2008). No changes of the seizure threshold were reported by Mares
and co-workers in flurothyl model (2013). Aggravation of epileptic
activity by melatonin was observed in CA1 pyramidal layer of the
hippocampus in vitro (Musshoff and Speckmann, 2003), while blockage
of hippocampal MT1b receptors delayed the onset of epileptic activity
induced by pilocarpine (Stewart and Leung, 2005). Phase-related
differences regarding the pro-epileptic effect of melatonin were found
in both studies and diurnal variation in density and sensitivity of
melatonin and GABAA receptors were speculated to be responsible for
the phase-related effects observed (Musshoff and Speckmann, 2003;
Stewart and Leung, 2005).
The effects of melatonin agonists in epilepsy were assessed with
two compounds: ramelteon and agomelatine. Ramelteon, registered as
melatonin-derived drug against insomnia (McGechan and Wellington,
2005), was found to increase the afterdischarge threshold and a
decreased afterdischarge duration in hippocampal-kindled rats, as well
as decrease the frequency of seizures in Kcna 1-null mice, an epilepsy
model showing spontaneous recurrent seizures. However, doses of the
compound used in the study: 30 and 100 mg/kg were far above a dose
of 8 mg/kg recommended for patients dealing with insomnia (FenoglioSimeone et al., 2009). Agomelatine has been tested in the following
models of epilepsy: PTZ, pilocarpine, picrotoxin, strychnine and
electroshock model, showing anticonvulsive action only after PTZ and
pilocarpine administration. Likewise ramelteon, the effective doses
used in the study: 50 and 75 mg/kg, exceeded the recommended dose
(25 mg/kg) (Aguiar et al., 2012).
The failure of agomelatine in preventing the occurrence of SWDs
in the present study might be attributed to relatively low doses of the
199
drug given to animals both during the acute and chronic experiment.
As shown above, anticonvulsive effect of melatonin and its analogues
was achieved by doses far outside physiological or recommended
therapeutic concentration. Agomelatine in 10 mg/kg, the highest dose
used in the present study, was however found to provide true
entrainment of the activity rhythm and to interfere with the sleep-wake
cycle in rats (Martinet et al., 1996; Descamps et al., 2009). It was
expected, that the compound would indirectly affect the incidence of
absence seizures by changes in the level of vigilance (promote sleep)
in the acute, and promote sleep and accelerate the speed of reentrainment in the chronic study. Apart from slightly disadvantageous
experimental design of the phase-shift experiment (timed stimulation
of melatonin receptors is more effective in phase advance rather than
phase delay paradigm), the lack of response to agomelatine might be
also a result of inter-strain differences in a density of melatonin
receptors and reactivity of melatonin system in the brain of WAG/Rij
rats. Significant rat strains differences in the excretion profile of 6sulphatoxymelatonin, a melatonin metabolite, plasma and pineal gland
melatonin concentration, and melatonin binding sites in the SCN have
been reported (Klante et al., 1999). Also other rodent strains differ in
the melatonin content (Goto et al., 1989). On the other hand, a
reduction of the amount of circulating melatonin by pinealectomy was
found to enhanced epileptogenesis in Wistar rats suggesting, that
decreased melatonin level may predispose to seizures (de Lima et al.,
2005). Comparative assessment of the melatonin physiology in
WAG/Rij rats has the potential to answer the following questions:
whether WAG/Rij rats differ in the melatonin content, receptor density,
their reactivity and a response to exogenous melatonin receptor
antagonists. If so, then it is likely that these differences participate in
the development of epileptic phenotype. Experimental phase advance
200
of the light-dark cycle would additionally extend the knowledge of the
consequences of circadian disturbances on epileptic activity.
Is the rhythm of absence seizures driven by a common
or distinct oscillator?
The results of the experiments investigating an endogenous
character of the rhythm of absence seizures and re-synchronization of
sleep-wake states and SWDs to the shifted photoperiod raised the
question about a circadian oscillator governing the rhythm of SWDs.
Internal desynchronization between the rhythm of absence seizures
and general motor activity described in Chapter 3 and confirmed in
Chapter 4 and 5, suggests distinct circadian mechanisms engaged in
regulation of these processes. Data complemented in Chapter 5 by the
analysis of four additional sleep-wake states revealed coupling
between active wakefulness and deep slow-wave sleep, and between
light slow-wave sleep and SWDs. The outcomes support the presence
of distinct oscillators for activity/deep slow-wave sleep and absences,
concurrently linking the latter to light slow-wave sleep. Persisting
rhythmicity in the occurrence of absence seizures together with
substantial changes in circadian parameters of the rhythm in constant
dim light, suggests dependency on the light-entrainable oscillator, the
SCN.
Two
well-defined
parts
of
the
SCN:
ventrolateral
and
dorsomedial, respectively, were found to govern different sleep-wake
states
and
behavioral
rhythms
(Cambras
et
al.,
2007).
The
dorsomedial SCN controls circadian oscillations of REM sleep and core
body temperature independently of the light-dark cycle information.
Rhythmic occurrence of motor activity and slow-wave sleep are
governed by both SCN divisions: dorsomedial and ventrolateral part
(Cambras et al. 2007). The latter is the recipient of the pathways from
the retina. Considering the marked contribution of the light-dark cycle
201
to the rhythmic occurrence of SWDs presented in Chapter 3 and SWDs
- REM disparity in the re-synchronization rate described in Chapter 5,
it might be assumed that the rhythm of absence seizures is governed
by both oscillators within the SCN. This assumption places the rhythm
in a group with active wakefulness and slow-wave sleep. Nonetheless,
the present results differentiate between these states and SWDs, and
also between both slow-wave sleep stages. Regarding the hierarchical
organization of the circadian timing system, it may be speculated that,
while common in the SCN, mechanisms governing rhythmical
expression or re-synchronization of rhythms investigated, diverge on
the level of dependent oscillators.
The output pathways by which the master circadian pacemaker
regulates the timing of particular sleep-wake states are not fully
established. Only the medial preoptic area (MPA), ventrolateral
preoptic nuclei (VLPO) and orexin-containing neurons in the posterior
hypothalamus were shown to receive direct projections from the SCN
(Leak and Moore, 2001; Novak and Nunez, 2000; Chou et al., 2002;
Abrahamson et al., 2000). Circadian information to the remaining
sleep-wake regulatory centers conveys through the following relay
nuclei:
the MPA,
dorsomedial
hypothalamic nuclei
(DMH)
and
subparaventricular zone (SPVZ) (Deurveilher and Semba, 2005).
Among structures directly innervated by the SCN, the VLPO seems to
be of a particular interest considering the epileptic phenotype of
WAG/Rij rats. A role of this hypothalamic nucleus in sleep physiology
is well established (Sherin et al., 1996; Szymusiak et al., 1998; Gaus
et al., 2002). Region-specificity with regard to two sleep stages was
found in a study investigating effects of bilateral, cell-specific lesion of
the VLPO on slow-wave and REM sleep (Lu et al., 2000). Loss of sleeppositive neurons in VLPO cell cluster correlated positively with a
decrease of the amount of slow-wave sleep and delta power (Lu et al,
2000). Such a parallel was not observed with regard to REM sleep
202
reduction, however when dorsal and medial-extended VLPO were
considered, the correlation between the number of cells and slow-wave
sleep (and delta) loss became apparent and significant (Lu et al.,
2000). Suntsova and co-workers (2007) reported a functional
deficiency of the VLPO sleep-promoting system in WAG/Rij rats
suggesting that absence seizures may develop as a result of insufficient
sleep initiation mechanisms (Suntsova et al., 2007). As mentioned
previously, the occurrence of SWDs is strongly and opposed related to
the occurrence of both slow-wave sleep stages: deep slow-wave sleep
inhibits
absences,
while
light
slow-wave
sleep
enhances
their
generation (Drinkenburg et al., 1991). Taken this relationship together
with the slow-wave sleep-specific character of the VLPO
and
insufficiency of the sleep-promoting system within these hypothalamic
nuclei in WAG/Rij rats, it might be speculated that VLPO is one of the
potential candidates to host a dependent oscillator for the rhythm of
SWDs. However, it should be noted that while a lesion of the VLPO
resulted in a marked decrease in the amount of both slow-wave and
REM sleep, the circadian rhythm of the remaining sleep was not
affected (Lu et al., 2000). Thus, there might be some other
oscillator/oscillators, which impose the rhythmicity on sleep generated
by the VLPO.
The DMH and SPVZ are both sites with direct projections to the
SCN and also sites of efferent projections to the VLPO (Thompson and
Swanson, 1998; Leak and Moore, 2001; Deurveilher and Semba, 2005;
Nakamura et al., 2008). The structures are involved in a wide range of
physiological processes such as feeding, thermoregulation, restactivity cycle, autonomic stress responses, corticosteroids secretion
(Kalsbeek et al., 1996; Bali et al., 2005; Saper et al, 2005;
Abrahamson and Moore, 2006). A bilateral, neurotoxic lesion of the
DMH and SPVZ resulted in a marked reduction of circadian rhythms of
slow-wave sleep, REM sleep, motor activity and body temperature (Lu
203
et al., 2001; Chou et al., 2003). The SCN – DMH/SPVZ – VLPO circuitry
seems to be a promising option for the search of the circadian oscillator
governing the rhythm of absence seizures. Taking into consideration
interactions between SWDs and stress hormones, an involvement of
the relay nuclei (the DMH, SPVZ) in reactivity to stress and
corticosteroid secretion is additionally encouraging.
Figure 1. Schematic representation of the neuronal connections
between the master circadian pacemaker, the suprachiasmatic nuclei of the
hypothalamus (SCN) and the ventolateral peoptic nuclei (VLPO) responsible for
sleep initiation and maintenance. An insufficient sleep-promoting mechanism
of the VLPO in WAG/Rij rats is linked to the generation of absence seizures
(Suntsova et al., 2007). Together with slow-wave sleep/REM sleep regionspecificity of the VLPO and the fact that the rhythm of SWDs is linked to the
rhythm of light slow-wave sleep, the VLPO may be considered as a potential
oscillator for the rhythm of SWDs. The subparaventricular zone (SPVZ) and
dorsomedial hypothalamic nuclei (DMH) serve as relay nuclei. Modified from
Paxinos and Watson, 2007
Future directions
Basic chronobiological approaches such as constant condition
and phase shift paradigms have been used in the present thesis to
assess the contribution of the circadian timing system and external
factors to the occurrence of absence seizures in WAG/Rij rats. The
204
outcomes of the experiments suggest an endogenous character of the
rhythm of SWDs, the rhythm’s dependency on sleep-wake states and
photic entrainment; and distinct dependent oscillatory mechanisms
governing
rhythms
of
various
electroencephalographic
states.
Considering the functional complexity of the circadian timing system,
a multiplicity of factors affecting absence seizures and possible
interactions between circadian and epileptic mechanisms, the present
results create more research questions. But not only questions, also
experimental opportunities to elucidate how the circadian system
governs the SWD rhythm. Apart from ideas mentioned in the previous
sections such as implementation of the forced desynchrony protocol, a
phase advance or various shift work paradigms, and the assessment of
the functionality of the melatonin system and the SCN - DMH/SPVZ –
VLPO circuitry in WAG/Rij rats, some other, more applicable and
patient-oriented studies should be also considered.
The rhythmic incidence of absence seizures, with well-defined
timing of the maximum and minimum, seems to be a promising target
for chronotherapy. The concept is simply to optimize the timing of drug
administration to obtain the highest therapeutic effect with minimal or
no adverse effects (Reinberg, 1991; Smolensky and Peppas, 2007;
Ohdo, 2010). Chronotherapy integrates chronopathology, which deals
with a circadian modulation of clinical symptoms of diseases,
chronopharmacology,
pharmacokinetics
and
which
comprise
of
pharmacodynamics
circadian
and
aspects
of
chronotoxicology
providing information about cyclic changes in drugs toxicity (Kaur et
al., 2013).
Chronopharmacological
studies
towards
assessment
of
pharmacodynamics, pharmacokinetics and toxicity of AEDs with
respect to circadian rhythmicity have been undertaken, mostly with
valproic acid, an effective AED for various seizure types, including
absence seizures (Meinardi et al., 1975; Patel et al., 1982; Klotz and
205
Reimann, 1984; Nakano et al., 1984; Shirkey et al., 1985; Theisohn et
al., 1989; Yoshiyama et al., 1989; Ohdo et al., 1991; Ohdo et al.,
1992; Reith et al., 2001; Ben-Cherif et al., 2012; Ben-Cherif et al.,
2013). Time-of-day differences with regard to absorption, metabolism,
distribution, elimination, toxicity and efficacy of AEDs have been found.
Attempts to implement the principles of chronopharmacotherapy in
treatment of seizures have been undertaken and described in two
studies to date. Yegnanarayan and co-workers (2006) showed the
efficacy and safety of chronotherapy in patients with tonic-clonic
seizures who either did not respond to standard doses of phenytoin or
carbamazepine, or showed serum concentrations of these AEDs above
a safe and acceptable level (Yegnanarayan et al., 2006). Improved
seizure
control
obtained
by
an
adjustment
of
the
timing
of
administration of AEDs with a constant total dose of medications in
patients with nocturnal and early-morning seizures has been reported
(Guilhoto et al., 2011). Both studies support the idea that the finetuned timing of drugs administration with respect to the occurrence of
seizures may help to achieve good control of epilepsy and minimize
adverse side effects.
A pronounced contribution of the circadian timing system to the
occurrence
of
absence
seizures
in
WAG/Rij
rats,
well-defined
relationship between SWDs and sleep-wake states and susceptibility of
the rhythm to phase-shifts makes the model a valuable tool in
chronopharmacologcial research of anti-absence medications, complex
relationships between systems involved in seizures generation,
circadian rhythmicity and drugs metabolism; and from a more
applicable point of view: an optimal drug administration schedule.
References:
Abrahamson EE, Leak RK, Moore RY. 2001. The suprachiasmatic nucleus projects to
posterior hypothalamic arousal systems. Neuroreport. 12: 435-440.
206
Abrahamson EE, Moore RY. 2006. Lesions of suprachiasmatic nucleus efferents
selectively affect rest-activity rhythm. Mol Cell Endocrinol. 252: 46-56.
Aerospace Medical Association Medical Guidelines Task Force. 2003. Medical
Guidelines for Airline Travel, 2nd ed. Aviat Space Environ Med. 74: A1-19.
Aguiar CC, Almeida AB, Araújo PV, Vasconcelos GS, Chaves EM, do Vale OC, Macêdo
DS, de Sousa FC, Viana GS, Vasconcelos SM. 2012. Anticonvulsant effects of
agomelatine in mice. Epilepsy Behav. 24: 324-328.
Alonso-Cánovas A, de Felipe-Mimbrera A, González-Valcárcel J, García-Barragán
N, Corral I, Masjuan J. 2011. Neurology at the airport. J Neurol Neurosurg
Psychiatry. 82: 981-985.
Antón-Tay F, Díaz JL, Fernández-Guardiola A. 1971. On the effect of melatonin upon
human brain. Its possible therapeutic implications. Life Sci. 10: 841-850.
Antón-Tay F. 1974. Melatonin: effects
Psychopharmacol. 11: 315-324.
on
brain
function.
Adv
Biochem
Bali B, Erdélyi F, Szabó G, Kovács KJ. 2005. Visualization of stress-responsive inhibitory
circuits in the GAD65-eGFP transgenic mice. Neurosci Lett. 380: 60-65.
Barreto JR, Fernandes RM, Sakamoto AC. 2002. Correlation of sleep macrostructure
parameters and idiopathic epilepsies. Arq Neuropsiquiatr. 60: 353-357.
Baykan B, Matur Z, Gürses C, Aykutlu E, Gökyiğit A. 2005. Typical absence
seizures triggered by photosensitivity. Epilepsia. 46:159-163.
Bazil CW, Castro LH, Walczak TS. 2000. Reduction of rapid eye movement sleep by
diurnal and nocturnal seizures in temporal lobe epilepsy. Arch Neurol. 57: 363368.
Benca R, Duncan MJ, Frank E, McClung C, Nelson RJ, Vicentic A. 2009. Biological
rhythms, higher brain function, and behavior: Gaps, opportunities, and
challenges. Brain Res Rev. 62: 57-70.
Ben-Cherif W, Dridi I, Aouam K, Ben-Attia M, Reinberg A, Boughattas NA. 2012.
Chronotolerance study of the antiepileptic drug valproic acid in mice. J
Circadian Rhythms. 10: 3.
Ben-Cherif W, Dridi I, Aouam K, Ben-Attia M, Reinberg A, Boughattas NA. 2013.
Circadian variation of Valproic acid pharmacokinetics in mice. Eur J Pharm Sci.
49: 468-473.
Bikjdaouene L, Escames G, León J, Ferrer JM, Khaldy H, Vives F, Acuña-Castroviejo D.
2003. Changes in brain amino acids and nitric oxide after melatonin
administration in rats with pentylenetetrazole-induced seizures. J Pineal
Res. 35: 54-60.
207
Bittman EL, Costello MK, Brewer JM. 2007. Circadian organization of tau mutant
hamsters: aftereffects and splitting. J Biol Rhythms. 22: 425-431.
Cambras T, Weller JR, Anglès-Pujoràs M, Lee ML, Christopher A, Diez-Noguera A,
Krueger JM, da la Iglesia HO. 2007. Circadian desynchronization of core body
temperature and sleep stages in the rat. Proc. Natl. Sci. U.S.A. 104: 7634-7639.
Chou TC, Bjorkum AA, Gaus SE, Lu J, Scammell TE, Saper CB. 2002. Afferents to the
ventrolateral preoptic nucleus. J Neurosci. 22: 977-990.
Chou TC, Scammell TE, Gooley JJ, Gaus SE, Saper CB, Lu J. 2003. Critical role of
dorsomedial hypothalamic nucleus in a wide range of behavioral circadian
rhythms. J Neurosci. 23: 10691-10702.
Coppola G, Iervolino G, Mastrosimone M, La Torre G, Ruiu F, Pascotto A. 2004.
Melatonin in wake-sleep disorders in children, adolescents and young adults
with mental retardation with or without epilepsy: a double-blind, cross-over,
placebo-controlled trial. Brain Dev. 26: 373-376.
Costa G. 2003. Shift work and occupational medicine: an overview. Occup Med
(Lond). 53: 83-88.
Czeisler CA, Duffy JF, Shanahan TL, Brown EN, Mitchell JF, Rimmer DW, Ronda JM, Silva
EJ, Allan JS, Emens JS, Dijk DJ, Kronauer RE. 1999. Stability, precision, and
near-24-hour period of the human circadian pacemaker. Science. 284: 21772181.
Dasgupta AK, Saunders M, Dick DJ. 1982. Epilepsy in the British Steel Corporation: an
evaluation of sickness, accident, and work records. Br J Ind Med. 39: 145-148.
de la Iglesia HO, Cambras T, Schwartz WJ, Díez-Noguera A. 2004. Forced
desynchronization of dual circadian oscillators within the rat suprachiasmatic
nucleus. Curr Biol. 14: 796-800.
de Lima E, Soares JM Jr, del Carmen Sanabria Garrido Y, Gomes Valente S, Priel
MR, Chada Baracat E, Abrão Cavalheiro E, da Graça Naffah-Mazzacoratti
M, Amado D. 2005. Effects of pinealectomy and the treatment with melatonin on
the temporal lobe epilepsy in rats. Brain Res. 1043: 24-31.
Descamps A, Rousset C, Millan MJ, Spedding M, Delagrange P, Cespuglio R. 2009.
Influence of the novel antidepressant and melatonin agonist/serotonin2C
receptor antagonist, agomelatine, on the rat sleep-wake cycle architecture.
Psychopharmacology (Berl). 205: 93-106.
Deurveilher S, Semba K. 2005. Indirect projections from the suprachiasmatic nucleus
to major arousal-promoting cell groups in rat: implications for the circadian
control of behavioural state. Neuroscience. 130: 165-183.
208
Drinkenburg WH, Coenen AM, Vossen JM, Van Luijtelaar EL. 1991. Spike-wave
discharges and sleep-wake states in rats with absence epilepsy. Epilepsy Res.
9: 218-224.
Duffy JF, Dijk DJ. 2002. Getting through to circadian oscillators: why use constant
routines? J Biol Rhythms. 17: 4-13.
Elkhayat HA, Hassanein SM, Tomoum HY, Abd-Elhamid IA, Asaad T, Elwakkad AS.
2010. Melatonin and sleep-related problems in children with intractable
epilepsy. Pediatr Neurol. 42: 249-254.
Erren TC, Pape HG, Reiter RJ, Piekarski C. 2008. Chronodisruption and cancer.
Naturwissenschaften. 95: 367-382.
Fauteck J, Schmidt H, Lerchl A, Kurlemann G, Wittkowski W. 1999. Melatonin in
epilepsy: first results of replacement therapy and first clinical results. Biol
Signals Recept. 8: 105-110.
Fenoglio-Simeone K, Mazarati A, Sefidvash-Hockley S, Shin D, Wilke J, Milligan
H, Sankar R, Rho JM, Maganti R. 2009. Anticonvulsant effects of the
selective melatonin receptor agonist ramelteon. Epilepsy Behav.16: 52-57.
Gaus SE, Strecker RE, Tate BA, Parker RA, Saper CB. 2002. Ventrolateral preoptic
nucleus contains sleep-active, galaninergic neurons in multiple mammalian
species. Neuroscience. 115: 285-294.
Gervasoni D, Lin SC, Ribeiro S, Soares ES, Pantoja J, Nicolelis MA. 2004. Global
forebrain dynamics predict rat behavioral states and their transitions. J
Neurosci. 24: 11137-11147.
Goldberg-Stern H, Oren H, Peled N, Garty BZ. 2012. Effect of melatonin on seizure
frequency in intractable epilepsy: a pilot study. J Child Neurol. 27: 1524-1528.
Goodwin GM, Emsley R, Rembry S, Rouillon F, Agomelatine Study Group. 2009.
Agomelatine prevents relapse in patients with major depressive disorder
without evidence of a discontinuation syndrome: a 24-week randomized,
double-blind, placebo-controlled trial. J. Clin. Psychiatry 70: 1128–1137.
Goto M, Oshima I, Tomita T, Ebihara S. 1989. Melatonin content of the pineal gland in
different mouse strains. J Pineal Res. 7: 195-204.
Grünewald RA, Aliberti V, Panayiotopoulos CP. 1992. Exacerbation of typical absence
seizures by progesterone. Seizure. 1: 137-138.
Guilhoto LM, Loddenkemper T, Vendrame M, Bergin A, Bourgeois BF, Kothare SV. 2011.
Higher evening antiepileptic drug dose for nocturnal and early-morning
seizures. Epilepsy Behav. 20: 334-337.
209
Gupta M, Aneja S, Kohli K. 2005. Add-on melatonin improves sleep behavior in children
with epilepsy: randomized, double-blind, placebo-controlled trial. J Child
Neurol. 20: 112-115.
Halász P, Terzano MG, Parrino L. 2002. Spike-wave discharge and the microstructure
of sleep-wake continuum in idiopathic generalised epilepsy. Neurophysiol Clin.
32: 38-53.
Hofstra WA, de Weerd AW. 2008. How to assess circadian rhythm in humans: a review
of literature. Epilepsy Behav. 13: 438-444.
Jandó G, Carpi D, Kandel A, Urioste R, Horvath Z, Pierre E, Vadi D, Vadasz C, Buzsáki
G. 1995. Spike-and-wave epilepsy in rats: sex differences and inheritance of
physiological traits. Neuroscience. 64: 301-17.
Jud C, Schmutz I, Hampp G, Oster H, Albrecht U. 2005. A guideline for analyzing
circadian wheel-running behavior in rodents under different lighting conditions.
Biol Proced Online. 7:101-116.
Kalsbeek A, Drijfhout WJ, Westerink BH, van Heerikhuize JJ, van der Woude TP, van
der Vliet J, Buijs RM. 1996. GABA receptors in the region of the dorsomedial
hypothalamus of rats are implicated in the control of melatonin and
corticosterone release. Neuroendocrinology. 63: 69-78.
Kaur G, Phillips C, Wong K, Saini B. 2013. Timing is important in medication
administration: a timely review of chronotherapy research. Int J Clin Pharm. 35:
344-358.
Klante G, Secci K, Masson-Pévet M, Pévet P, Vivien-Roels B, Steinlechner S, Wollnik F.
1999. Interstrain differences in activity pattern, pineal function, and
SCN melatonin receptor density of rats. Am J Physiol. 276: R1078-1086.
Klotz U, Reimann IW. 1984. Chronopharmacokinetic study with prolonged infusion of
midazolam. Clin Pharmacokinet. 9: 469–474.
Knutsson A. 2003. Health disorders of shift workers. Occup Med (Lond). 53: 103-108.
Koller M. Occupational health services for shift and night workers. Appl Ergon. 27: 3137.
LaDou J, 1982. Health Effects of Shift Work. West J Med. 137: 525–530.
Lannes B, Micheletti G, Vergnes M, Marescaux C, Depaulis A, Warter JM. 1988.
Relationship between spike-wave discharges and vigilance levels in rats with
spontaneous petit mal-like epilepsy. Neurosci Lett. 94: 187-191.
Lapin IP, Mirzaev SM, Ryzov IV, Oxenkrug GF. 1998. Anticonvulsant activity of
melatonin against seizures induced by quinolinate, kainate, glutamate, NMDA,
and pentylenetetrazole in mice. J Pineal Res. 24: 215-218.
210
Leak RK, Moore RY. 2001. Topographic organization of suprachiasmatic nucleus
projection neurons. J Comp Neurol. 433: 312-334.
Lima E, Cabral FR, Cavalheiro EA, Naffah-Mazzacoratti Mda G, Amado D. 2011.
Melatonin administration after pilocarpine-induced status epilepticus: a new
way to prevent or attenuate postlesion epilepsy? Epilepsy Behav. 20: 607-612
Loo H, Hale A, D’haenen H. 2002. Determination of the dose of agomelatine, a
melatonergic agonist and selective 5-HT2C antagonist, in the treatment of
major depressive disorder: a placebo-controlled dose range study. Int. Clin.
Psychopharmacol. 17: 239–247.
Lu J, Greco MA, Shiromani P, Saper CB. 2000. Effect of lesions of the ventrolateral
preoptic nucleus on NREM and REM sleep. J Neurosci. 20: 3830-3842.
Lu J, Zhang YH, Chou TC, Gaus SE, Elmquist JK, Shiromani P, Saper CB. 2001.
Contrasting effects of ibotenate lesions of the paraventricular nucleus and
subparaventricular zone on sleep-wake cycle and temperature regulation. J
Neurosci. 21: 4864-4874.
Lüttjohann A, van Luijtelaar G. 2012. The dynamics of cortico-thalamo-cortical
interactions at the transition from pre-ictal to ictal LFPs in absence epilepsy.
Neurobiol Dis. 47: 49-60.
Maganti R, Sheth RD, Hermann BP, Weber S, Gidal BE, Fine J. 2005. Sleep architecture
in children with idiopathic generalized epilepsy. Epilepsia. 46: 104-109.
Mareš J, Stopka P, Nohejlová K, Rokyta R. 2013. Oxidative stress induced by epileptic
seizure and its attenuation by melatonin. Physiol Res. 62: S67-74.
Martinet L, Guardiola-Lemaitre B, Mocaer E. 1996. Entrainment of circadian rhythms by
S-20098, a melatonin agonist, is dose and plasma concentration dependent.
Pharmacol Biochem Behav. 54: 713-718.
Matos G, Andersen ML, do Valle AC, Tufik S. 2010. The relationship between sleep
and epilepsy: evidence from clinical trials and animal models. J Neurol Sci. 295:
1-7.
McGechan A, Wellington K. 2005. Ramelteon. CNS Drugs. 19: 1057-1065.
Meinardi H, Van Der Kleijn E, Meijer JW, Van Rees H. 1975. Absorption and distribution
of antiepileptic drugs. Epilepsia. 16: 353-365.
Mitchell G, Deva N, Harkin M, Skinner P, Werthmann A. 2011. Written Declaration
pursuant to Rule 123 of the Rules of Procedure on epilepsy. PE464.670v01-00.
Musshoff U, Speckmann EJ. 2003. Diurnal actions of melatonin on epileptic activity in
hippocampal slices of rats. Life Sci. 73: 2603-2610.
211
Nakamura W, Yamazaki S, Nakamura TJ, Shirakawa T, Block GD, Takumi T. 2008. In
vivo monitoring of circadian timing in freely moving mice. Curr Biol. 18: 381385.
Nakano S, Watanabe H, Nagai K, Ogawa N. 1984. Circadian stage-dependent changes
in diazepam kinetics. Clin Pharmacol Ther. 36: 271–277.
Nicholson PJ, D'Auria DA. 1999. Shift work, health, the working time regulations and
health assessments. Occup Med (Lond). 49: 127-137.
Novak CM, Nunez AA. 2000. A sparse projection from the suprachiasmatic nucleus to
the sleep active ventrolateral preoptic area in the rat. Neuroreport. 11: 93-96.
Ohdo S, Nakano S, Ogawa N. 1991. Chronopharmacokinetics of valproic acid following
constant-rate administration in mice. Chronobiol Int. 8: 35-43.
Ohdo S, Nakano S, Ogawa N. 1992. Circadian changes of valproate kinetics depending
on meal condition in humans. J Clin Pharmacol. 32: 822–6.
Ohdo S. 2010. Chronotherapeutic strategy: Rhythm monitoring, manipulation and
disruption. Adv Drug Deliv Rev. 62: 859-875.
Papp M, Gruca P, Boyer PA, Mocaër E. 2003. Effect of agomelatine in the chronic mild
stress model of depression in the rat. Neuropsychopharmacology 28: 694–703.
Patel IH, Venkataramanan R, Levy RH, Viswanathan CT, Ojemann LM. 1982. Diurnal
oscillations in plasma protein binding of valproic acid. Epilepsia. 23: 283–90.
Pavlova MK, Shea SA, Scheer FA, Bromfield EB. 2009. Is there a circadian variation of
epileptiform
abnormalities
in
idiopathic
generalized
epilepsy?
Epilepsy Behav. 16: 461-467.
Paxinos G, Watson C. 2007. The rat brain in stereotaxic coordinates, 6th Edition.
Elsevier Academic Press.
Peled N, Shorer Z, Peled E, Pillar G. 2001. Melatonin effect on seizures in children with
severe neurologic deficit disorders. Epilepsia. 42: 1208-1210.
Quigg M, Clayburn H, Straume M, Menaker M, Bertram III EH. 2000. Effects of circadian
regulation and rest-activity state on spontaneous seizures in rat model of limbic
epilepsy. Epilepsia 41: 502-509.
Redman J, Armstrong S, Ng KT. 1983. Free-running activity rhythms in the rat:
entrainment by melatonin. Science. 219:1089-1091.
Reinberg AE. 1991. Concepts of circadian chronopharmacology. Ann N Y Acad Sci. 618:
102-115.
Reinberg A, Ashkenazi I. 2008. Internal desynchronization of circadian rhythms and
tolerance to shift work. Chronobiol Int. 25: 625-643.
212
Reinberg AE, Ashkenazi I, Smolensky MH. 2007. Euchronism, allochronism, and
dyschronism: is internal desynchronization of human circadian rhythms a sign
of illness? Chronobiol Int. 24: 553-588.
Reith DM, Andrews J, McLaughlin D. 2001. Valproic acid has temporal variability in
urinary clearance of metabolites. Chronobiol Int. 18: 123–129.
Ross C, Davies P, Whitehouse W. 2002. Melatonin treatment for sleep disorders in
children with neurodevelopmental disorders: an observational study. Dev Med
Child Neurol. 44: 339-344.
Ruby NF, Brennan TJ, Xie X, Cao V, Franken P, Heller HC, O'Hara BF. 2002. Role of
melanopsin in circadian responses to light. Science. 298: 2211-2213.
Sandyk R, Tsagas N, Anninos PA. 1992Melatonin as a proconvulsive hormone in
humans. Int J Neurosci. 63: 125-135.
Saper CB, Lu J, Chou TC, Gooley J. 2005. The hypothalamic integrator for circadian
rhythms. Trends Neurosci. 28: 152-157.
Sarkisova K, van Luijtelaar G. 2011. The WAG/Rij strain: a genetic animal model of
absence epilepsy with comorbidity of depression. Prog Neuropsychopharmacol
Biol Psychiatry. 35: 854-876.
Sarkisova KY, Kuznetsova GD, Kulikov MA, van Luijtelaar G. 2010. Spike-wave
discharges are necessary for the expression of behavioral depression-like
symptoms. Epilepsia. 51: 146-160.
Sheldon SH. 1998. Pro-convulsant effects of oral melatonin in neurologically disabled
children. Lancet. 351:1254.
Sherin JE, Shiromani PJ, McCarley RW, Saper CB. 1996. Activation of ventrolateral
preoptic neurons during sleep. Science. 271: 216-219.
Shirkey RJ, Jellett LB, Kappatos DC, Maling TJ, Macdonald A. 1985. Distribution of
sodium valproate in normal whole blood and in blood from patients with renal
or hepatic disease. Eur J Clin Pharmacol. 28: 447–452.
Shouse MN, Farber PR, Staba RJ. 2000. Physiological basis: how NREM sleep
components can promote and REM sleep components can suppress seizure
discharge propagation. Clin Neurophysiol. 2: S9-S18.
Sirven JI, Claypool DW, Sahs KL, Wingerchuk DM, Bortz JJ, Drazkowski J, Caselli
R, Zanick D. 2002. Is there a neurologist on this flight? Neurology. 58: 17391744.
Somerville ER. 2009. Some treatments cause seizure aggravation in idiopathic
epilepsies (especially absence epilepsy). Epilepsia. 8: 31-36.
213
Smolensky MH, Peppas NA. 2007. Chronobiology, drug
chronotherapeutics. Adv Drug Deliv Rev. 59: 828-851.
delivery,
and
Stewart LS, Leung LS. 2005. Hippocampal melatonin receptors modulate seizure
threshold. Epilepsia. 46: 473-480.
Suntsova N, Kumar S, Guzman-Marin R, Alam MN, Szymusiak R, McGinty D. 2009. A
role for the preoptic sleep-promoting system in absence epilepsy. Neurobiol Dis.
36: 126-141.
Szymusiak R, Alam N, Steininger TL, McGinty D. 1998. Sleep-waking discharge patterns
of ventrolateral preoptic/anterior hypothalamic neurons in rats. Brain Res. 803:
178-188.
Tchekalarova J, Petkova Z, Pechlivanova D, Moyanova S, Kortenska L, Mitreva
R, Lozanov V, Atanasova D, Lazarov N, Stoynev A. 2013. Prophylactic treatment
with melatonin after status epilepticus: effects on epileptogenesis, neuronal
damage, and behavioral changes in a kainate model of temporal lobe epilepsy.
Epilepsy Behav. 27: 174-187.
Theisohn M, Assion HJ, Werner U. 1989. Influence of breakfast and day time on the
disposition of carbamazepine in healthy volunteers after intake of single dose
(600 mg) of a standard or retard preparation of carbamazepine.
Chronopharmacology in Therapy of the Epilepsies. New York: Raven Press; pp.
31–54.
Thompson RH, Swanson LW. 1998. Organization of inputs to the dorsomedial nucleus
of the hypothalamus: a reexamination with Fluorogold and PHAL in the rat. Brain
Res Brain Res Rev. 27: 89-118.
Tolmacheva EA, Oitzl MS, van Luijtelaar G. 2012. Stress, glucocorticoids and absences
in a genetic epilepsy model. Horm Behav. 61: 706-710.
Trevorrow T. 2006. Air travel and seizure frequency for individuals with epilepsy.
Seizure. 15: 320-327.
Uberos J, Augustin-Morales MC, Molina Carballo A, Florido J, Narbona E, Muñoz-Hoyos
A. 2011. Normalization of the sleep-wake pattern and melatonin and 6sulphatoxy-melatonin levels after a therapeutic trial with melatonin in children
with severe epilepsy. J Pineal Res. 50: 192-196.
Van Luijtelaar EL, Coenen AM. 1988. Circadian rhythmicity in absence epilepsy in rats.
Epilepsy Res. 2: 331-336.
van Luijtelaar G, Budziszewska B, Jaworska-Feil L, Ellis J, Coenen A, Lasoń W. 2001.
The ovarian hormones and absence epilepsy: a long-term EEG study and
pharmacological effects in a genetic absence epilepsy model. Epilepsy Res. 46:
225-239.
214
van Luijtelaar G, Bikbaev A. 2007. Midfrequency cortico-thalamic oscillations and the
sleep cycle: genetic, time of day and age effects. Epilepsy Res. 73: 259-265.
van Luijtelaar G, Wilde M, Citraro R, Scicchitano F, van Rijn C. 2012. Does
antiepileptogenesis affect sleep in genetic epileptic rats? Int J Psychophysiol.
85: 49-54.
Videnovic A, Lazar AS, Barker RA, Overeem S. 2014. 'The clocks that time us'--circadian
rhythms in neurodegenerative disorders. Nat Rev Neurol. 10: 683-693.
Waterhouse J, Weinert D, Minors D, Folkard S, Owens D, Atkinson G, Macdonald I,
Sytnik N, Tucker P, Reilly T. 2000. A comparison of some different methods for
purifying core temperature data from humans. Chronobiol. Int. 17:539–566
Weinert D, Waterhouse J. 1998. Diurnally changing effects of locomotor activity on body
temperature in laboratory mice. Physiol Behav. 63:837-43.
Wolf P, Goosses R. 1986. Relation of photosensitivity to epileptic syndromes. J Neurol
Neurosurg Psychiatry. 49: 1386–1391.
Xu K, Stringer JL. 2008. Antioxidants and free radical scavengers do not consistently
delay seizure onset in animal models of acute seizures. Epilepsy Behav. 13: 7782.
Yegnanarayan R, Mahesh SD, Sangle S. 2006. Chronotherapeutic dose schedule of
phenytoin and carbamazepine in epileptic patients. Chronobiol Int. 23: 10351046.
Yi PL, Chen YJ, Lin CT, Chang FC. 2012. Occurrence of epilepsy at different zeitgeber
times alters sleep homeostasis differently in rats. Sleep. 35: 1651-1665.
Yildirim M, Aydin-Abidin S, Abidin I, Akca M, Canpolat S, Cansu A. 2013. Evaluation of
the role of chronic daily melatonin administration and pinealectomy on
penicillin-induced focal epileptiform activity and spectral analysis of ECoG in
rats: an in vivo electrophysiological study. Neurochem Res. 38: 1672-1685.
Yildirim M, Marangoz C. 2006. Anticonvulsant effects of melatonin on penicillin-induced
epileptiform activity in rats. Brain Res. 1099: 183-188.
Yoshiyama Y, Nakano S, Ogawa N. 1989. Chronopharmacokinetic study of valproic acid
in man: comparison of oral and rectal administration. J Clin Pharmacol. 29:
1048–1052.
215
ADDENDUM
English summary
Rhythm in the occurrence of absence seizures in WAG/Rij rats,
its origin and interactions with different factors comprise the topic of
the present thesis. The experiments described were aimed to answer
the question whether the rhythm of SWDs is generated endogenously
by the circadian timing system and how the rhythm synchronizes to
the most powerful circadian Zeitgeber, the light-dark cycle. Other
questions regarded the temporal relationship between SWDs and
sleep-wake states, and to which of the sleep-wake rhythms absences
are coupled to; finally, whether the melatonin agonist agomelatine is
able to influence both SWDs themselves and their rhythm. The thesis
provides an insight into circadian aspects of the occurrence of absence
seizures, it allows also some predictions about behavior and frequency
of absences during regular conditions and during a circadian challenge
such as internal desynchronization.
Chapter 1 provides general information from the field of
chronobiology including time ranges of physiological processes and
overview of the mammalian circadian timing system with an emphasis
on the pineal hormone melatonin. Examples of 24-h variation in
symptoms of common diseases are presented. Next, a general
introduction to epilepsy with a particular focus on childhood absence
epilepsy and non-random patterns of seizures occurrence is given. The
Chapter provides a brief description of various rodent models of
childhood absence epilepsy concentrating subsequently on the WAG/Rij
strain of rats. The experiments were performed in rats of this strain:
the WAG/Rij model is considered as one of the best characterized and
validated models. The aims and outline of the thesis are given at the
end of the Chapter.
In Chapter 2, different, phase-related temporal relationships
between SWDs and five sleep-wake states (active wakefulness, passive
219
wakefulness, light and deep slow-wave sleep, REM sleep) were
described. The course of epileptic activity and vigilance states was
investigated across 24 h, as well as separately for the light and the
dark phase of the 12:12 light-dark cycle. Temporal relationships
between the five EEG behavioral states and SWDs were assessed by
means of correlation and cross-correlation analyses. Differences with
regard to the data resolution and the phase of the photoperiod were
found. SWDs correlated negatively with deep slow-wave sleep and
positively with active wakefulness. An increase in the resolution of the
data from 1h to 0.5 min was accompanied by a decrease in the strength
of the correlations; this was interpreted as the result of decreased
temporal relationship between variables. The character of the
relationships appeared to be phase-dependent: unidirectional in the
light phase and bidirectional in the dark phase, which suggests
different regulatory mechanisms governing the temporal organization
of sleep, wakefulness and absence seizures.
The
endogenous
character
of
the
rhythm
of
SWDs
is
demonstrated in Chapter 3. WAG/Rij rats, kept initially in 12:12 lightdark cycle, were deprived of this potent circadian Zeitgeber by
changing the light regime into constant dim light (20 days). The
presence of circadian rhythms in the occurrence of SWDs and general
motor activity in both experimental conditions was assessed by the
Cosinor method. Additionally, sleep-wake states preceding and
following SWDs were identified to establish whether the well-known
relationship between SWDs and sleep-wake states is influenced by the
circadian timing system. Both rhythms were detected in the constant
dim light conditions, however, their parameters changed substantially.
Although still present, the rhythm of SWDs lost some of its organization
which emphasized the role of the photic entrainment in the timed
occurrence of SWDs. Internal desynchronization between SWDs and
general motor activity was found suggesting that the rhythms are
220
controlled
by
relationship
different,
between
dependent
SWDs
and
circadian
sleep-wake
mechanism.
states
The
established
previously was not affected in the constant dim light condition.
The matter of internal desynchronization and dependent
oscillators governing rhythms of absence seizures and sleep-wake
states is further investigated in Chapters 4 and 5. Chapter 4 describes
the process of resynchronization of the rhythms of SWDs and general
motor activity in WAG/Rij rats subjected to an 8 h light phase delay.
Differences regarding the speed and character of re-entrainment were
found. After immediate advance, the acrophase of the general motor
activity rhythm gradually reached its pre-shift position on the seventh
post shift day, whereas the resynchronization of the rhythm of SWDs
lasted
one
day
longer
and
was
discontinuous.
Internal
desynchronization between the rhythms was accompanied by an
increase in the number and duration of SWDs; this increase occurred
exclusively in the new light phase.
To get a detailed insight into the resynchronization process of
the rhythm of SWDs and to identify factors responsible for aggravation
of epileptic activity, analysis of four remaining sleep-wake states was
added to the study described in Chapter 5. Additionally, an attempt to
alleviate negative consequences of internal desynchronization was
undertaken
by
timed
administration
of
a
melatonin
agonist
agomelatine. The procedure of the phase shift was identical as
presented in Chapter 4. WAG/Rij rats were subjected to the 8 h light
phase delay, after which the resynchronization process of the whole
sleep-wake cycle was investigated for 10 days. Different speed of reentrainment and coupling between various rhythms was found. The
rhythm of SWDs resynchronized together with the rhythm of light slowwave sleep, whereas they uncoupled from the active wakefulness deep slow-wave sleep pair. Again, an increase in the number and
duration of SWDs was noted, explained by post shift changes in the
221
amount of passive wakefulness and deep slow-wave sleep. As
previously established and confirmed in the chapters of this thesis,
passive wakefulness, contrary to deep slow-wave sleep, is the most
favorable state of vigilance for the occurrence of SWDs. The melatonin
agonist agomelatine had no effects on sleep-wake parameter. Next,
the timed administration of agomelatine failed to improve the speed of
re-synchronization, it showed also no effect on SWDs during condition
of entrainment to the 12:12 light-dark cycle.
The thesis ends with Chapter 6, in which all experimental results
are evaluated and critically discussed in the view of the main questions
addressed in this thesis. It is concluded that the rhythm of SWDs is
generated endogenously by the circadian timing system rather than it
is imposed by the presence of the light-dark cycle. However, this potent
Zeitgeber, has a great impact on the organization of the SWD rhythm.
Disturbances in the photic entrainment, either chronic (the constant
dim light condition) or acute (phase shift) result in internal
desynchronization between the rhythm of SWDs and sleep-wake
states, which in turn lead to an increased epileptic activity. The
presence of a stable light-dark cycle is an important factor contributing
to the occurrence of SWDs, so does the momentary state of vigilance.
Apart from momentary relationships, SWDs and sleep-wake states are
temporally related. This new finding implies that the duration of
particular sleep-wake states can be used as a predictor of the duration
of SWDs. While the momentary relationships are constant across the
conditions of entrainment and during free-running state of the
circadian timing system, temporal relationships between sleep-wake
stages inclusive SWDs during entrainment are modulated by the phase
of the light-dark cycle.
SWDs are particularly related to both slow-wave sleep stages,
which is especially apparent during the resynchronization process after
the phase shift in the 12:12 light-dark cycle. The rhythm of SWDs re222
entrains together with light slow-wave sleep, while marked increase in
the number and duration of absence seizures observed after the phase
shift may be attributed to a reduction in the amount of deep slow-wave
sleep. Another factor investigated in the thesis, a melatonin agonist
agomelatine was found ineffective in influencing SWDs, both in the
acute and chronic study, as well as directly and indirectly through
sleep-wake states or through interactions with the serotonin system.
Results of the experiments described in the thesis generate a
number of research questions regarding chronobiological aspects of the
occurrence of epileptic seizures. Potential directions of the future
studies are presented at the end of Chapter 6.
223
Nederlandse samenvatting
Ritmiek
in
de
aanwezigheid,
de
oorsprong
en
omgevingsinteracties van absence seizures (aanvallen), zoals die bij
WAG/Rij ratten voorkomen, is het onderwerp van dit proefschrift. De
beschreven
experimenten
hebben
als
doel
om
de
vraag
te
beantwoorden of het ritme van de piekgolf ontladingen (PGO’s),
typerend voor deze aanvallen endogeen opgewekt wordt en hoe het
ritme gesynchroniseerd wordt aan de meest krachtige circadiane
Zeitgeber, de licht donker cyclus. Andere vragen betreft de temporele
relatie tussen PGO’s en de slaap-waak stadia, en aan welk van de
slaap-waak ritmes het ritme van PGO’s gekoppeld is; tenslotte, of de
melatonine agonist agomelatine een effect heeft op de aanwezigheid
van PGO’s zelf, op het ritme van PGO’s en op de slaap-waak stadia. Dit
proefschrift geeft inzicht in de circadiane aspecten in het optreden van
de aanvallen, het geeft een paar voorspellingen over eigenschappen
en de frequentie van de aanvallen tijdens normale omstandigheden en
tijdens een aangebrachte verstoring zoals het geval is bij interne
desynchronisatie.
Hoofdstuk 1 geeft algemene informatie uit het domein van de
chronobiologie inclusief de tijdvensters waarin fysiologische processen
zich herhalen en een overzicht van het circadiane tijd systeem van
zoogdieren met een nadruk of het pijnappelklier hormoon melatonine.
Voorbeelden van 24 uur variatie in symptomen van veel voorkomende
ziektes worden gegeven. Vervolgens wordt het onderwerp epilepsie
ingeleid, met de nadruk op absence epilepsie, zoals dat bij kinderen
voorkomt (childhood absence epilepsy, CAE) en op de niet-willekeurige
aanwezigheid van de aanvallen bij deze kinderen. Het hoofdstuk geeft
verder een korte beschrijving van de verschillende knaagdier modellen
van CAE waarbij vervolgens de nadruk ligt op het WAG/Rij ratten
model. De experimenten zijn uitgevoerd met dit ratten van deze stam:
224
het WAG/Rij model is een van de best getypeerde en gevalideerde
modellen. De vraagstellingen en de verdere opzet van het proefschrift
worden aan het eind van het hoofdstuk gepresenteerd.
In
hoofdstuk
2
werden
verschillende,
fase
gerelateerde
temporele relaties tussen absences enerzijds en de vijf slaap-waak
stadia (actieve waakzaamheid, passieve waakzaamheid, lichte en
diepe („slow wave”) slaap en REM-slaap) anderzijds beschreven. Het
verloop van de epileptische activiteit en de vijf stadia werd onderzocht
over 24 uur, alsmede afzonderlijk voor de licht en de donker fase van
de ieder durende 12:12 licht-donker cyclus. Temporele relaties tussen
de verschillende EEG-gedrag gedefinieerde stadia en de PGO’s werden
bepaald met behulp van correlatie en kruis-correlatie analyses. Er
werden verschillen gevonden betreft de gegevensresolutie en de fase
van de fotoperiode. Negatief gecorreleerd met PGO’s is diepe slowwave slaap terwijl actief waakzaamheid en PGO’s positief gecorreleerd
zijn. Een verhoging van de resolutie van de gegevens van 1 uur tot 0,5
min ging gepaard met een daling van de sterkte van de correlaties; dit
werd geïnterpreteerd als het resultaat van verminderde temporele
relatie tussen de stadia. Het karakter van de relaties bleek faseafhankelijk te zijn: unidirectioneel in licht periode en bidirectioneel in
het donker. Dit suggereert dat er in deze twee periodes verschillende
regulerende mechanismen zijn voor de temporele organisatie van
slaap, wakker zijn en PGO’s.
Het endogene karakter van het ritme van PGO’s wordt in
Hoofdstuk 3 aangetoond. WAG/Rij ratten, die aanvankelijk in een 24h
12:12 licht-donker cyclus gehuisvest werden, werden van hun
krachtige circadiane Zeitgeber beroofd door hen gedurende 20 dagen
in constant gedimd licht te houden. De aanwezigheid van circadiane
ritmes van PGO’s en van algehele motorische activiteit in zowel de
eerste als in de tweede periode werd bepaald met behulp van de
Cosinor methode. Ook werden de slaap-wake stadia voorafgaand en
225
na afloop van de PGO’s bepaald teneinde vast te stellen of de bekende
relatie tussen PGO’s en de vijf slaap-waak stadia beinvloed werd door
het circadiaanse tijd sysyeem. Beide ritmes werden gedetecteerd
tijdens de standaard huisvesting, de 12-12 periode, als tijdens de
constante schemerige lichtomstandigheden.
Echter, de parameters
van de ritmes waren wezenlijk verschillend. Hoewel nog steeds
aanwezig, het ritme van PGO’s verloor wat van zijn strakke temporele
organisatie en dit benadrukt de rol van licht in het beinvloeden van
het door het endogene ritme opgelegde en daardoor getimde optreden
van de PGO’s. Interne desynchronisatie tussen PGO’s en algehele
motorische activiteit werd gevonden; dit suggereert dat de ritmes
worden
gecontroleerd
door
verschillende,
afhankelijk
circadiane
mechanismes. De relatie tussen PGO’s en de vijf slaap-waak stadia,
zoals die eerder was vastgesteld, werd niet differentieel beïnvloed in
de constante schemerige lichtomstandigheden.
De kwestie van de interne desynchronisatie en afhankelijkheid
van de oscillatoren die verantwoordelijk zijn voor de ritmes van de
aanvallen en slaap-waak toestanden, werd verder onderzocht in de
hoofdstukken 4 en 5. Hoofdstuk 4 beschrijft het proces van
resynchronisatie van de ritmes van PGO’s en algehele motorische
activiteit in WAG/Rij ratten die blootgesteld werden aan een 8 uur fase
verlenging van de licht periode (phase vertraging). Er werden
verschillen in snelheid en aard van de resynchronisatie gevonden. De
acrophase van het algehele motorische activiteit ritme verschoof
geleidelijk en bereikte zijn pre-shift positie op de zevende dag na de
verschuiving. De resynchronisatie van het ritme van PGO’s duurde een
dag langer en was discontinu. Interne desynchronisatie tussen de
ritmes werd vergezeld door een toename van het aantal en de duur
van de PGO’s, deze toename vond exclusief plaats in de nieuwe licht
periode. m tot een gedetailleerd inzicht in het resynchronisatie proces
van het ritme van PGO’s te kunnen komen en om factoren, die
226
verantwoordelijk zijn voor de toename van het aantal aanvallen te
kunnen identificeren, werd het experiment bij een nieuwe groep dieren
herhaald en zijn de ook de niet eerder geanalyseerde slaap-waak stadia
meegenomen en geanalyseerd, de studie is beschreven in Hoofsdtuk
5. Daarnaast is onderzocht of de negatieve gevolgen van interne
desynchronisatie verlicht kunnen worden door de herhaalde en
getimede toediening van de melatonine agonist agomelatine. De
procedure van de faseverschuiving was identiek aan die welke
gepresenteerd is in hoofdstuk 4. Naïeve WAG/Rij ratten werden
wederom onderworpen aan de 8 uur fase vertraging en het
resynchronisatie proces van de hele slaap-waak cyclus werd gedurende
10 dagen onderzocht. Verschillende snelheid van re-synchronizatie en
koppelingen tussen verschillende ritmes werden gevonden. Het ritme
van PGO’s was gesynchroniseerd aan het ritme van lichte „slow-wave”
slaap, terwijl het PGO ritme juist losgekoppeld was van het gepaarde
ritme van actief waakzaakheid en diepe „slow-wave” slaap. Er werd
wederom een toename van het aantal en de duur van de PGO’s
opgemerkt: dit kon worden toegeschreven worden aan de toename in
de hoeveelheid passief waakzaamheid en de afname in diepe „slowwave” slaap. Zoals eerder vastgesteld en bevestigd in de hoofdstukken
van dit proefschrift, passief waakzaamheid is, in tegenstelling tot diepe
„slow-wave” slaap, de toetand die het meest gunstig is voor het
optreden van PGO’s. De melatonine agonist agomelatine had noch een
effect op de slaap-waak parameters, noch op de snelheid van het
resynchronisatie proces, noch op het aantal PGO’s tijdens de 10 daagse
meting volgend op de fase verschuiving.
Het proefschrift eindigt met Hoofdstuk 6, hier worden alle
experimentele bevindingen geevalueerd en tegen het licht gehouden
tegen de achtergrond van de belangrijkste vragen zoals die in de
inleiding geformuleerd zijn. Geconcludeerd wordt dat het ritme van
PGO’s endogeen bepaald wordt en niet opgelegd wordt door de
227
afwisseling van licht en donker. Deze krachtige Zeitgever heeft echter
veel impact op de organisatie van het ritme: verstoringen in de vorm
van de afwezigheid van deze Zeitgeber, zowel chronisch (tijdens de
constante schemer conditie) of acuut (in de twee fase verschuivings
experimenten) hadden altijd interne desynchronizatie tussen de ritmes
van PGO’s en de slaap stadia tot gevolg, dat op zijn beurt weer
aanleiding gaf tot een toename van het aantal aanvallen. De constante
aanwezigheid van de licht-donker cyclus is daarom een belangrijke
factor die bijdraagt aan de aanwezigheid van PGOs, alsmede het
momentane slaap-waak stadium.
De slaap-waak stadia en PGO’s zijn op twee manieren met
elkaar verbonden: naast de momentane preferentie van PGO’s op
vooral tijdens passief waakzaamheid voor te komen, is een nieuwe
bevinding dat de duur van de verschillende slaap-waak stadia gebruikt
worden als een voorspeller van de duur van PGOs. Terwijl de
momentane relaties tussen PGO’s en de slaap stadia altijd hetzelfde
zijn, ongeacht of het in
base-line condities
of constant schemer
betreft, de temporele relaties tussen de slaapstadia onderling inclusief
die
met
PGO’s
gedurende
entrainment
worden
gemoduleerd
afhankelijk van of het licht of donker is.
PGO’s zijn met enige nadruk gekoppeld aan zowel lichte and
diepe „slow-wave” slaap, hetgeen vooral nog eens extra benadrukt
wordt tijdens het resynchronisatie proces na de fase vertraging in de
12:12 licht-donker
cyclus. Het ritme van PGO’s loopt tijdens dit
resynchronisatie proces in de pas met het ritme van lichte „slow wave”
slaap, terwijl de pregnante toename in het aantal PGO’s na de fase
verschuiving toegeschreven kan worden aan de afname in de diepe
„slow-wave” slaap. Een andere vraag die in het proefschrift onderzocht
is, is of de melatonine agonist agomelatine effectief is in het
beinvloeden van slaap, PGO’s en de aanpassing na verstoringen. Zowel
in de acute als in de chronische studie werden geen directe of indirecte
228
effecten vastgesteld op slaap-waak parameters, of via interakties met
het serotonine systeem.
Tenslotte, de resultaten van de experimenten beschreven in het
proefschrift
genereren
chronobiologische
een
aspecten
aantal
van
het
onderzoeksvragen
optreden
van
over
epileptische
aanvallen. Mogelijke richtingen van toekomstig onderzoek worden op
het einde van Hoofdstuk 6 gepresenteerd.
229
Curriculum vitae
Magdalena Kinga Smyk was born on 7th of October 1983 in
Rzeszów, Poland. There, in 2002, she completed the Secondary School
of General Education. In the same year she started studies at the
Jagiellonian University in Krakow, Faculty of Biology and the Earth
Sciences. In 2007 she obtained the MSc degree in Biology, majoring in
Neurobiology, with the thesis entitled “Effects of brain injury on status
epilepticus induced in rats with no susceptibility to spontaneous
epileptic seizures”, supervised by dr. Zuzanna Setkowicz.
She started her doctoral studies in 2007 at the Faculty of
Biology and the Earth Sciences of Jagiellonian University in Krakow, in
the Department of Neurophysiology and Chronobiology, under the
supervision of Prof. dr. Marian H. Lewandowski. One year later she
became a fellow PhD student in the Donders Centre for Cognition,
Radboud University Nijmegen. Under the supervision of Prof. dr. Gilles
van Luijtelaar, Prof. dr. Anton Coenen, and Prof. dr. Marian
Lewandowski she worked on the PhD project investigating the
relationship between absence epilepsy and the circadian timing system
finalized and presented in the present thesis.
Schools and courses
2014
12th International Course on Epilepsy: Advance International
Course: Bridging Basic with Clinical Epileptology. 20 July – 1
August, San Servolo, Venice, Italy
2013
14th International Course on Chronopharmacology. 29 July
– 2 August, Mannheim, Germany
2012
IBRO–VLTP Summer School. 27 June–5 July, Krakow, Poland
230
Awards
2015
Travel grant 9th IBRO World Congress of Neuroscience,
Rio de Janeiro, Brazil
2014
Travel grant 12th International Course on Epilepsy:
Advanced International Course: Bridging Basic with
Clinical Epileptology, San Servolo, Venice, Italy
2013
Young Investigator Award, 11 th International Congress
of the Polish Neuroscience Society
2013
Travel grant Understanding the
of diurnality, Strasbourg, France
2013
Travel grant Society – Environment – Technology,
Project of the Jagiellonian University for attending 14 th
International Summer Course on Chronopharmacology,
Mannheim, Germany
2011
Travel grant, 8th IBRO World Congress of Neuroscience,
Florence, Italy
2011
Travel grant 3rd World Congress of Chronobiology,
Puebla de Zaragoza, Mexico
2011
Best Student Presentation Prize,
Neuroscience Forum, Kraków, Poland
neuronal
IBRO
basis
Young
List of publications
International peer reviewed journals
Smyk MK, Coenen A, Lewandowski MH, van Luijtelaar G. 2012.
Internal desynchronization facilitates seizures. Epilepsia. 53:
1511-1518.
Smyk MK, Coenen A, Lewandowski MH, van Luijtelaar G. 2011.
Endogenous rhythm of absence epilepsy: Relationship with
231
general motor activity
Research. 93: 120-127.
and
sleep-wake
states.
Epilepsy
Book chapters
Smyk MK, van Luijtelaar G, Drinkenburg WH. 2014. Differential
ultradian organization of vigilance states in the light and the
dark phase. In: Sleep-wake research in the Netherlands. Annual
proceedings of the NSWO. Eds. Tom de Boer, Viviane van
Kasteel, Cathalijn Leenaars, Peter Meerlo, Els Most, Floor van
Oosterhout and Johan Verbraecken. 25: 77-80.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2010.
Spike-wave discharges and sleep-wake states in entrained and
free-running conditions. In: Sleep-wake research in the
Netherlands. Annual proceedings of the NSWO. Eds. Tom de
Boer, Viviane van Kasteel, Birgit Koch, Gilles van Luijtelaar and
Johan Verbraecken. 21: 94-97.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2010.
Endogenny rytm wyładowań epileptycznych mózgowia szczura
w kontekście rytmu ogólnej aktywności motorycznej. In:
Techniki
elektrofizjologiczne
w
badaniach
zjawisk
bioelektrycznych: od kanałów jonowych po sieci neuronalne. Ed.
Maria Stankiewicz. Wydawnictwo naukowe Uniwersytetu
Mikołaja Kopernika. Toruń. 119-131.
Conferences contributions
Oral presentations
Smyk MK, van Luijtelaar G, Drinkenburg WH. 2013. Old rhythm new
methodology: differential organization of sleep-wake states in
the light and the dark phase. 11th International Congress of the
Polish Neuroscience Society. 15 – 17 September 2013. Poznań,
Poland.
232
Smyk MK. 2013. Together but apart: relationship between absence
seizures and motor activity. Neuronus IBRO & IRUN
Neuroscience Forum. 9 – 11 May 2013. Kraków, Poland.
Smyk MK. 2011. Epileptic clock: Circadian control of absence seizures.
IBRO Young Neuroscience Forum Neuronus. 15 – 17 April 2011.
Kraków, Poland.
Smyk MK. 2011. Shifting the clock: seizure response to phase delay.
Krakow Workshop on Psychophysiology. 25 – 27 May 2011.
Krakow, Poland.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2010.
Endogenny rytm wyładowań epileptycznych mózgowia szczura
w kontekście rytmu ogólnej aktywności motorycznej. VII
Konferencja – Techniki elektrofizjologiczne w badaniach zjawisk
bioelektrycznych: od kanałów jonowych po sieci neuronalne. 4
– 5 czerwca 2010. Toruń, Polska.
Smyk MK. 2009. Circadian rhythmicity and absence epilepsy. Krakow
Workshop on Psychophysiology. 17 – 19 June 2009. Krakow,
Poland.
Smyk MK. 2008. Future experiments on circadian rhythmicity and
absence epilepsy. Krakow Workshop on Psychophysiology. 15 –
18 April 2008. Krakow, Poland.
Poster presentations
van Luijtelaar G, Smyk MK, Huysmans
Seizures, sleep-wake states and
antidepressant help to restore
International Epilepsy Congress,
Istanbul, Turkey.
H, Drinkenburg WH. 2015.
jet lag: Does melatonergic
internal synchrony? 31st
5 – 9 September 2015.
Smyk MK, van Luijtelaar G, Huysmans H, Drinkenburg WH. 2015.
Agomelatine does not improve speed of re-entrainment of rat
sleep-wake states and absence seizures after a phase delay. 9th
IBRO World Congress of Neuroscience. 7 – 11 July 2015. Rio de
Janeiro, Brazil.
233
Smyk MK, van Luijtelaar G, Drinkenburg WH. 2015. Sleep-wake states
across 24 h are differentially coupled in epileptic and control
rats. Neuronus IBRO & IRUN Neuroscience Forum. 17 – 19 April
2015. Kraków, Poland.
Smyk MK, van Luijtelaar G, Drinkenburg WH. 2013. Old rhythm new
methodology: differential organization of sleep-wake states in
the light and the dark phase. 11th International Congress of the
Polish Neuroscience Society. 15 – 17 September 2013. Poznań,
Poland.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2013.
Epileptic clock: circadian control of absence seizures.
Understanding the neural basis of diurnality. 26 – 28 November
2013. Strasbourg, France.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2011. Do
not mess with your biological clock: Seizure aggravation after
the phase shift. 3rd World Congress of Chronobiology. 5 – 9 May
2011. Puebla de Zaragoza, Mexico.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2011. Do
not mess with your biological clock: Seizure aggravation after
the phase shift. 8th IBRO World Congress of Neuroscience. 14
– 18 July 2011. Florence, Italy.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2011. The
jet lag syndrome in epileptic rats: seizures response to 8 hr
phase-shift. 10th International Congress of the Polish
Neuroscience Society. 21 – 24 September 2011. Łódź, Poland.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2010. The
circadian control of absence seizures. 7th FENS Forum of
European Neuroscience. 3 – 7 July 2010. Amsterdam, the
Netherlands.
Smyk MK, Coenen AML, Lewandowski MH, van Luijtelaar G. 2009.
Seizure control in free-running rats. 9th International Congress
of the Polish Neuroscience Society. 9 – 12 September 2009,
Warsaw, Poland.
234
Acknowledgements
Success has many fathers (we won’t talk about failure here! ;)).
So true. I wouldn’t have reached so much without generous support
from many great people. Let me express my gratitude to everybody
who accompanied me during this long, difficult but also very exciting
journey called the doctoral project.
At first I would like to thank my supervisors. I had the pleasure
to work with four brilliant gentlemen, two of them being my promoters.
Some people may find it difficult, but personally, I liked it a lot.
Dear Gilles, remember our first meeting? Because of the
carnival you had to wait for me at the train station more than an hour.
As it appeared later, that was not the last time you had to wait. You’re
the most patient and understanding boss I’ve ever had. Thank you for
the time we spent on designing experiments, discussing results and
papers. You taught me that there’s no such thing as a bad result, that
there’s no need to spend ages (really ages) on data analysis and that
my graphs won’t be more beautiful because they are already perfect.
You’re a word champion and a gold medalist in fighting with my
stubbornness, which is very impressive because it’s a heavyweight
category. Thank you for your faith and trust in me. It’s you who made
me believe I can send a paper to Epilepsia just like that. From that
moment I started shyly thinking about myself as a scientist. During
last Neuronus party somebody said “I want to be Gilles”. I’m not sure
whether the person meant your scientific achievements, dancing skills,
or both. Doesn’t matter, I want to be Gilles too!
Dear Marian, I said once that if I could choose a father, that
would be you. I keep in mind all of your advice but the one I like the
most is “Can you count Magda? Count on yourself!” I particularly like
it because, when I think about all these years in our department, the
advice is rubbish. I could always count on you! I really appreciate your
235
constant belief in me. Thank you for your support, encouragement to
apply for any scholarship or scientific school possible, for zillion of
recommendation letters you wrote for me and for a lesson how to cope
in the administrative jungle (that’s my second favorite piece of advice).
Thank you for all positive kicks and motivation. You’re a real
chronobiologist,
their
timing
was
just
perfect.
Speaking
of
chronobiology, looking at absence epilepsy from this point of view was
a fascinating adventure which wouldn’t be possible without your
background. I still remember your lectures for the first year students.
I thought then, oh my God I wish I could lecture like that one day.
Dear Ton, this project wouldn’t be possible without you. Your
warm feelings for Krakow and a friendship with Prof. Jan Kaiser let us
meet and discuss whether and why chronobiology of absence epilepsy
is worthy to investigate. Thank you for organizing things. Thank you
for the critical reviews of the manuscripts and perseverance in a
struggle with chaos inside. Thank you for all these questions “why” and
“how”. You made me improve the way of selling ideas to make them
understandable for the audience. Thank you for introducing me to the
cool young psychologists with whom I shared a building in Krakow but
surprisingly, I’ve never talked to before. With some of them, namely
Wiola Walentowska, Mirek Wyczesany and Kuba Szewczyk I lived an
exciting life of an external PhD student of the Radboud University
Nijmegen.
Dear Pim, I’m very grateful for the Janssen opportunity and
your support during my stay in Beerse. I’m truly amazed how you cope
with this crazy amount of work. I appreciate even more that you found
time for our project. It was a great experience to be a part of your
team. Please pass my kind regards to the members of the Department
of Neuroscience, Janssen R&D I had the pleasure to work with.
Dear Saskia (Menting-Hermeling), Heidi (Huysmans) and Hans
(Krijnen), thank you for your help during preparation and conducting
236
of the experiments. I wouldn’t manage without your excellent skills.
Big thank you and a hug ;) go to technicians-magicians: Gerard van
Oijen, Pascal de Water and Dirk Nuyts making incredible things with
the equipment and software, things I will never fully understand.
Dear Family, you have never had a choice – you must love me
form the definition ;). I’m pretty aware, however, how difficult it is. I
would like to thank you here for all this love, patience and
understanding. Agnieszka, Lucjan, Ola, Halina and Marian, during my
doctoral studies you witnessed a wide range of extreme behaviors:
from insane happiness to furious anger and hopeless depression. You
may think I’ve chosen a difficult path. I have. Science is not a piece of
cake (right uncle engineer?), but thanks to you I know who I am and I
know I will always manage. Thank you for the freedom of choice and
acceptance for all my decisions, especially those in opposition to what
you believe in. Thank you for letting me making my own mistakes and
learning by that. Thank you for being there for me. Always.
My dear girls: Becia, MES and Pati (alphabetical order, I love
you equally), as young people are saying nowadays, you’re my BFFs.
Thank you for your presence, your patience, kindness, for wild parties
and the ocean of alcohol we had together. We girls love to talk a lot.
Thank you for all discussions, especially those in which we differed
substantially. Of course, I appreciate gossiping too. It’s your super
power to deal with me when I’m so annoying that even I can’t stand
myself. I’m impressed by your threshold of tolerance. Having such
friends as three of you is a privilege – it’s good to be me :).
Dear Dirk, time flies but I still remember my first 2 – 3 years in
Nijmegen. What a wonderful, intense time. Thank you for showing me
some cool spots in the Netherlands and for giving me the best
impression of a Dutch gentleman. You were always very supportive and
you had a great faith in me. It meant a lot.
237
Pati and Dirk, thank you for standing next to me during the
defence. Right people at the right place – I couldn’t think of anybody
else.
Dear Margo, Emanuel, Valerio, Serhiy, Rustam, Jeroen - my
lovely Nijmegen team, thank you for the time we spent together, all
these crazy moments I will remember for the rest of my life. You made
my stay in the city truly awesome! What I appreciate the most is your
open mind and a shoulder always ready for me to cry on. Margo, I’m
pretty sure you’re well aware of your emotional support but do you
know about your contribution to the thesis? Let me explain. Every time
I started writing (it’s not my favorite thing to do) I heard our
conversation:
-
I wrote an introduction!
-
Congrats Margo!
-
No, no… The word “introduction”
This made the whole process a little bit more tolerable :).
Dear Gilles’ Angels: Annika, Lili, Mehrnoush and Valerio. It was
great to be a part of the team. Thank you for sharing an office, ideas,
technical know-how, tons of chocolate, cookies and sour candies. I
miss the moments we laugh so hard that the only thing we could do
afterwards was going home – any productive work was impossible.
Dear Biological Psychologists from Nijmegen, I wish I could
teleport myself to your coffee table every day. Thank you for a warm
welcome every time I came to Donders. I appreciate friendly
atmosphere and invaluable help regarding work-related and non-workrelated matters.
Dear Neurophysiologists and Chronobiologist from Krakow. It
was, and still, it is an honour to share a working space with you. Our
lab is a special place, as other jealous departments claim. It’s because
of you creating this great atmosphere with a perfect balance between
238
science and social activities. With you success tastes better, failures
hurt less.
Dear Huber and Lagrange Family, thank you for making me feel
at home during my stay in Nijmegen and Vosselaar. Being abroad alone
was not that difficult knowing that you’re around. Thank you for the
understanding for a Polish girl who, apart from a hard work of course,
was partying a lot, sleeping till midday, drinking tea instead of water
and was having problems with using a garage door to put her bike
inside ;).
Last but not least, Mr. Moś and Zdzisława, thank you guys for
showing me that life is not only about doing experiments and writing
papers. Ordinary things such as a full fridge, soft bed and willing
stroking hand matter as well. Thanks for showing me in practice how
the food-entrainable oscillator works and how the social cues set the
biological clock. That’s the real life science!
Podziękowania
Sukces ma wielu ojców (nie będziemy wspominać tutaj o
porażce! ;)). Jakie to prawdziwe. Nie osiągnęłabym tyle bez hojnego
wsparcia
wielu
wspaniałych
osób.
Chciałabym
wyrazić
swoją
wdzięczność wszystkim, którzy towarzyszyli mi w tej długiej, trudnej,
ale i niezwykle fascynującej drodze zwanej studiami doktoranckimi.
Na początku chciałabym podziękować moim przełożonym.
Miałam przyjemność pracować z czterema genialnymi dżentelmenami,
z których dwaj to moi promotorzy. Niektórzy sądzą, iż posiadanie tylu
szefów jest męczące, aczkolwiek, osobiście bardzo to lubiłam.
Drogi Gilles’u pamiętasz nasze pierwsze spotkanie? Z powodu
karnawału musiałeś czekać na mnie na dworcu ponad godzinę. Jak się
później okazało, nie był to ostatni raz, kiedy musiałeś na mnie czekać.
Jesteś
najbardziej
cierpliwym
i
wyrozumiałym
239
szefem,
jakiego
kiedykolwiek miałam. Dziękuję Ci za czas poświęcony na planowanie
eksperymentów, omawianie wyników i manuskryptów. Nauczyłeś
mnie, że nie ma czegoś takiego jak zły wynik, że nie ma potrzeby
spędzania wieków (naprawdę wieków) nad analizą danych i że moje
wykresy nie będą jeszcze piękniejsze, bo już są perfekcyjne. Jesteś
mistrzem świata i złotym medalistą w walce z moim uporem, co
imponuje tym bardziej, iż jest to waga ciężka. Dziękuję za wiarę we
mnie i za zaufanie. To Ty sprawiłeś, że uwierzyłam, że mogę wysłać
papier do Epilepsji – tak po prostu. Od tego momentu zaczęłam
nieśmiało postrzegać siebie, jako naukowca. Podczas ostatniej imprezy
Neuronusowej ktoś powiedział „chcę być Gilles’em”. Nie jestem pewna
czy osoba ta miała na myśli Twoje osiągnięcia naukowe, umiejętności
taneczne czy obie te rzeczy naraz. To bez znaczenia, ja też chcę być
Gilles’em!
Drogi Marianie, powiedziałam kiedyś, że gdybym mogła wybrać
sobie tatę to byłby to Pan. Przechowuję w pamięci wszystkie dobre
rady, które od Pana dostałam, ale moją ulubioną jest ta: „Umiesz liczyć
Magda? Licz na siebie!”. Lubię ją szczególnie, ponieważ gdy spoglądam
na te wszystkie lata spędzone w naszym zakładzie, ta rada to wierutna
bzdura. Zawsze mogłam liczyć na Pana! Naprawdę doceniam Pana
niezmienną wiarę we mnie. Dziękuję za wsparcie, za zachętę do
aplikowania o wszystkie możliwe stypendia i szkoły naukowe, za
zyliony listów rekomendacyjnych, które Pan dla mnie napisał i za lekcję
jak przetrwać w biurokratycznej dżungli (to moja druga ulubiona rada).
Dziękuje za pozytywne kopy i
motywację. Prawdziwy z Pana
chronobiolog, zachęta zawsze przychodziła w odpowiednim czasie.
Mówiąc o chronobiologii, badanie napadów nieświadomości pod tym
kątem nie byłoby możliwe bez Pana naukowych korzeni. Do dzisiaj
pamiętam Pana wykład dla studentów pierwszego roku. Pomyślałam
sobie wtedy: Boże, chciałabym tak kiedyś wykładać.
240
Drogi Tonie, ta praca nie doszłaby do skutku bez Twojego
wkładu. Twoja sympatia do Krakowa i przyjaźń z profesorem Janem
Kaiserem pozwoliły nam się spotkać i przedyskutować czy i dlaczego
warto badać napady nieświadomości pod kątem chronobiologicznym.
Dziękuję Ci za zorganizowanie wszystkiego. Dziękuję za krytyczne
recenzje naszych manuskryptów i niezłomną walkę z chaosem w nich
zawartym. Dziękuję za te wszystkie pytania „dlaczego” i „jak”. Dzięki
Tobie nauczyłam się lepiej „sprzedać” swoje pomysły i wyniki
słuchaczom i czytelnikom. Dziękuję za przedstawienie mnie fajowym
młodym psychologom, z którymi dzieliłam budynek w Krakowie, a z
którymi, nie wiedzieć czemu, nie zamieniłam wcześniej ani słowa. Z
niektórymi z nich, a mianowicie z Wiolą Walentowską, Mirkiem
Wyczesanym i Kubą Szewczykiem dzieliłam potem żywot studenta
Uniwersytetu Radboud w Nijmegen.
Drogi Pim’ie jestem bardzo wdzięczna za szansę pobytu w
Janssen’ie oraz Twoje wsparcie. Jestem pełna podziwu dla sposobu, w
jakim radzisz sobie z tym szalonym nawałem obowiązków. Jeszcze
bardziej doceniam więc, że znalazłeś czas na nasz projekt. Bycie
częścią Twojego zespołu to wspaniałe doświadczenie. Proszę, przekaż
moje ukłony członkom Department of Neuroscience, Janssen R&D, z
którymi miałam przyjemność pracować.
Droga Saskia (Menting-Hermeling), Heidi (Huysmans) oraz
Hans’ie
(Krijnen)
dziękuję
za
pomoc
w
przygotowaniu
i
przeprowadzeniu doświadczeń. Nie dałabym sobie rady bez waszych
niesamowitych umiejętności. Duże „dziękuję” oraz uściski wędrują do
moich techników-magików: Gerard’a van Oijen, Pascal’a de Water i
Dirk’a Nuyts, którzy wyczyniali ze sprzętem i oprogramowaniem takie
cuda, jakich nigdy do końca nie ogarnę.
Droga Rodzino, nigdy nie miałaś wyboru – musisz mnie kochać
z definicji ;). Jestem jednakże świadoma, jak trudne to zadanie. Z tego
miejsca chciałabym podziękować Wam za tą całą miłość, cierpliwość i
241
zrozumienie, którymi mnie obdarzyliście. Aguniu, Lucjanie, Olusiu,
ciociu Halinko i wujku Marianie, podczas moich studiów doktoranckich
dane
Wam
zachowań:
było
od
zaobserwować
szalonego
szeroką
szczęścia
przez
gamę
ekstremalnych
wściekły
gniew
do
beznadziejnej depresji. Możecie pomyśleć, że wybrałam trudną drogę.
Wybrałam. Nauka to nie bułka z masłem (prawda Wujku Inżynierze?),
ale dzięki Wam wiem, kim jestem i wiem, że zawsze dam sobie radę.
Dziękuję za wolność wyboru i akceptację dla wszystkich moich
wyborów, szczególnie dla tych, z którymi Wam nie po drodze. Dziękuję
za prawo do popełniania swoich własnych błędów i za naukę z nich
płynącą. Dziękuję, że jesteście przy mnie. Zawsze.
Moje
drogie
dziewczęta:
Beciu,
MES
i
Pati
(kolejność
alfabetyczna – kocham Was jednakowo), jak to mawia dzisiejsza
młodzież,
jesteście
moimi
BFF.
Dziękuję
za
Waszą
obecność,
cierpliwość i dobroć, za dzikie melanże i ocean alkoholu wypity razem.
My dziewczyny uwielbiamy dużo mówić. Dziękuję za wszystkie
rozmowy, a szczególnie te, w których znacznie się różniłyśmy.
Oczywiście, doceniam i ploteczki. Posiadacie niesamowitą super moc
radzenia sobie ze mną w momentach, w których nawet ja sama nie
mogę siebie znieść. Posiadanie takich przyjaciółek jak Wy to zaszczyt
– dobrze jest być mną :).
Drogi Dirk’u, czas mija nieubłaganie, ale nadal pamiętam moje
2 - 3 pierwsze lata w Nijmegen. Cóż za wspaniały, intensywny czas.
Dziękuję za pokazanie mi kilku fajowych miejsc w Holandii oraz za
najlepsze wrażenie odnośnie holenderskich dżentelmenów. Zawsze
mnie wspierałeś i zawsze we mnie wierzyłeś. To wiele dla mnie
znaczyło.
Pati i Dirk’u dziękuję za towarzyszenie mi podczas obrony.
Właściwi ludzie na właściwym miejscu – nie mogłam pomyśleć o nikim
innym.
242
Droga Margo, Emanuel’u Valerio, Serhiy, Rustam’ie i Jeroen’ie
– moja kochana drużyno z Nijmegen, dziękuję za wspólny czas i te
wszystkie szalone momenty, które pozostaną ze mną do końca życia.
Uczyniliście mój pobyt w mieście naprawdę odlotowym. Szczególnie
doceniam Wasze otwarte umysły i ramiona zawsze gotowe, aby się na
nich wypłakać. Margo, zapewne zdajesz sobie sprawę ze swojego
wkładu emocjonalnego, ale czy wiesz jak przyczyniłaś się do powstania
tej pracy? Za każdym razem, kiedy rozpoczynałam pisanie (co, jak
wiadomo, nie jest moją ulubioną czynnością) słyszałam naszą
rozmowę:
- Napisałam wstęp!
- Gratulacje Margo!
- nie, nie… słowo „wstęp”
Dzięki temu proces pisania stawał się odrobinę bardziej znośny.
Drogie Aniołki Gilles’a: Annika, Lili, Mehrnoush i Valerio.
Wspaniale było być częścią naszego teamu. Dziękuję za dzielenie
pokoju, pomysłów, technicznego know-how, ton czekolady, ciastek i
kwaśnych cukierków. Tęsknię za momentami, w których śmialiśmy się
tak mocno, że musieliśmy iść do domów, bo jakakolwiek praca była
niemożliwa.
Drodzy Biopsycholodzy z Nijmegen, żałuję, że nie mogę
teleportować się do Waszego stolika kawowego codziennie. Dziękuję
za ciepłe powitania z okazji moich wizyt w Donders’ie. Doceniam
przyjazną atmosferę i nieocenioną pomoc w sprawach zawodowych jak
również tych poza zawodowych.
Drodzy Neurofizjolodzy i Chronobiolodzy z Krakowa, zaszczytem
było, i jest nadal, dzielenie z Wami miejsca pracy. Jak potwierdzają
inne zazdrosne zakłady, nasz lab to szczególne miejsce. Szczególne,
ponieważ to właśnie Wy tworzycie niepowtarzalny klimat z idealną
równowagą pomiędzy nauką a życiem towarzyskim. Z Wami sukces
smakuje lepiej, porażki bolą mniej.
243
Droga rodzino Huber i Lagrange, dzięki Wam czułam się w
Nijmegen i Vosselaar jak w domu. Samotny pobyt za granicą nie był
taki
straszny
wiedząc,
że
jesteście
w
pobliżu.
Dziękuję
za
wyrozumiałość dla polskiej dziewczyny, która poza ciężką pracą
oczywiście, dużo imprezowała, spała do południa, piła herbatę zamiast
wody do obiadu i miała problemy z otwieraniem drzwi garażowych, aby
zaparkować rower w środku ;).
Ostatni, ale nie mniej ważni, Panie Mosiu i Zdzisławo, dziękuję
Wam za pokazanie mi, iż życie nie polega jedynie na przeprowadzaniu
doświadczeń i pisaniu artykułów. Zwykłe, codzienne rzeczy takie jak
pełna lodówka, wygodne łóżko i ręka chętna do głaskania są równie
ważne. Dzięki za pokazanie mi w praktyce jak działa oscylator
nastawiany pokarmem i jak bodźce socjalne nastawiają zegar
biologiczny. To są prawdziwe nauki przyrodnicze!
244
Donders Graduate School for Cognitive Neuroscience Series
1.
2.
3.
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5.
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7.
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15.
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17.
Van Aalderen-Smeets, S.I. (2007). Neural dynamics of visual selection. Maastricht
University, Maastricht, the Netherlands.
Schoffelen, J.M. (2007). Neuronal communication through coherence in the human
motor system. Radboud University Nijmegen, Nijmegen, the Netherlands.
De Lange, F.P. (2008). Neural mechanisms of motor imagery. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Grol, M.J. (2008). Parieto-frontal circuitry in visuomotor control. Utrecht
University, Utrecht, the Netherlands.
Bauer, M. (2008). Functional roles of rhythmic neuronal activity in the human visual
and somatosensory system. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Mazaheri, A. (2008). The influence of ongoing oscillatory brain activity on evoked
responses and behaviour. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Hooijmans, C.R. (2008). Impact of nutritional lipids and vascular factors in
Alzheimer’s disease. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Gaszner, B. (2008). Plastic responses to stress by the rodent urocortinergic EdingerWestphal nucleus. Radboud University Nijmegen, Nijmegen, the Netherlands.
Willems, R.M. (2009). Neural reflections of meaning in gesture, language and action.
Radboud University Nijmegen, Nijmegen, the Netherlands.
Van Pelt, S. (2009). Dynamic neural representations of human visuomotor space.
Radboud University Nijmegen, Nijmegen, the Netherlands.
Lommertzen, J. (2009). Visuomotor coupling at different levels of complexity.
Radboud University Nijmegen, Nijmegen, the Netherlands.
Poljac, E. (2009). Dynamics of cognitive control in task switching: Looking beyond the
switch cost. Radboud University Nijmegen, Nijmegen, the Netherlands.
Poser, B.A. (2009). Techniques for BOLD and blood volume weighted fMRI.
Radboud University Nijmegen, Nijmegen, the Netherlands.
Baggio, G. (2009). Semantics and the electrophysiology of meaning. Tense, aspect, event
structure. Radboud University Nijmegen, Nijmegen, the Netherlands.
Van Wingen, G.A. (2009). Biological determinants of amygdala functioning.
Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Bakker, M. (2009). Supraspinal control of walking: Lessons from motor imagery.
Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Aarts, E. (2009). Resisting temptation: The role of the anterior cingulate cortex in
adjusting cognitive control. Radboud University Nijmegen, Nijmegen, the
Netherlands.
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Prinz, S. (2009). Waterbath stunning of chickens – Effects of electrical parameters on
the electroencephalogram and physical reflexes of broilers. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Knippenberg, J.M.J. (2009). The N150 of the Auditory Evoked Potential from the
rat amygdala: In search for its functional significance. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Dumont, G.J.H. (2009). Cognitive and physiological effects of 3,4methylenedioxymethamphetamine (MDMA or ’ecstasy’) in combination with alcohol or
cannabis in humans. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Pijnacker, J. (2010). Defeasible inference in autism: A behavioral and electrophysiogical
approach. Radboud University Nijmegen, Nijmegen, the Netherlands.
De Vrijer, M. (2010). Multisensory integration in spatial orientation. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Vergeer, M. (2010). Perceptual visibility and appearance: Effects of color and form.
Radboud University Nijmegen, Nijmegen, the Netherlands.
Levy, J. (2010). In cerebro unveiling unconscious mechanisms during reading. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Treder, M. S. (2010). Symmetry in (inter)action. Radboud University Nijmegen,
Nijmegen, the Netherlands.
Horlings C.G.C. (2010). A weak balance: Balance and falls in patients with
neuromuscular disorders. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Snaphaan, L.J.A.E. (2010). Epidemiology of post-stroke behavioural consequences.
Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Dado – Van Beek, H.E.A. (2010). The regulation of cerebral perfusion in patients
with Alzheimer’s disease. Radboud University Nijmegen Medical Centre,
Nijmegen, the Netherlands.
Derks, N.M. (2010). The role of the non-preganglionic Edinger-Westphal nucleus in
sex-dependent stress adaptation in rodents. Radboud University Nijmegen,
Nijmegen, the Netherlands.
Wyczesany, M. (2010). Covariation of mood and brain activity. Integration of
subjective self-report data with quantitative EEG measures. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Beurze S.M. (2010). Cortical mechanisms for reach planning. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Van Dijk, J.P. (2010). On the Number of Motor Units. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Lapatki, B.G. (2010). The Facial Musculature - Characterization at a Motor Unit
Level. Radboud University Nijmegen, Nijmegen, the Netherlands.
Kok, P. (2010). Word order and verb inflection in agrammatic sentence production.
Radboud University Nijmegen, Nijmegen, the Netherlands.
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49.
50.
51.
52.
van Elk, M. (2010). Action semantics: Functional and neural dynamics. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Majdandzic, J. (2010). Cerebral mechanisms of processing action goals in self and
others. Radboud University Nijmegen, Nijmegen, the Netherlands.
Snijders, T.M. (2010). More than words - Neural and genetic dynamics of syntactic
unification. Radboud University Nijmegen, Nijmegen, the Netherlands.
Grootens, K.P. (2010). Cognitive dysfunction and effects of antipsychotics in
schizophrenia and borderline personality disorder. Radboud University Nijmegen
Medical Centre, Nijmegen, the Netherlands.
Nieuwenhuis, I.L.C. (2010). Memory consolidation: A process of integration –
Converging evidence from MEG, fMRI and behavior. Radboud University
Nijmegen Medical Centre, Nijmegen, the Netherlands.
Menenti, L.M.E. (2010). The right language: Differential hemispheric contributions to
language production and comprehension in context. Radboud University Nijmegen,
Nijmegen, the Netherlands.
Van Dijk, H.P. (2010). The state of the brain, how alpha oscillations shape behaviour
and event related responses. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Meulenbroek, O.V. (2010). Neural correlates of episodic memory in healthy aging and
Alzheimer’s disease. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Oude Nijhuis, L.B. (2010). Modulation of human balance reactions. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Qin, S. (2010). Adaptive memory: Imaging medial temporal and prefrontal memory
systems. Radboud University Nijmegen, Nijmegen, the Netherlands.
Timmer, N.M. (2011). The interaction of heparan sulfate proteoglycans with the
amyloid protein. Radboud University Nijmegen, Nijmegen, the Netherlands.
Crajé, C. (2011). (A)typical motor planning and motor imagery. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Van Grootel, T.J. (2011). On the role of eye and head position in spatial localisation
behaviour. Radboud University Nijmegen, Nijmegen, the Netherlands.
Lamers, M.J.M. (2011). Levels of selective attention in action planning. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Van der Werf, J. (2011). Cortical oscillatory activity in human visuomotor integration.
Radboud University Nijmegen, Nijmegen, the Netherlands.
Scheeringa, R. (2011). On the relation between oscillatory EEG activity and the
BOLD signal. Radboud University Nijmegen, Nijmegen, the Netherlands.
Bögels, S. (2011). The role of prosody in language comprehension: When prosodic
breaks and pitch accents come into play. Radboud University Nijmegen,
Nijmegen, the Netherlands.
Ossewaarde, L. (2011). The mood cycle: Hormonal influences on the female brain.
Radboud University Nijmegen, Nijmegen, the Netherlands.
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63.
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67.
Kuribara, M. (2011). Environment-induced activation and growth of pituitary
melanotrope cells of Xenopus laevis. Radboud University Nijmegen, Nijmegen,
the Netherlands.
Helmich, R.C.G. (2011). Cerebral reorganization in Parkinson’s disease. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Boelen, D. (2011). Order out of chaos? Assessment and treatment of executive
disorders in brain-injured patients. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Koopmans, P.J. (2011). fMRI of cortical layers. Radboud University Nijmegen,
Nijmegen, the Netherlands.
van der Linden, M.H. (2011). Experience-based cortical plasticity in object category
representation. Radboud University Nijmegen, Nijmegen, the Netherlands.
Kleine, B.U. (2011). Motor unit discharges - Physiological and diagnostic studies in
ALS. Radboud University Nijmegen Medical Centre, Nijmegen, the
Netherlands.
Paulus, M. (2011). Development of action perception: Neurocognitive mechanisms
underlying children’s processing of others’ actions. Radboud University Nijmegen,
Nijmegen, the Netherlands.
Tieleman, A.A. (2011). Myotonic dystrophy type 2. A newly diagnosed disease in the
Netherlands. Radboud University Nijmegen Medical Centre, Nijmegen, the
Netherlands.
Van Leeuwen, T.M. (2011). ‘How one can see what is not there’: Neural mechanisms
of grapheme-colour synaesthesia. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Van Tilborg, I.A.D.A. (2011). Procedural learning in cognitively impaired patients
and its application in clinical practice. Radboud University Nijmegen, Nijmegen,
the Netherlands.
Bruinsma, I.B. (2011). Amyloidogenic proteins in Alzheimer’s disease and
Parkinson’s disease: Interaction with chaperones and inflammation. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Voermans, N. (2011). Neuromuscular features of Ehlers-Danlos syndrome and
Marfan syndrome; expanding the phenotype of inherited connective tissue disorders and
investigating the role of the extracellular matrix in muscle. Radboud University
Nijmegen Medical Centre, Nijmegen, the Netherlands.
Reelick, M. (2011). One step at a time. Disentangling the complexity of preventing falls
in frail older persons. Radboud University Nijmegen Medical Centre, Nijmegen,
the Netherlands.
Buur, P.F. (2011). Imaging in motion. Applications of multi-echo fMRI. Radboud
University Nijmegen, Nijmegen, the Netherlands.
Schaefer, R.S. (2011). Measuring the mind’s ear: EEG of music imagery. Radboud
University Nijmegen, Nijmegen, the Netherlands.
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71.
72.
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74.
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76.
77.
78.
79.
80.
81.
82.
Xu, L. (2011). The non-preganglionic Edinger-Westphal nucleus: An integration center
for energy balance and stress adaptation. Radboud University Nijmegen,
Nijmegen, the Netherlands.
Schellekens, A.F.A. (2011). Gene-environment interaction and intermediate
phenotypes in alcohol dependence. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Van Marle, H.J.F. (2011). The amygdala on alert: A neuroimaging investigation into
amygdala function during acute stress and its aftermath. Radboud University
Nijmegen, Nijmegen, the Netherlands.
De Laat, K.F. (2011). Motor performance in individuals with cerebral small vessel
disease: An MRI study. Radboud University Nijmegen Medical Centre,
Nijmegen, the Netherlands.
Mädebach, A. (2011). Lexical access in speaking: Studies on lexical selection and
cascading activation. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Poelmans, G.J.V. (2011). Genes and protein networks for neurodevelopmental
disorders. Radboud University Nijmegen, Nijmegen, the Netherlands.
Van Norden, A.G.W. (2011). Cognitive function in elderly individuals with cerebral
small vessel disease. An MRI study. Radboud University Nijmegen Medical
Centre, Nijmegen, the Netherlands.
Jansen, E.J.R. (2011). New insights into V-ATPase functioning: the role of its
accessory subunit Ac45 and a novel brain-specific Ac45 paralog. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Haaxma, C.A. (2011). New perspectives on preclinical and early stage Parkinson's
disease. Radboud University Nijmegen Medical Centre, Nijmegen, the
Netherlands.
Haegens, S. (2012). On the functional role of oscillatory neuronal activity in the
somatosensory system. Radboud University Nijmegen, Nijmegen, the
Netherlands.
van Barneveld, D.C.P.B.M. (2012). Integration of exteroceptive and interoceptive cues
in spatial localization. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Spies, P.E. (2012). The reflection of Alzheimer disease in CSF. Radboud
University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Helle, M. (2012). Artery-specific perfusion measurements in the cerebral vasculature by
magnetic resonance imaging. Radboud University Nijmegen, Nijmegen, the
Netherlands.
Egetemeir, J. (2012). Neural correlates of real-life joint action. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Janssen, L. (2012). Planning and execution of (bi)manual grasping. Radboud
University Nijmegen, Nijmegen, the Netherlands.
249
83.
Vermeer, S. (2012). Clinical and genetic characterisation of autosomal recessive
cerebellar ataxias. Radboud University Nijmegen Medical Centre, Nijmegen,
the Netherlands.
84.
Vrins, S. (2012). Shaping object boundaries: Contextual effects in infants and
adults. Radboud University Nijmegen, Nijmegen, the Netherlands.
Weber, K.M. (2012). The language learning brain: Evidence from second
language and bilingual studies of syntactic processing. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Verhagen, L. (2012). How to grasp a ripe tomato. Utrecht University,
Utrecht, the Netherlands.
Nonkes, L.J.P. (2012). Serotonin transporter gene variance causes individual
differences in rat behaviour: For better and for worse. Radboud University
Nijmegen Medical Centre, Nijmegen, the Netherlands.
85.
86.
87.
88.
89.
90.
91.
92.
93.
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95.
96.
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98.
Joosten-Weyn Banningh, L.W.A. (2012). Learning to live with Mild Cognitive
Impairment: development and evaluation of a psychological intervention for patients with
Mild Cognitive Impairment and their significant others. Radboud University
Nijmegen Medical Centre, Nijmegen, the Netherlands.
Xiang, HD. (2012). The language networks of the brain. Radboud University
Nijmegen, Nijmegen, the Netherlands.
Snijders, A.H. (2012). Tackling freezing of gait in Parkinson's disease. Radboud
University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Rouwette, T.P.H. (2012). Neuropathic pain and the brain - Differential involvement
of corticotropin-releasing factor and urocortin 1 in acute and chronic pain processing.
Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Van de Meerendonk, N. (2012). States of indecision in the brain:
Electrophysiological and hemodynamic reflections of monitoring in visual language
perception. Radboud University Nijmegen, Nijmegen, the Netherlands.
Sterrenburg, A. (2012). The stress response of forebrain and midbrain regions:
Neuropeptides, sex-specificity and epigenetics. Radboud University Nijmegen,
Nijmegen, The Netherlands.
Uithol, S. (2012). Representing action and intention. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Van Dam, W.O. (2012). On the specificity and flexibility of embodied lexicalsemantic representations. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Slats, D. (2012). CSF biomarkers of Alzheimer’s disease: Serial sampling analysis
and the study of circadian rhythmicity. Radboud University Nijmegen Medical
Centre, Nijmegen, the Netherlands.
Van Nuenen, B.F.L. (2012). Cerebral reorganization in premotor parkinsonism.
Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
van Schouwenburg, M.R. (2012). Fronto-striatal mechanisms of attentional control.
Radboud University Nijmegen, Nijmegen, The Netherlands.
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113.
Azar, M.G. (2012). On the theory of reinforcement learning: Methods, convergence
analysis and sample complexity. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Meeuwissen, E.B. (2012). Cortical oscillatory activity during memory formation.
Radboud University Nijmegen, Nijmegen, The Netherlands.
Arnold, J.F. (2012). When mood meets memory: Neural and behavioral perspectives on
emotional memory in health and depression. Radboud University Nijmegen,
Nijmegen, The Netherlands.
Gons, R.A.R. (2012). Vascular risk factors in cerebral small vessel disease: A
diffusion tensor imaging study. Radboud University Nijmegen Medical Centre,
Nijmegen, the Netherlands.
Wingbermühle, E. (2012). Cognition and emotion in adults with Noonan syndrome:
A neuropsychological perspective. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Walentowska, W. (2012). Facing emotional faces. The nature of automaticity of facial
emotion processing studied with ERPs. Radboud University Nijmegen, Nijmegen,
The Netherlands.
Hoogman, M. (2012). Imaging the effects of ADHD risk genes. Radboud
University Nijmegen, Nijmegen, The Netherlands.
Tramper, J. J. (2012). Feedforward and feedback mechanisms in sensory motor control.
Radboud University Nijmegen, Nijmegen, The Netherlands.
Van Eijndhoven, P. (2012). State and trait characteristics of early course major
depressive disorder. Radboud University Nijmegen Medical Centre, Nijmegen,
the Netherlands.
Visser, E. (2012). Leaves and forests: Low level sound processing and methods for the
large-scale analysis of white matter structure in autism. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Van Tooren-Hoogenboom, N. (2012). Neuronal communication in the
synchronized brain. Investigating the functional role of visually-induced gamma band
activity: Lessons from MEG. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Henckens, M.J.A.G. (2012). Imaging the stressed brain. Elucidating the time- and
region-specific effects of stress hormones on brain function: A translational approach.
Radboud University Nijmegen, Nijmegen, The Netherlands.
Van Kesteren, M.T.R. (2012). Schemas in the brain: Influences of prior knowledge on
learning, memory, and education. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Brenders, P. (2012). Cross-language interactions in beginning second language learners.
Radboud University Nijmegen, Nijmegen, The Netherlands.
Ter Horst, A.C. (2012). Modulating motor imagery. Contextual, spatial and
kinaesthetic influences. Radboud University Nijmegen, Nijmegen, The
Netherlands.
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114. Tesink, C.M.J.Y. (2013). Neurobiological insights into language comprehension in
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
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autism: Context matters. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Böckler, A. (2013). Looking at the world together. How others’ attentional relations to
jointly attended scenes shape cognitive processing. Radboud University Nijmegen,
Nijmegen, The Netherlands.
Van Dongen, E.V. (2013). Sleeping to Remember. On the neural and behavioral
mechanisms of sleep-dependent memory consolidation. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Volman, I. (2013). The neural and endocrine regulation of emotional actions.
Radboud University Nijmegen, Nijmegen, The Netherlands.
Buchholz, V. (2013). Oscillatory activity in tactile remapping. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Van Deurzen, P.A.M. (2013). Information processing and depressive symptoms in
healthy adolescents. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Whitmarsh, S. (2013). Nonreactivity and metacognition in mindfulness. Radboud
University Nijmegen, Nijmegen, The Netherlands.
Vesper, C. (2013). Acting together: Mechanisms of intentional coordination.
Radboud University Nijmegen, Nijmegen, The Netherlands.
Lagro, J. (2013). Cardiovascular and cerebrovascular physiological measurements in
clinical practice and prognostics in geriatric patients. Radboud University Nijmegen
Medical Centre, Nijmegen, the Netherlands.
Eskenazi, T.T. (2013). You, us & them: From motor simulation to ascribed shared
intentionality in social perception. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Ondobaka, S. (2013). On the conceptual and perceptual processing of own and others’
behavior. Radboud University Nijmegen, Nijmegen, The Netherlands.
Overvelde, J.A.A.M. (2013). Which practice makes perfect? Experimental studies on
the acquisition of movement sequences to identify the best learning condition in good and
poor writers. Radboud University Nijmegen, Nijmegen, The Netherlands.
Kalisvaart, J.P. (2013). Visual ambiguity in perception and action. Radboud
University Nijmegen Medical Centre, Nijmegen, The Netherlands.
Kroes, M. (2013). Altering memories for emotional experiences. Radboud
University Nijmegen, Nijmegen, The Netherlands.
Duijnhouwer, J. (2013). Studies on the rotation problem in self-motion perception.
Radboud University Nijmegen, Nijmegen, The Netherlands.
Nijhuis, E.H.J (2013). Macroscopic networks in the human brain: Mapping
connectivity in healthy and damaged brains. University of
Twente, Enschede, The
Netherlands
252
130. Braakman, M. H. (2013). Posttraumatic stress disorder with secondary psychotic
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
features. A diagnostic validity study among refugees in the Netherlands. Radboud
University Nijmegen, Nijmegen, The Netherlands.
Zedlitz, A.M.E.E. (2013). Brittle brain power. Post-stroke fatigue, explorations into
assessment and treatment. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Schoon, Y. (2013). From a gait and falls clinic visit towards self-management of falls
in frail elderly. Radboud University Nijmegen Medical Centre, Nijmegen, The
Netherlands.
Jansen, D. (2013). The role of nutrition in Alzheimer's disease - A study in transgenic
mouse models for Alzheimer's disease and vascular disorders. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Kos, M. (2013). On the waves of language - Electrophysiological reflections on semantic
and syntactic processing. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Severens, M. (2013). Towards clinical BCI applications: Assistive technology and gait
rehabilitation. Radboud University Nijmegen, Nijmegen, Sint Maartenskliniek,
Nijmegen, The Netherlands.
Bergmann, H. (2014). Two is not always better than one: On the functional and
neural (in)dependence of working memory and long-term memory. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Wronka, E. (2013). Searching for the biological basis of human mental abilitites. The
relationship between attention and intelligence studied with P3. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Lüttjohann, A.K. (2013). The role of the cortico-thalamo-cortical system in absence
epilepsy. Radboud University Nijmegen, Nijmegen, The Netherlands.
Brazil, I.A. (2013). Change doesn’t come easy: Dynamics of adaptive behavior in
psychopathy. Radboud University Nijmegen, Nijmegen, The Netherlands.
Zerbi, V. (2013). Impact of nutrition on brain structure and function. A magnetic
resonance imaging approach in Alzheimer mouse models. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Delnooz, C.C.S. (2014). Unravelling primary focal dystonia. A treatment update and
new pathophysiological insights. Radboud University Nijmegen Medical Centre,
Nijmegen, The Netherlands.
Bultena, S.S. (2013). Bilingual processing of cognates and language switches in sentence
context. Radboud University Nijmegen, Nijmegen, The Netherlands.
Janssen, G. (2014). Diagnostic assessment of psychiatric patients: A contextual
perspective on executive functioning. Radboud University Nijmegen, Nijmegen,
The Netherlands.
Piai, V. Magalhães (2014). Choosing our words: Lexical competition and the
involvement of attention in spoken word production. Radboud University Nijmegen,
Nijmegen, The Netherlands.
253
145. Van Ede, F. (2014). Preparing for perception. On the attentional modulation,
146.
147.
148.
149.
150.
perceptual relevance and physiology of oscillatory neural activity. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Brandmeyer, A. (2014). Auditory perceptual learning via decoded EEG
neurofeedback: a novel paradigm. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Radke, S. (2014). Acting social: Neuroendocrine and clinical modulations of approach
and decision behavior. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Simanova, I. (2014). In search of conceptual representations in the brain: towards
mind-reading. Radboud University Nijmegen, Nijmegen, The Netherlands.
Kok, P. (2014). On the role of expectation in visual perception: A top-down view of
early visual cortex. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Van Geldorp, B. (2014). The long and the short of memory: Neuropsychological
studies on the interaction of working memory and long-term memory
formation. Radboud University Nijmegen, Nijmegen, The Netherlands.
151. Meyer, M. (2014). The developing brain in action - Individual and joint action
152.
153.
154.
155.
156.
157.
158.
159.
160.
processing. Radboud University Nijmegen, Nijmegen, The Netherlands.
Wester, A. (2014). Assessment of everyday memory in patients with alcohol-related
cognitive disorders using the Rivermead Behavioural Memory Test. Radboud
University Nijmegen, Nijmegen, The Netherlands.
Koenraadt, K. (2014). Shedding light on cortical control of movement. Radboud
University Nijmegen, Nijmegen; Sint Maartenskliniek, Nijmegen, The
Netherlands.
Rutten-Jacobs, L.C.A. (2014). Long-term prognosis after stroke in young adults.
Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
Herbert, M.K. (2014). Facing uncertain diagnosis: the use of CSF biomarkers for the
differential diagnosis of neurodegenerative diseases. Radboud University Nijmegen
Medical Centre, Nijmegen, The Netherlands.
Llera Arenas, A. (2014). Adapting brain computer interfaces for non-stationary
changes. Radboud University Nijmegen, Nijmegen, The Netherlands.
Smulders, K. (2014). Cognitive control of gait and balance in patients with chronic
stroke and Parkinson's disease. Radboud University Nijmegen Medical Centre,
Nijmegen, The Netherlands.
Boyacioglu, R. (2014). On the application of ultra-fast fMRI and high resolution
multiband fMRI at high static field strengths. Radboud University Nijmegen,
Nijmegen, The Netherlands.
Kleinnijenhuis, M. (2014). Imaging fibres in the brain. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Geuze, J. (2014). Brain Computer Interfaces for Communication: Moving
beyond the visual speller. Radboud University Nijmegen, Nijmegen, The
Netherlands.
254
161. Platonov, A. (2014). Mechanisms of binocular motion rivalry. Radboud
University Nijmegen, Nijmegen, The Netherlands.
162. Van der Schaaf, M.E. (2014). Dopaminergic modulation of reward and punishment
163.
164.
165.
166.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
learning. Radboud University Nijmegen Medical Centre, Nijmegen, The
Netherlands.
Aerts, M.B. (2014). Improving diagnostic accuracy in parkinsonism. Radboud
University Nijmegen Medical Centre, Nijmegen, The Netherlands.
Vlek, R. (2014). From Beat to BCI: A musical paradigm for, and the ethical
aspects of Brain-Computer Interfacing. Radboud University Nijmegen,
Nijmegen, The Netherlands.
Massoudi, R. (2014). Interaction of task-related and acoustic signals in single neurons
of monkey auditory cortex. Radboud University Nijmegen, Nijmegen, The
Netherlands
Stolk, A. (2014). On the generation of shared symbols. Radboud University
Nijmegen, Nijmegen, The Netherlands.
Krause F. (2014). Numbers and magnitude in the brain: A sensorimotor grounding of
numerical cognition. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Munneke, M.A.M. (2014). Measuring and modulating the brain with non-invasive
stimulation. Radboud University Nijmegen Medical Centre, Nijmegen, The
Netherlands.
Von Borries, K. (2014). Carrots & Sticks - a neurobehavioral investigation of
affective outcome processing in psychopathy. Radboud University Nijmegen Medical
Centre, Nijmegen, The Netherlands.
Meel-van den Abeelen, A.S.S. (2014). In control. Methodological and clinical
aspects of cerebral autoregulation and haemodynamics. Radboud University
Nijmegen Medical Centre, Nijmegen, The Netherlands.
Leoné, F.T.M. (2014). Mapping sensorimotor space: Parieto-frontal contributions to
goal-directed movements. Radboud University Nijmegen, Nijmegen, The
Netherlands.
Van Kessel, M. (2014). Nothing left? How to keep on the right track - Spatial and
non-spatial attention processes in neglect after stroke. Radboud University,
Nijmegen, The Netherlands.
Vulto-van Silfhout, A. T. (2014). Detailed, standardized and systematic phenotyping
for the interpretation of genetic variation. Radboud University Medical Centre,
Nijmegen, The Netherlands.
Arnoldussen, D. (2015). Cortical topography of self-motion perception. Radboud
University, Nijmegen, The Netherlands.
Meyer, M.C. (2015). “Inbetween Modalitiers combined EEG – fMR”. Radboud
University, Nijmegen, The Netherlands.
Bralten, J. (2015). Genetic factors and the brain in ADHD. Radboud university
medical center, Nijmegen, The Netherlands.
255
177. Spaak, E. (2015). On the role of alpha oscillations in structuring neural information
processing. Radboud University, Nijmegen, The Netherlands.
178. Van der Doelen, R. (2015). Translational psychiatry; the twists and turns of early life
179.
180.
181.
182.
183.
184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
stress and serotonin transporter gene variation. Radboud university medical center,
Nijmegen, The Netherlands.
Lewis, C. (2015). The structure and function of intrinsic brain activity. Radboud
University, Nijmegen, The Netherlands.
Huang, Lili. (2015). The subiculum: a promising new target of deep brain stimulation
in temporal lobe epilepsy. Investigation of closed-loop and open-loop high frequency
stimulation of the subiculum in seizure and epilepsy models in rats. Radboud
University, Nijmegen, The Netherlands.
Maaijwee, N.A.M.M (2015). Long-term neuropsychological and social consequences
after stroke in young adults. Radboud university medical center, Nijmegen, The
Netherlands.
Meijer, F.J.A. (2015). Clinical Application of Brain MRI in Parkinsonism: From
Basic to Advanced Imaging. Radboud university medical center, Nijmegen, The
Netherlands.
Van der Meij, R. (2015). On the identification, characterization and investigation of
phase dependent coupling in neuronal networks. Radboud University, Nijmegen,
The Netherlands.
Todorovic, A. (2015). Predictive adaptation in early auditory processing. Radboud
University, Nijmegen, The Netherlands.
Horschig, J.M. (2015). Flexible control and training of posterior alpha-band
oscillations. Radboud University, Nijmegen, The Netherlands.
Vogel, S. (2015). The runner-up: on the role of the mineralocorticoid receptor in human
cognition. Radboud University Medical Center, Nijmegen, The Netherlands.
Clemens, I.A.H. (2015). Multisensory integration in orientation and self-motion
perception. Radboud University, Nijmegen, The Netherlands.
Nonnekes, J. (2015). Balance and gait in neurodegenerative disease: what startle tells
us about motor control. Radboud university medical center, Nijmegen, The
Netherlands.
Stapel, J.C. (2015). Action prediction and the development thereof. Radboud
University, Nijmegen, The Netherlands.
De Grauwe, S. (2015). The Processing of Derivations in Native and Non-Native
Speakers of Dutch. Radboud University, Nijmegen, The Netherlands.
Atucha Treviño, E. (2015). Emotional Modulation of Memory: Mechanisms
underlying strength and accuracy of Memory. Radboud University, Nijmegen, The
Netherlands
Bosch, M.P.C. (2015) Needles on the Couch; acupuncture in the Treatment of
Depression, Schizophrenia and Sleep Disorders. Radboud University, Nijmegen,
The Netherlands.
Marshall, T. (2015) On the Control and manipulation of Alpha and Gamma
oscillations in visual cortex. Radboud University, Nijmegen, The Netherlands.
256
194. Rijken, N. (2015). Balance and gait in facioscapulohumeral muscular dystrophy,
relations with individual muscle involvement. Radboud University Medical Center,
Nijmegen, The Netherlands.
195. Blokpoel, M. (2015). Understanding understanding: A computational-level perspective.
Radboud University, Nijmegen, The Netherlands.
196. Smyk, M. (2015). Chronobiology of absence epilepsy. Radboud University,
Nijmegen, The Netherlands.
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