Behavioural Brain Research 180 (2007) 48–61
Research report
Dissociable learning-dependent changes in REM and non-REM
sleep in declarative and procedural memory systems
Stuart M. Fogel a , Carlyle T. Smith b , Kimberly A. Cote a,∗
a
Brock University, St. Catharines, Ontario, Canada
Trent University, Peterborough, Ontario, Canada
b
Received 17 August 2006; received in revised form 14 February 2007; accepted 19 February 2007
Available online 28 February 2007
Abstract
Sleep spindles and rapid eye movements have been found to increase following an intense period of learning on a combination of procedural
memory tasks. It is not clear whether these changes are task specific, or the result of learning in general. The current study investigated changes
in spindles, rapid eye movements, K-complexes and EEG spectral power following learning in good sleepers randomly assigned to one of four
learning conditions: Pursuit Rotor (n = 9), Mirror Tracing (n = 9), Paired Associates (n = 9), and non-learning controls (n = 9). Following Pursuit
Rotor learning, there was an increase in the duration of Stage 2 sleep, spindle density (number of spindles/min), average spindle duration, and
an increase in low frequency sigma power (12–14 Hz) at occipital regions during SWS and at frontal regions during Stage 2 sleep in the second
half of the night. These findings are consistent with previous findings that Pursuit Rotor learning is consolidated during Stage 2 sleep, and provide
additional data to suggest that spindles across all non-REM stages may be a mechanism for brain plasticity. Following Paired Associates learning,
theta power increased significantly at central regions during REM sleep. This study provides the first evidence that REM sleep theta activity is
involved in declarative memory consolidation. Together, these findings support the hypothesis that brain plasticity during sleep does not involve
a unitary process; that is, different types of learning have unique sleep-related memory consolidation mechanisms that act in dissociable brain
regions at different times throughout the night.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Sleep spindles; Stage 2 sleep; REM sleep; Learning; Memory
1. Dissociable learning-dependent changes in REM and
non-REM sleep in declarative and procedural memory
systems
There is now strong evidence in both humans and animals from behavioural, developmental, neural, and molecular
experiments to support the hypothesis that sleep states play
an important role in the consolidation of memory [For review
see 41,52,66,75]. Rapid eye movement (REM) sleep has been
identified as being particularly important for memory consolidation in both animals [14,28,40,43,44,63,65], and humans
[42,66,70,74]. More recently, Stage 2 sleep has also been
reported to be important for memory consolidation of certain
∗ Corresponding author at: Brock University Sleep Research Laboratory,
Psychology Department, Brock University, St. Catharines, Ontario L2S 3A1,
Canada. Tel.: +1 905 688 5550x4806; fax: +1 905 688 6922.
E-mail address: [email protected] (K.A. Cote).
0166-4328/$ – see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.bbr.2007.02.037
kinds of tasks [21,24,49,60,69]. It has been suggested [66] that in
humans, procedural tasks (usually implicit) require REM sleep
for efficient memory consolidation. In particular, improvement
on tasks such as the Wff’n Proof Task [68,72,64], the Tower of
Hanoi [12,70], and the Mirror Tracing Task [56,70] require REM
sleep for normal improvement. One common characteristic of
these memory tasks is that they involve learning of a complex or
novel rule, or a procedure to improve task performance. Traditionally, memory tasks have been categorized primarily based on
the pattern of impaired performance observed following brain
damage in amnesic patients. Gabrielli [23] has categorized both
the Mirror Tracing Task and the Pursuit Rotor as Sensorimotor
tasks, and tasks such as the Tower of Hanoi as a cognitive skills
task. Evidence for the distinction between these tasks comes
from learning deficits observed in both brain injured populations and in dementia observed in neurodegenerative disease. In
the past, our group [66] has organized tasks according to the type
of sleep deprivation which impairs performance, and according
to the stage of sleep that is affected by learning. Tasks such as
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
the Mirror Tracing Task, and the Tower of Hanoi have distinct
characteristics from tasks such as the Pursuit Rotor in that they
seem to require the use of new cognitive strategies before they
can be solved; these have been referred to as “cognitive procedural” [66,65]. Recently, researchers have described a relationship
between Stage 2 sleep and procedural tasks including the Pursuit Rotor which have been described as simple procedural tasks
[21]. These tasks have very simple cognitive attributes, primarily involve implicit motor skills learning, and do not appear to
require the acquisition of any new cognitive strategy for task
improvement [67]. Tasks such as the ball-and-cup task [47], the
simple tracing task [2], the Pursuit Rotor [69], and the fingertapping task [80] have been reported to be dependent on Stage 2
sleep for maximum learning efficiency. More recently, we have
identified initial skill level to be an important determinant of
the nature of the changes observed in sleep after learning [54].
In low-skill individuals, learning-dependent changes in REM
sleep are observed, whereas in high-skill individuals, learningdependent changes in Stage 2 sleep are observed. Thus, it seems
that when learning novel tasks which are more difficult for the
individual to acquire, the task appears to be REM sleep dependent, whereas when only the refinement of existing well-learned
skills are improved upon, the task appears to be Stage 2 sleep
dependent. Declarative memory (usually explicit) has, to this
point, been less clearly related to any stage of sleep [3,66],
although recent work suggests that non-rapid eye movement
(NREM) sleep state characteristics may be correlated with the
acquisition of declarative material [26].
Fogel and Smith [21] found that motor skills learning
increased the duration of Stage 2 sleep and sleep spindle density (spindles/minute), and that the increase in the number of
sleep spindles was correlated with task performance improvement. A number of questions remained unanswered, including
the topographic distribution of the phenomena, possible qualitative differences in the spindles compared to baseline and
non-learning control values, and whether the increases in spindles were confined solely to Stage 2 sleep as opposed to slow
wave sleep (SWS), i.e., Non-REM sleep phenomena in general.
Siapas and Wilson [61] found that sleep spindles during SWS
are temporally correlated with hippocampal ripples. Both sleep
spindles and hippocampal ripples have been hypothesized to
be mechanisms for memory consolidation, which suggests that
sleep spindles during SWS (or NREM sleep in general) may be
involved in the interplay between hippocampal and neocortical
structures and may be involved in the consolidation of newly
formed memory traces during sleep. A recent series of studies [9,10] have demonstrated that the number of automatically
detected sleep spindles in left fronto-central regions are correlated with free recall of verbal material, while sleep spindles in
centro-parietal regions are correlated with visual spatial memory performance. The present study was designed to investigate
changes in Stage 2, SWS (Stages 3 and 4), and REM sleep after
new learning. Three different types of memory tasks (Pursuit
Rotor, Mirror Tracing, and Paired Associates) were chosen to
investigate the changes to sleep states.
Based on the results of Fogel and Smith [21], it was hypothesized that following learning on the Pursuit Rotor, an increase
49
in spindles and the duration of Stage 2 sleep would be observed
over baseline levels. No change in the number of rapid eye movements (REMs) or the duration of REM sleep was expected after
Pursuit Rotor learning, since this type of learning requires the
refinement of motor skills without the acquisition of new cognitive rules or strategies, and memory performance appears to
be uniquely sensitive to Stage 2 sleep interruption [69], and
because there was no change in REM sleep following Pursuit
Rotor learning in a previous study [21]. It has been found that
sigma power does not change in response to declarative learning
despite changes in sleep spindle activity [26]. Brain activity in
the sigma range includes both sigma which is associated with
sleep spindles, and sigma that does not originate from sleep
spindle activity. If non-spindle sigma is independent of spindle activity, then only robust changes in sleep spindles would
be expected to affect sigma power over and above non-spindle
sigma. A recent study [21] has shown that robust changes in sleep
spindles occur following Pursuit Rotor learning, which could be
large enough to affect sigma power during Stage 2 sleep. Thus, it
was expected that sigma power during Stage 2, and SWS would
follow the same pattern as visually detected sleep spindles, that
is, increase only following Pursuit Rotor learning. Investigating the topographic distribution of sigma power may provide
additional information regarding the underlying brain structures
involved in the generation of spindle activity, and allow the
differentiation between frontal slow activity and posterior fast
activity. If large enough increases in sleep spindles are observed,
then presumably sigma power should also increase, since sleep
spindles oscillate in the 12–16 Hz range. Learning-dependent
changes in Stage 2 sleep would be expected to be largest in the
second half of the night [80]. We also investigated the K-complex
as a potential marker of memory consolidation during sleep since
it is another phasic event that is characteristic of non-REM sleep.
It has been demonstrated using animal models that the combined
effect of sleep spindles and slow oscillations during NREM sleep
leads to the production of K-complexes [73]. However, there is
debate whether the K-complex generator is independent of the
spindle generators [13] and there is little behavioural evidence to
suggest that the sleep spindle and the K-complex serve functions
related to synaptic plasticity. We therefore hypothesized that the
number of K-complexes would not increase following new learning, and that spindle-dependent memory consolidation would be
dissociable from K-complex activity. Next, it was hypothesized
that following learning on the Mirror Tracing Task there would
be an increase in number and density of rapid eye movements
during REM sleep. No change was expected in sleep spindles or
Stage 2 sleep as a result of Mirror Trace learning since this task
requires the acquisition of new cognitive rules or strategies to
improve performance and since this type of memory has been
found to be uniquely sensitive to REM sleep deprivation [2,68].
Finally, declarative learning has sometimes been associated with
SWS [25,56,57]. However, during wakefulness, the formation
of declarative memory has also been related to theta activity,
which is thought to play a role in hippocampal communication
with other structures [34], as well as in the induction of longterm potentiation (LTP) [37]. Hippocampal theta predominates
during REM sleep [7]; thus, REM sleep may be an ideal time for
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S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
declarative memory consolidation to occur. It was hypothesized
that there would be an increase in theta power during REM sleep
following Paired Associates learning.
To investigate the questions above, an experimental design
was implemented in which good sleepers were randomly
assigned to one of four learning conditions: (1) Pursuit Rotor
(PR); (2) Mirror Tracing (MT); (3) Paired Associates (PA); and
(4) non-learning controls (C). Using this approach, the learningdependent changes in sleep macrostructure (sleep stages) and
the temporal and spatial changes to sleep microstructure (phasic
and tonic EEG activity) could be characterized. These learningdependent changes to sleep were expected to implicate particular
stages of sleep, and phasic activity such as sleep spindles,
K-complexes, and rapid eye movements as either markers of
increased brain plasticity or mechanisms for memory consolidation during sleep. Furthermore, the topographic distribution
of learning-dependent changes in the sigma and theta bands was
investigated to determine whether the consolidation of different types of learning during sleep might be localized to specific
brain regions.
2. Method
2.1. Participants
An initial telephone interview was used to exclude participants for lefthandedness, atypical sleep patterns (sleep time outside the approximate hours of
11:00 PM to 7:00 AM), shift work, head injury, cigarette smoking, and chronic
pain. In addition, participants were excluded for activities that involved the
development of simple motor skills (for example; dance lessons or other sports
activities), and complex motor skills (for example; piano lessons, video games)
and strategy games such as chess. If participants were engaged in this type
of activity more than once per week for a period of several hours, they were
excluded from participating in the experiment. If they engaged in this type of
activity less than once per week for a period of several hours, they were asked to
restrict themselves from this activity for the duration of the study. It was expected
that all participants would be engaged in regular amounts of declarative learning
since they were recruited from a university population. However, participants
were screened for above normal levels of declarative learning. If participants
were preparing for exams or writing papers in the week immediately prior to
or during overnight testing and recording sessions, they were not scheduled to
participate at that time.
Seventeen participants were excluded based on the telephone interview criteria, and three dropped out after the first screening night. Two participants
were excluded following the clinical screening night due to the appearance of
periodic limb movements associated with regular arousals throughout the night.
One participant was excluded from all analyses because of poor sleep quality
due to alpha intrusion on the EEG throughout both baseline and test nights, and
repeated awakenings throughout the night. This data was replaced by pilot data
that was complete with the exception that data was missing on the re-test for the
Pursuit Rotor Task due to a computer malfunction, and because the participant
elected not to have IQ measured. The final sample size included 36 participants
(6 males), aged 18–26 (M = 20.28, S.D. = 5.26) who spent three consecutive
nights in the Sleep Research Laboratory at Brock University. While there were
a greater proportion of females than males overall, the number of males and
females were evenly distributed so that each group included 1–2 males.
2.2. Screening questionnaires and sleep log
A sleep-wake questionnaire was used to screen candidates for sleep quality,
intake of illicit drugs, alcohol, chronic pain, shift work, family sleep history,
and health. All participants were medication-free. In addition, the Horne and
Ostberg [29] Circadian Rhythm Questionnaire was used to screen participants
for extreme morningness or eveningness; and the Fatigue Questionnaire [84]
was used to screen candidates for excessive daytime fatigue. If participants met
the inclusion criteria, they were given a sleep and activity diary that logged
information on sleep and wake times, caffeine and alcohol consumption, type
and duration of physical exercise, studying, and other leisure activities. The
activities measured with this instrument were reported daily for 10 days until
the completion of the last overnight spent in the sleep laboratory.
2.3. Learning tasks
2.3.1. Mirror Tracing Task
The Mirror Tracing Task was adapted from Plihal and Born [56]. The task
involved tracing around 14 figures with a pen as quickly and accurately as
possible (including two star-shaped practice figures, six human-like figures with
sharp corners, six human-like figures with curved corners). Two concentric lines
5 mm apart outlined the figures. The goal of the task was to trace around the
figure, keeping between the lines without touching them. The participant must
do this by watching their hand in a mirror. A shade blocked the participant from
directly tracking their hand movements. The dependent measure for this task
was the number of times the participant touched the outline of the figure. The
total trial duration was not measured, thus analysis of a speed-accuracy trade off
was not possible. However, the primary goal of measuring performance was to
record an index of the efficacy of the experimental manipulation. Participants
were instructed to trace around the figure as quickly as possible, without making
additional errors due to the speed at which they completed the task. Both speed
and accuracy increase with practice on the Mirror Tracing Task [33], while both
measures provide valuable information about the nature of improvement on the
task, they would also be expected to be highly correlated with one another, thus
only one index of performance was used here.
2.3.2. Pursuit Rotor Task
The Pursuit Rotor Task required the participant to follow a rotating target in a
square track using a (LogitechTM ) hand-held computer cordless optical mouse. A
computerized version of the Pursuit Rotor Task was used as its reliability with the
Pursuit Rotor apparatus had been established [20]. Participants completed forty
sets of 30-s trials (for 15 rotations per trial, or 600 rotations in total) with 60-s rest
intervals between sets of trials to minimize fatigue. The target revolved around
the track at 30 revolutions per minute. Time on target was counted when contact
was made between the rotating target and the crosshairs of the computer mouse.
The number of occurrences off-target could not be measured due to limitations of
the rotor software program. A tone sounded (440 Hz, 60 dB) when the crosshairs
were on target which served as positive feedback for task performance. The
track, target and crosshairs were displayed on a computer screen. The target
was a 1 cm diameter red circle which revolved around a 20 cm diameter square
track.
2.3.3. Paired-Associates Task
A modified version of the Paired-Associates Task [26] was used in this
experiment, such that words pairs were presented on the screen individually as
opposed to in a block of several pairs displayed on the screen at once. Words
were selected for high concreteness, low emotionality, and word length (3–11
letters). Words that fit these criteria were randomly selected from a revised
version of the General Service List [4] which contains 2284 words commonly
used in the English language. Words were randomly paired, and subsequently
screened to ensure that semantically related words were not paired together. Participants learned 168 word pairs presented in semantically unrelated individual
pairs during acquisition. This procedure was repeated twice, once with word
pairs displayed for 13.25 s to allow enough time for initial encoding and again
for 8.75 s to allow rehearsal. Word pairs were presented in the same order for
both of these learning trials. These intervals were chosen so that each word pair
was presented for the same duration per pair as used in the study by Gais et
al. [26]. Participants were instructed to visually relate the words to one another
with mental imagery to try to memorize the pairs. Participants were urged to
use creative and unusual imagery during memorization. This mnemonic strategy was instructed to the participants so that a similar strategy would be used
between individuals, and also because previous studies (for review see [66])
have not demonstrated a clear relationship between sleep and declarative learn-
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
ing using typical instructions which simply instruct the participant to “memorize
the word pairs”. Without specific instruction, the use of multiple strategies may
mask the relationship. Recall was tested immediately after learning of the two
blocks of acquisition sessions. During recall testing, the first word in the pair
of study words was presented alone in a randomized order. The participant was
instructed to respond with the other member of the pair from memory by typing their response onto the computer keyboard. Participants were instructed to
check each response (displayed on screen) for typographical errors before proceeding to the next test item. Words with multiple spellings were excluded from
the list of words to avoid spelling errors. Responses were saved to a text file
so that spelling errors could be checked, however, no typographical errors were
detected. Re-testing was the same as the testing procedure (including both the
training session and the testing session), and was repeated one week after initial
testing. The number of correctly recalled words was used as a measure of task
performance.
2.3.4. Control Tasks
The control group spent the equivalent amount of time as the learning groups
filling out sleep-related questionnaires and forms to collect demographic information. They also spent their time using a computer for various tasks such as
checking email, or using a person-to-person chat program. Thus, the control
group was engaged in equivalent types of motor activity as the Mirror Tracing
Task (writing), the Pursuit Rotor Task (using a computer mouse) and in reading,
recalling and typing words as in the Paired Associates Task, without a learning
component.
2.4. Polysomnographic recording
Physiological signals were recorded using a 64-channel Mizar SD32+ digital amplifier and Sandman and Spyder software (Tyco Inc.), to measure brain
wave activity (Electroencephalogram, EEG), horizontal eye movement recorded
from the left and right lower canthi (electro-oculogram, EOG), and submental
muscle activity (Electromyogram, EMG). EEG was recorded at 256 Hz with
hardware filters set to cut off frequencies below 0.099 and above 115.2 Hz (high
frequency cut off = 0.45 × sampling rate). Bipolar EEG was recorded from A1,
A2, Fp1, Fp2, F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, Oz, and O2 according
to the International 10–20 system for electrode placement [55] and was referenced to Fpz with a ground placed at AFz. EEG channels were re-referenced
offline to an average of A1 and A2, and a software filter was applied to cut off
frequencies below 0.5 Hz and above 35 Hz. A 35 Hz (high frequency cut off)
software filter was applied to the EOG channels, and a 0.1 Hz (low frequency
cut off) filter was applied to the EMG. A Notch (60 Hz) filter was applied to all
channels to eliminate electrical noise. On the screening night, respiratory effort
was measured from the chest and abdomen using respiratory effort belts, and leg
movements were measured using electrodes placed on the left and right anterior
tibialis muscle. Impedances were measured at the start and end of recording,
and were <5 Ohm for all EEG sites, <10 Ohm for EOG, and EMG.
2.5. Procedure
All participants spent three consecutive uninterrupted nights in the Sleep
Research Laboratory, including an acclimatization/screening, baseline, and test
night. Time in bed was fixed at 11:00 PM to 7:00 AM. The acclimatization night
served to control for the “first night effect” [82], and as a screening night to
exclude participants from further involvement in the study due to any symptoms
related to sleep disorders including sleep apnea and periodic leg movements
(reduced respiratory effort equivalent to a respiratory distress index above 5
events per hour, or periodic leg movements above 5 per hour). The baseline night
was used to collect baseline EEG for within subject comparisons to the test night.
On the test night, at 9:00 PM, participants were randomly assigned to perform
one of the following memory tasks: the Mirror Tracing Task (MT), Pursuit Rotor
Task (PR), Paired-Associates Task (PA), or no learning task (C). One week
thereafter, they repeated the practice session at 9:00 PM. The participants and
the experimenter were blind to the experimental condition before the assignment
of participants to the experimental condition on the test night.
Recordings from the baseline and test night were sleep stage scored in 30-s
epochs according to standard criteria [58] by one independent judge who was
51
blind to experimental condition and to the night the recording was made. Scoring reliabilities had been established previously between the judge and other
individuals above 90% agreement. Sleep spindles, K-complexes, and rapid eye
movements were all scored with the experimenter blind to the experimental
condition and to the night the recording was made. Sleep spindles were visually
counted from Cz with the aid of a filtered channel to display 12–16 Hz activity
only. Each sleep spindle was also measured in duration (seconds). Sleep spindles were counted and measured in Stages 2, and SWS separately across the
entire night. All spindles included in the count exceeded 0.5 s, and had typical
fusiform spindle morphology (waxing and waning amplitude). Typically, the
maximum amplitude exceeded 10 ␮V; however, there was no minimum amplitude criteria set in accordance with standard sleep scoring procedures [58].
K-complexes were visually counted across Fz, Cz and Pz with the aid of an
additional filtered channel at 12–16 Hz in order to identify K-complexes that
occurred overlapping in time with spindles. K-complexes were counted if they
had a typical morphology including a large negative peak followed by a positive
deflection, exceeded 75 ␮V, and were maximal in amplitude at frontal sites. Kcomplexes were binned into three categories: K-complexes that occurred in the
presence of spindles (spindle activity that occurred prior to, overlapping with,
or following the K-complex), K-complexes that occurred in the absence of spindles, and the total number of K-complexes (a combination of the two previous
categories).
Rapid eye movements were visually scored from left and right eye channels
identified according to standard criteria and examples [58]. The only criterion in
addition to this was a minimum amplitude criterion of 25 ␮V [71]. EOG channels
were filtered off-line at 0.5 Hz to eliminate any slow-rolling eye movements, and
to display a flat baseline. Thus, any deflections above the amplitude criterion
would be counted as a rapid eye movement. This filtering technique aided in the
identification of rapid eye movements, and eliminated only very slow rolling
eye movements. All phasic activity including sleep spindles, K-complexes, and
rapid eye movements were scored in the absence of movement artifact. Since
an increase in the duration of Stage 2 sleep following procedural learning was
observed previously [21], the number of spindles and K-complexes per minute
in Stages 2, 3 or 4 (i.e., density) were calculated to control for changes in sleep
architecture. Similarly, the number of rapid eye movements per minute during
REM sleep (REM density) was used.
Power spectral analysis of the EEG was done using Fast Fourier Transformation (FFT) techniques in each sleep stage separately including Stages 2, SWS
(Stages 3 and 4) and REM sleep. FFT analysis was conducted on all recorded
artifact-free epochs of the sleep recordings across the entire night of sleep. The
EEG was analyzed in 2 s Hanning windows with a 75% overlap, and was rereferenced to an average of A1 and A2. Low frequency EEG was filtered at
0.5 Hz using a software filter, and high frequency EEG cut-offs remained at the
hardware setting of 115.2 Hz described above. Stage 2 sleep was analyzed in
two separate halves to determine if time of night was an important factor for
learning-dependent changes in sleep. The duration of the night from sleep onset
to lights on was divided to mark the midpoint of the night for each participant on
each night separately. Sleep onset was considered to begin after 5 min of uninterrupted Stage 2 sleep. To follow-up any time of night differences, for Stage
2 sleep, the night was also divided by NREM period in a separate analysis. All
epochs scored as Stage 2 sleep were submitted for FFT analysis in each NREM
period. Each NREM period was separated by at least 5 min of uninterrupted
REM sleep. Only the first four NREM periods were analyzed due to the fact that
not all participants had five or more NREM periods. For all FFT analyses spectral
power was binned into eight frequency bins including: delta (0.5–4 Hz), theta
(4–8 Hz), low frequency alpha (8–10 Hz), high frequency alpha (10–12 Hz), low
frequency sigma (12–14 Hz), high frequency sigma (14–16 Hz), beta (16–35 Hz)
and gamma (35–60 Hz). All FFT data was log transformed prior to statistical
analysis to normalize the distribution of scores. The sigma band was split into low
and high frequency bins to further explore the topography of the two proposed
sigma generators [16,32,46].
2.6. Data analyses
2.6.1. Sleep architecture
To determine if new learning had an effect on sleep architecture, 2 × 4
(night × learning group) mixed-design ANOVAs were used to analyze differences in minutes spent in each of Stages 1, 2, SWS, REM and total sleep time
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S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
separately. If a significant group by night interaction was detected, a follow-up
simple effects one-way ANOVA on baseline night data only was used to determine if the four groups differed significantly at baseline. If it was found that
groups differed at baseline, a one-way ANCOVA was used to partial out baseline
differences to determine which groups differed on the test night. If a significant
main effect for learning group was found using the one-way ANCOVA, independent t-tests with a Bonferroni correction were used. If groups did not differ at
baseline, paired Bonferroni t-tests using the pooled error term from the overall
interaction were used to test changes from baseline to test night in each group
[30]. Due to the highly variable duration of Stages 3 and 4 sleep across individuals, Stages 3 and 4 sleep were collapsed into a single category, slow wave sleep
(SWS). This was done for sleep scoring, spindle counts, K-complexes, rapid eye
movements and FFT analyses.
across the scalp. The previous analyses were used to establish where the effects
were maximal along the midline of the scalp. For the following analyses, a less
conservative approach was taken in order to gain a more descriptive picture of
the topography of these effects at Fp1, Fp2, F3, Fz, F4, C3, Cz, C4, P3, Pz,
P4, O1, Oz, and O2. Each significant finding reported was followed up using
paired t-tests. To this end, in the groups that had a change in spectral power from
baseline to test night, both statistically significant results (p < .05) and trends
(p < .10) were reported. Brain maps were computed for topographic display of
the frequencies of interest using SpyderTM software (Tyco Inc.).
3. Results
3.1. Learning data
2.6.2. Sleep spindles, K-complexes and rapid eye movements
The same analytic strategy was used for sleep spindles, K-complexes and
rapid eye movements as described above for the sleep architecture data. Using
this strategy, the number of sleep spindles per minute (spindle density), average
spindle duration, and the number of K-complexes per minute were analyzed for
Stage 2 and SWS separately. The assumption of normality for the PA and the
C groups was questionable for REM density on the test night. The number of
rapid eye movements per minute in REM sleep (REM density) was analyzed
using the Sign test, which tests whether the probability of observing the number
of differences in a score is beyond chance.
2.6.3. Spectral analysis of sleep EEG using FFT techniques
In addition to the analytic strategy outlined above for the analysis of the
sleep architecture data, a “top-down” approach was used to analyze the FFT data
that involved several steps. This was done to analyze the data in a systematic
hypothesis-driven fashion that controlled inflation of experimentwise Type-I
error rates and is similar in concept to the protected tests strategy recommended
by Howell [30]. First, each frequency band (delta, theta, low frequency alpha,
high frequency alpha, low frequency sigma, high frequency sigma, beta and
gamma) in each stage of sleep (2, SWS, REM) was analyzed separately at midline
sites (Fz, Cz, Pz, Oz) to determine if there were any changes from baseline to test
night as a function of learning condition, and at which site along the midline the
effect was significant. Follow-up analyses were conducted only at the midline
site where the effect was found to be significant. To further investigate time
of night differences in Stage 2 sleep sigma power, total sigma power for the
first and second halves of the night were analyzed separately at the site where
the effect was significant along the midline. If changes in the total sigma band
varied as a function of learning condition in a particular part of the night, then
low and high frequency sigma were analyzed separately to determine if low or
high frequency sigma power was of particular importance to sleep-dependent
memory consolidation at the site where the effect was significant. Low and
high frequency sigma bands were analyzed separately in order to differentiate
between the anterior and posterior generators. The same analytic strategy was
used to investigate learning-related changes in spectral power during SWS and
REM sleep with the exception that they were not categorized into the first and
second halves of the night due to the fact that SWS dominates the early portion
of the sleep period while REM dominates the latter.
There was no EEG data from the Pz site for two participants on the baseline
night due to an irresolvable signal problem with that channel. For these data,
the power from the four nearest neighbors (Cz, P3, P4, Oz) was averaged and
included in the data set. Two participants (one in the MT group and one in the
PA group) were excluded from all FFT data analyses due to poor impedances
(>100 K Ohm) on the recording reference electrodes (Fpz). In addition, two more
participants were excluded based on unusually high power across all bands which
occurred only after re-referencing the active sites to an average of A1 and A2.
After the removal of these participants (for EEG spectral analysis only), the
remaining samples were eight in the PR group, seven in the MT group, nine in
the PA group, and eight in the C group.
2.6.4. Topography of the learning-dependent changes in spectral power
Statistically significant findings from the spectral analysis of sleep using FFT
described in the previous section were investigated further to determine how
the learning-dependent changes in spectral power during sleep were distributed
Paired t-tests were used to assess pre-post changes in performance for each memory group. It was found that all groups
improved on memory task performance from test to re-test
(Table 1). Following the retention interval, the MT group made
fewer errors on the Mirror Tracing Task (t(8) = 10.00 p < .001);
the PA group recalled more word pairs (t(8) = 4.19, p = .003);
and the PR group had a higher percent time-on-target on the
Pursuit Rotor task (t(7) = 4.62, p = .002).
3.2. Sleep architecture data
To determine if sleep architecture was affected by learning,
the duration of time spent in each stage of sleep (Stages 1, 2,
SWS, and REM) prior to and following learning was compared
in the four learning conditions. As predicted, there was a significant night by group interaction for the duration of Stage 2 sleep
(F(3, 32) = 4.34, p = .01). A one-way ANOVA revealed that the
groups had a significantly different duration of Stage 2 sleep on
the baseline night (F(3, 32) = 3.09, p = .04). However, followup tests did not reveal any significant group differences using
Bonferroni paired comparisons. A one-way ANCOVA revealed
a significant effect of learning on the test night after baseline differences have been removed (F(3, 31) = 5.93, p = .003).
Using independent Bonferroni t-tests, it was found that the PR
group had more Stage 2 sleep on the test night than the control group (t(8) = 3.83, p = .004). The MT and the PA group
did not differ from controls in the duration of Stage 2 sleep on
test night. No significant night by group interaction was found
for the duration of Stage 1, SWS, REM sleep or total sleep
time. Group means for baseline and test nights are presented in
Table 2.
Table 1
Means (M) and standard deviations (S.D.) for test and re-test on the Pursuit
Rotor Task (PR), Mirror Tracing Task (MT), and Paired-Associates Task (PA)
Test
Rotora
Pursuit
Mirror Tracingb
Paired-Associatesc
a
b
c
Re-test
M
S.D.
M
S.D.
13.01
116.56
116.44
3.83
38.71
35.40
15.27
45.00
149.67
2.50
24.37
13.42
Time on target.
Number of errors.
Number of correctly recalled word pairs.
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
Table 2
Means (M) and Standard Deviations (S.D.) for minutes of sleep Stages 1, 2,
SWS, REM and total sleep time (TST) for the Pursuit Rotor learning (PR)
group, Mirror Trace learning (MT), Paired Associates learning (PA) and (C)
control group on the baseline night and test night
Baseline night
Test night
M
S.D.
M
S.D.
PR
Stage 1
Stage 2
SWS
REM
TST
6.3
250.8
74.2
111.5
436.6
5.2
18.6
18.1
27.2
13.3
5.8
268.5*
62.4
111.5
442.4
3.7
25.3
15.0
16.0
9.3
MT
Stage 1
Stage 2
SWS
REM
TST
13.0
251.7
76.2
95.7
423.5
14.5
25.7
17.9
21.7
35.0
12.6
238.1
74.7
100.4
413.2
17.3
33.2
22.1
24.7
35.4
PA
Stage 1
Stage 2
SWS
REM
TST
12.2
220.9
89.6
106.3
416.8
18.3
33.2
29.0
31.1
22.6
10.1
203.0
87.8
118.0
408.8
5.7
35.1
27.9
21.8
14.7
Control
Stage 1
Stage 2
SWS
REM
TST
9.4
233.0
78.6
102.7
414.3
7.3
21.8
23.7
22.9
15.9
20.1
209.6
79.3
95.1
384.0
12.4
24.6
16.9
18.5
34.1
53
an average increase of 0.89 spindles per minute from baseline (M = 6.36, S.D. = 2.08) to test night (M = 7.25, S.D. = 2.09)
(t(32) = 3.14, p < .05). Spindle density did not change from baseline to test night for the PA, MT or C groups.
3.3.2. Stage 2 spindle duration
A similar pattern of results was found for Stage 2 sleep spindle
duration. A 2 × 4 (night × learning group) ANOVA revealed a
significant night by group interaction (F(3, 32) = 5.65, p = .003)
for Stage 2 sleep spindle duration (Fig. 1B). The groups did
not differ at baseline (F(3, 32) = 1.35, p = .28). Bonferroni t-tests
revealed that spindle duration increased from baseline (M = 1.42,
S.D. = 0.18) to test night (M = 1.68, S.D. = 0.12) following PR
learning (t(32) = 4.21, p < .01). Spindle duration in the MT, PA
and C groups did not change from baseline to test night.
3.3.3. SWS spindle density
There was no significant interaction between night and learning condition for SWS spindle density. However, this analysis
revealed a similar pattern of results as spindle density in Stage
2 sleep, and the interaction did approach significance (F(3,
32) = 2.52, p = .075). Thus, follow-up analyses were conducted
on the hypothesized differences. The groups did not differ significantly at baseline (F(3, 32) = 0.528, p = .67). Bonferroni t-tests
revealed that as with the Stage 2 sleep data, there was a significant increase from baseline (M = 7.68, S.D. = 3.00) to test night
(M = 8.75, S.D. = 2.40) in sleep spindle density following PR
learning during SWS (t(32) = 3.01, p < .05) but not in the MT,
PA or C groups (Table 3 and Fig. 1C).
Note: Significant difference from control group is indicated by *p = .004.
3.3. Sleep spindles
3.3.1. Stage 2 spindle density
As predicted, a 2 × 4 (night × learning group) ANOVA
revealed a significant interaction between night and learning
group (F(3, 32) = 3.02, p = .04) for sleep spindle density during Stage 2 sleep (Fig. 1A). A one-way ANOVA revealed that
the groups did not differ on the baseline night (F(3, 32) = 1.49,
p = .24). Bonferroni t-tests revealed that the PR group had
3.3.4. SWS spindle duration
A similar pattern of results was found for spindle duration in
SWS. A 2 × 4 (night × learning group) ANOVA revealed a significant interaction between night and learning group for spindle
duration (F(3, 32) = 3.86, p = .018) and the four groups did not
differ at baseline (F(3, 32) = 0.42, p = .74). Post-hoc t-tests did
not detect any significant change in spindle duration in the PR,
MT, PA or C groups, however, it is worth noting that spindle
duration did change in the hypothesized direction from baseline
(M = 1.02, S.D. = 0.20) to the test night (M = 1.11, S.D. = 0.27)
in the PR group.
Fig. 1. Changes in sleep spindles during Stage 2 and SWS following Pursuit Rotor learning. (A) The number of sleep spindles per minute (spindle density) in Stage
2 sleep from baseline (night 1) to test night (night 2), p < .05. (B) The average duration of the sleep spindle in Stage 2 sleep from baseline (night 1) to test night (night
2), p < .01. (C) The number of sleep spindle per minute (spindle density) in SWS from baseline (night 1) to test night (night 2), p < .05. () Pursuit Rotor (PR); ()
Paired Associates (PA); () Mirror Tracing (MT); () control (C).
54
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
Table 3
The number of sleep spindles per minute (spindle density) in SWS, number of
total K-complexes, K-complexes in the absence of spindles, and K-complexes
in the presence of spindles, are displayed separately per minute in Stage 2 sleep.
Means (M) and Standard Deviations (S.D.) are displayed for the Pursuit Rotor
(PR), Mirror Tracing (MT), Paired Associates (PA), and control (C) groups on
the baseline and test nights
Baseline night
Test night
M
S.D.
M
S.D.
SWS spindle density
PR
7.68
MT
6.79
PA
7.26
C
6.38
3.00
1.84
2.23
2.14
8.75*
6.50
7.45
6.58
2.40
1.22
2.54
2.15
Total # of K-complexes
PR
2.51
MT
2.40
PA
2.58
C
2.73
1.01
0.73
0.80
0.93
2.66
2.28
2.77
2.74
1.37
0.90
0.83
0.70
K-complexes in the absence of spindles
PR
0.86
0.49
MT
0.89
0.41
PA
1.03
0.31
C
1.08
0.47
0.90
0.84
1.15
0.97
0.59
0.34
0.39
0.45
K-complexes in the presence of spindles
PR
1.65
0.64
MT
1.50
0.47
PA
1.55
0.59
C
1.65
0.60
1.77
1.44
1.61
1.76
1.00
0.60
0.66
0.63
Note: Significant difference from control group is indicated by *p < .05.
3.4. K-Complexes
Night by Group (2 × 4) ANOVAs revealed that there was
no change from baseline to test night as a function of learning
condition in the total number of K-complexes (F(3, 32) = 0.35,
p = .79), K-complexes that co-occurred with sleep spindles (F(3,
32) = 0.17, p = .92), or K-complexes that did not co-occur with
sleep spindles (F(3, 32) = 0.81, p = .50) per minute of Stage 2
sleep. The results from K-complex analyses are displayed in
Table 3.
3.5. Rapid eye movements
It was observed in these data, following learning on the Mirror Tracing Task, that 8 of the 9 participants had an increase in
REM density from baseline to test night as predicted, whereas
in the C, PA and PR groups there was no consistent change in
REM density. The Sign test is used to test the null hypothesis that the number of instances from one set of observations
to another remains unchanged. It is particularly useful when
the assumption of normality is questionable. A Sign test was
therefore used to determine if the number of positive increases
in REM density following Mirror Trace learning was beyond
chance. This analysis revealed that the number of subjects who
had an increase in REM density was significantly beyond chance
following learning on the Mirror Tracing Task from baseline to
test night (p = .04). The number of subjects who had a change in
Fig. 2. Percentage of participants who had an increase in REM density from the
baseline to the test night in each experimental condition. A significant number
of participants had increases in REM density following Mirror Trace learning,
indicated by *p < .05.
REM density in the control (p = 1.0), PA (p = 1.0) or PR (p = .51)
groups was not beyond chance (Fig. 2).
3.6. Spectral analysis of sleep using FFT
3.6.1. Stage 2 sigma power
At all midline sites, 2 × 4 (night × group) ANOVAs were
used to test if total sigma power (12–16 Hz) for the entire night
changed from baseline to test night as a function of learning
condition during Stage 2 sleep. It was found that night and
learning condition interacted significantly along the midline at
Oz only (F(3, 28) = 4.03, p = .017) and did not differ at baseline (F(3, 28) = 1.07, p = .19). There were no significant night
by learning group interactions for any frequencies outside of the
sigma band at Oz. Follow-up analyses revealed that there was
a significant night by learning condition interaction in low frequency sigma (12–14 Hz) power at Oz in the second half of the
night (F(3, 28) = 3.48, p = .03) and did not differ at baseline (F(3,
28) = 2.82, p = .06). Bonferroni t-tests revealed that there was a
significant decrease in sigma following Paired Associates learning (t(28) = −3.06, p < .05). Contrary to the predictions, there
was no increase from baseline to test night in the PR group during Stage 2 sleep, however, it is worth noting that sigma power
did change in the hypothesized direction, although this effect
was not statistically robust (t(28) = 1.54, p < .10). A summary of
these results is displayed in Fig. 3A. The results from the followup analysis of Stage 2 sleep broken down into NREM periods
did not yield any additional findings.
3.6.2. SWS sigma power
Total sigma power (12–16 Hz) during SWS was analyzed
using a 2 × 4 (night × learning group) ANOVA to determine the
effect of learning during SWS at all midline sites. It was found
that total sigma during SWS changed as a function of learning
condition from baseline to test night at Oz only (F(3, 28) = 6.28,
p = .002) and was not significantly different at baseline (F(3,
28) = 0.94, p = .44). There were no significant night by learning
group interactions for any frequencies outside of the sigma band
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
55
revealed that the four groups did not differ on the baseline night
in low frequency sigma power (F(3, 28) = 1.33, p = .28). Pairwise
Bonferroni t-tests revealed that low frequency sigma power during SWS increased in the PR group (t(28) = 3.37, p < .01), and
decreased in the control group (t(28) = −2.27, p < .05). Low frequency sigma power during SWS did not change from baseline
to the test night in the MT group, although the decrease in sigma
power did approach significance in the PA group (t(28) = −2.66,
p < .10). A summary of these results is displayed in Fig. 3B.
There was no significant change in the high frequency sigma
band during SWS.
3.6.3. REM sleep theta power
A 2 × 4 (night × learning group) ANOVA was used at each
midline site to determine if theta changed during REM sleep as
a function of learning condition from baseline to test night. It
was found that there was a significant night by learning group
interaction at Cz (F(3, 28) = 3.27, p = .036), but not at any other
midline sites and that the groups did not differ on the baseline
night (F(3, 28) = .98, p = .42). As predicted, paired Bonferroni ttests revealed that there was a significant increase in theta power
during REM sleep from the baseline to test night for the PA group
(t(28) = 4.52, p < .01), but no change in theta power for the PR,
MT, or C groups (Fig. 3C).
Fig. 3. Learning-dependent changes in spectral power (log 10 ␮v2 ) displayed as
mean differences so that positive scores reflect increases in the power spectra,
whereas negative scores reflect decreases from baseline to test night. (A: late
Stage 2 sleep) Following Paired Associates learning, a decrease in low frequency
sigma power (12–16 Hz) from the baseline to the test night was observed at Oz
during Stage 2 sleep in the second half of the night, (*p < .05) and a statistical
trend for increased low sigma power following Pursuit Rotor learning, (+p < .10).
(B: Slow wave sleep) During SWS at Oz, a similar decrease in low sigma power
was observed following Paired Associates learning where a statistical trend was
observed, (+p < .10), and a significant increase following Pursuit Rotor learning, (**p < .01). There was also a smaller, yet statistically significant decrease
in the control group, (*p < .05). (C: REM sleep) Following Paired Associates
learning, an increase in theta power during REM sleep was observed at Cz,
(**p < .01), whereas an increase in low frequency sigma power was observed
at Cz, (**p < .01) during REM. Group legend: Control (C), Paired Associates
(PA), Pursuit Rotor (PR), Mirror Tracing (MT).
at Oz. Using a 2 × 4 (night × learning group) ANOVA to analyze low frequency sigma power (12–14 Hz) during SWS it was
found that there was a significant night by learning condition
interaction (F(3, 28) = 8.16, p = .00046). A one-way ANOVA
3.6.4. REM sleep sigma power
Additional 2 × 4 (night × learning group) ANOVAs were
performed at Cz for the delta, alpha, sigma, beta and gamma
bands to determine if the changes in power during REM sleep
were isolated to the theta band. Interestingly, there was a significant night by learning condition interaction in the low frequency
sigma band (12–16 Hz) at Cz (F(3, 28) = 4.19, p = .014), but
not in any other frequency bands. A follow-up simple effects
ANOVA for sigma at Cz during REM sleep revealed that
the groups were not statistically different at baseline (F(3,
28) = 0.92, p = .45), and paired Bonferroni t-tests revealed that
the PA group had an increase in the low frequency sigma band
during REM sleep at Cz from baseline to test night (t(28) = 4.17,
p < .01). There was no change from baseline to test night in the
PR, MT and C groups for sigma power during REM sleep at Cz.
There was no significant change in high frequency sigma during
REM sleep. A summary of these results is displayed in Fig. 3C.
3.7. Topography of the learning-dependent changes in
spectral power
While the decrease in the sigma band during Stage 2 sleep
for the PA group was not as hypothesized, in light of the finding that both theta and sigma power were affected by Paired
Associates learning during REM sleep, the topography of this
effect was investigated further to provide a more complete picture of the effect of Paired Associates learning on sleep. To
summarize from the previous section, it was found that the PA
group had a decrease in low frequency sigma power at Oz during the second half of the night during Stage 2 sleep, but not
in high frequency sigma power, or sigma power during the first
half of the night. There was no change in the sigma band dur-
56
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
ing Stage 2 sleep for the PR, MT, or C groups. Paired t-tests
at all active sites for the PA group (Fp1, Fp2, F3, Fz, F4, C3,
Cz, C4, P3, Pz, P4, O1, Oz, and O2) revealed that there was
a significant decrease in low frequency sigma power or trend
towards significance in Stage 2 sleep for the last half of the
night at Cz (t(8) = 2.09, p = .07), Pz (t(8) = 1.94, p = .089), and
Oz (t(8) = 6.59, p = .0002) where the effect was most robust.
It is also worth noting that low frequency sigma power was
maximal at Pz on baseline (M = 0.787, S.D. = 0.206) and test
night (M = 0.741, S.D. = 0.219), however, the largest decrease in
low frequency sigma power was statistically most robust at Oz
(Fig. 4A). Given the change in sleep spindles during stage 2 sleep
and the hypothesized, but non-significant increase in late Stage
2 low frequency sigma power, we investigated the topographic
nature of sigma power during Stage 2 sleep following Pursuit
Rotor learning. It was found that there was a significant increase
from baseline to test night in low frequency sigma power in the
PR group during late Stage 2 sleep at F4 (t(7) = −2.53, p = .039)
(Fig. 4A). The distribution of low frequency sigma power was
maximal at Cz on the baseline (M = 0.655, S.D. = 0.289) and test
night (M = 0.657, S.D. = 0.274).
To summarize from the previous section, it was found that
during SWS, low frequency sigma power increased at Oz in
the PR group, but not in any other groups. For the following
tests, the data was excluded from one participant, due to poor
quality recordings from O2 (impedances > 100 K Ohm). Multiple paired t-tests at all active sites revealed that there was a
significant increase from baseline to test night in low frequency
sigma power or trend towards significance in the PR group during SWS sleep at Fp1 (t(7) = −2.82, p = .026), Fp2 (t(7) = −2.49,
p = .042), F4 (t(7) = −2.58, p = .036), Cz (t(7) = −3.41, p = .011),
Pz (t(7) = −1.96, p = .091), and Oz (t(7) = −3.11, p = .017). The
distribution of low frequency sigma power was maximal at Cz
on baseline (M = 0.566, S.D. = 0.289) and test night (M = 0.588,
S.D. = 0.299); and the increase in low frequency sigma power
was most statistically robust at Cz. Interestingly, it is also worth
noting that there was a robust increase in low frequency sigma
power at Oz (Fig. 4B).
From the analyses reported in the previous section it was
found that there was an increase in theta at Cz during REM sleep
for the PA group only. Multiple paired t-tests revealed that there
was a significant increase or trend towards significance in theta
Fig. 4. Learning-dependent changes from baseline (top maps) to test night (bottom maps) in sleep EEG. (A) Decrease in low frequency sigma (12–14 Hz) power
during late stage 2 sleep in the second half of the night following Paired Associates learning (PA; left) as indicated by a decrease in warmer colours at occipital,
central, and parietal regions. Sigma power was maximal at parietal regions on both nights, however, the decrease was maximal at occipital regions. Increase in
low frequency sigma (12–14 Hz) power during late stage 2 sleep (2nd half of the night) following Pursuit Rotor learning (PR; right) as indicated by an increase in
warmer colours at frontal regions. Sigma power was maximal at the vertex, however, the largest increase was maximal over right frontal regions. (B) Increase in
low frequency sigma (12–14 Hz) power during SWS following Pursuit Rotor (PR) learning as indicated by an increase in warm colours widely distributed across
frontal, central, parietal and occipital regions. Sigma power was maximal at the vertex, however, the largest increase was maximal at central and occipital regions.
(C) Increase in theta (4–8 Hz) power during REM sleep following Paired Associates (PA) learning as indicated by an increase in warmer colours distributed across
frontal, central, parietal and occipital regions. Theta power was maximal at the vertex, and the largest increase was maximal at central regions. (D) Increase in low
frequency sigma (12–14 Hz) power during REM sleep following Paired Associates (PA) learning as indicated by an increase in warmer colours at frontal, central and
occipital regions. Sigma power was maximal at occipital regions, however, the largest increase was maximal at central regions. Electrode scalp locations indicated
by open circles, and orientation of maps indicated by A: anterior, and R: right side.
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
from baseline to test night during REM sleep at Fz (t(8) = −2.29,
p = .051), F4 (t(8) = −4.97, p = .001), C3 (t(8) = −2.33, p = .048),
Cz (t(8) = −5.32, p = .001), C4 (t(8) = −4.28, p = .003), Pz
(t(8) = −3.56, p = .007), and O1 (t(8) = −4.22, p = .003). Theta
was maximal at Cz on baseline (M = 1.010, S.D. = 0.131) and
test night (M = 1.064, S.D. = 0.116); the increase from baseline to test night in the PA group was most statistically robust
at Cz (t(8) = −5.315, p = .001) (Fig. 4C). Changes in spectral power during REM sleep following Paired Associates
learning were not limited to the theta band. Multiple paired
t-tests revealed that there was a significant increase or trend
towards significance in sigma from baseline to test night during REM sleep at F3 (t(8) = −2.21, p = .058), Fz (t(8) = −2.26,
p = .033), F4 (t(8) = −2.51, p = .037), C3 (t(8) = −2.93, p = .019),
Cz (t(8) = −3.42, p = .009), C4 (t(8) = −2.05, p = .074) and O1
(t(8) = −2.07, p = .072). Low frequency sigma was maximal
at O1 on baseline (M = 0.056, S.D. = 0.197) and the test night
(M = 0.111, S.D. = 0.202); however, the increase in power was
most statistically robust at Cz (Fig. 4D).
4. Discussion
In the on-going debate among scientists over whether or
not memory consolidation is one of the functions of sleep
[42,62,76,77,78,79], recent attempts have been made to identify
which sleep stages are important for particular types of memory
consolidation [for review see: 66]. An important issue that has
been debated in the sleep and memory literature centers around
what type of learning is sleep dependent, and what specific brain
activity during sleep is important for memory consolidation.
This has been done by Smith’s group using selective sleep deprivation techniques [2,36,68,69] and sleep recording techniques
[5,6,21,26]. Generally, it has been found that simple motor procedural learning is Stage 2 sleep dependent [2,21,68,69], while
learning involving procedural rules and strategies such as the
Mirror Tracing Task is REM dependent [2,5,6,36,71]. Relationships have been found using declarative learning tasks, however,
the findings have been less consistent with respect to a particular sleep stage [3,19,22,26,56,66,83]. It appears that memory
consolidation during sleep is not a uniform process. Rather, different types of procedural learning produce dissociable changes
in brain activity during sleep. This suggests that there are different subtypes of procedural tasks across a continuum of cognitive
complexity, or according to the level of familiarity with the task
demands. A recent formulation of this hypothesis [67] and subsequent preliminary findings [54] suggest that it is not the type
of learning that affects sleep; rather, it is the individual’s initial
skill level that determines the nature of the learning-dependent
changes to sleep. In low-skill individuals, changes to REM sleep
are observed, whereas in high-skill individuals, changes to Stage
2 sleep are observed. These findings indicate that REM sleep is
involved in the consolidation of newly learned skills, whereas
Stage 2 sleep is involved in the refinement of existing skills.
An experimental design using Pursuit Rotor, Mirror Trace, and
Paired Associates learning tasks was used in the current study to
investigate the learning-dependent changes to subsequent sleep.
The findings from the present study have demonstrated that there
57
are dissociable learning-dependent changes in REM and nonREM sleep in declarative and procedural memory systems. As
hypothesized, the duration of Stage 2 sleep and sleep spindle
activity (in both Stage 2 sleep and SWS) increased following
Pursuit Rotor learning, REMs increased following Mirror Trace
learning, and theta power increased following Paired Associates
learning. There was also an unexpected increase in sigma power
following Paired Associates learning during REM sleep, and a
decrease in sigma power during Stage 2 sleep in the second half
of the night.
4.1. Pursuit Rotor learning-dependent changes in sleep
Pursuit Rotor learning on the Pursuit Rotor Task affected
sleep in a number of ways. As hypothesized, it was found that the
duration of Stage 2 sleep increased from baseline to test night following Pursuit Rotor learning, but not following Mirror Tracing
or Paired Associates learning. It was also found that the number
of sleep spindles per minute increased during Stage 2 and SWS,
and that the average duration of sleep spindles increased during
Stage 2 sleep following Pursuit Rotor learning. Together (Stage
2 duration, spindle density and spindle duration) on average,
this amounts to an additional 13.7 min of spindle activity or an
average increase of 35% more spindle time following Pursuit
Rotor learning. These findings suggest that simple procedural
memory consolidation may require Stage 2 sleep, with a higher
density of spindles that are longer in duration. In addition, SWS
may be necessary where the changes in spindle generation persist. The overall learning group by night interaction effect for
increased spindle density during SWS was less robust than that
during Stage 2 sleep. Future research could further investigate
the function of the sleep spindle during SWS, to identify whether
the sleep spindle has a uniform function across NREM sleep
stages. Consistent with the change in spindles, power spectral
analyses revealed changes in sigma EEG power in both Stage 2
and SWS. This indicates that it is not only Stage 2 sleep per se,
but the state of NREM sleep in general that may be important for
procedural memory consolidation. The changes in sigma power
during Stage 2 sleep were observed during the second half of the
night. It is unclear whether the changes in sigma power during
SWS were isolated to the first or second half of the night. Time
of night differences in SWS sigma power could be investigated
in future research. The changes to the duration of Stage 2 sleep
and spindles may be required to efficiently encode new learning into a more permanent form. These findings provide strong
evidence that sleep spindles may be a mechanism for synaptic
plasticity of simple motor procedural memory traces in the neocortex. The finding that low frequency sigma power (12–14 Hz),
but not high frequency sigma power (14–16 Hz), increased following Pursuit Rotor learning may indicate that low frequency
sigma is particularly well suited for synaptic plasticity, and
importantly, that there may be a functional dissociation between
low and high frequency spindles. Alternatively, visual identification of sleep spindles (which includes characteristics like shape)
may be a more reliable way to detect spindles because spectra
in the 12–16 Hz range can include activity other than spindle
activity.
58
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
Analysis of the topographic changes following learning
revealed a number of interesting findings, and supported a number of hypotheses. Previous research has shown that fast spindles
are localized to posterior regions whereas slow spindles are
localized to anterior regions [16,32]. It has been suggested that
slow and fast spindles have separate generators [46], and further,
that these frequencies may be functionally dissociable. Consistent with the previous literature, sigma power was maximal at
Cz, which is typical for slower spindle frequencies [32]. Low
frequency sigma activity was maximal at Cz, and the largest
increase in low frequency sigma power from baseline to test
night was at Cz. In addition, there was an increase in low
frequency sigma activity over occipital regions. One intriguing hypothesis is that this learning-dependent change in sigma
reflects reactivation of the brain areas involved in the acquisition of motor tasks involving eye-hand coordination. Using
neuroimaging techniques, it has been found elsewhere that the
brain regions active during learning are reactivated during sleep
[53]. Thus, the increase in sigma power at centro-parietal and
occipital regions following Pursuit Rotor learning suggests that
these areas are not only reactivated during post-learning sleep,
but that sleep spindles may be a mechanism for that reactivation
and be involved in synaptic plasticity.
Since Stage 2 sleep in general has been implicated in the consolidation of simple motor procedural learning, it was important
to investigate other phasic activity that characterizes Stage 2
sleep such as K-complexes. K-complexes may occur spontaneously in sleep or they may be reliably evoked by external
stimuli [15]. While disagreement remains as to whether the
K-complex is sleep protective, or an arousal [1,11,81], its sensitivity to stimulus salience (e.g., the sleeper’s own name) suggests
that it plays a role in information processing in some way. In
the current study, there was no change in K-complex density
whether K-complexes occurred in the presence of sleep spindles,
in the absence of sleep spindles, or both combined. It may be
that K-complexes and sleep spindles are functionally unrelated
phasic events in Stage 2 sleep, even when they occur simultaneously. The possibility remains that K-complexes are related to
some aspects of memory consolidation during sleep for types of
memory that were not tested in this experiment. Alternatively, Kcomplexes may not be related to consolidation of new memories,
but rather they may be involved in memory retrieval processes
(e.g., comparing a stimulus with information stored in memory,
in order to determine whether to remain asleep or wake up to
take action).
The increase in sigma power during Stage 2 sleep following
Pursuit Rotor learning was not as strong as would be expected
given the robust changes observed in sleep spindles. FFT procedures may have not been sensitive to detecting changes in sleep
spindles since sleep spindles are not the only source of 12–16 Hz
activity in Stage 2 sleep. It is possible that other frequencies not
functionally associated with the spindle, such as high frequency
alpha may have contaminated the user-selected 12–16 Hz frequency band. Alternatively, if the association with learning and
spindles was more robust at a particular time of night, the relationship may have been blurred by collapsing NREM periods
into gross comparisons of early and late halves of the night.
An attempt was made to further investigate changes in spectral
power during Stage 2 sleep by dividing the night into NREM
periods. This method of analysis did not improve the association between spindle counts and FTT measures; however, one
problem with looking at the data in this way is that participants
did not have the same duration of NREM within the periods,
and thus any changes in spectral power isolated to the end of
the night could not be tested. This period has been suggested
to be of particular importance for the consolidation of procedural memory [80]. Nonetheless, you can expect some level of
disagreement between visual spindle counts, and sigma power
due to tonic 12–16 Hz EEG that is perhaps unrelated to spindle
activity or unaffected by learning.
4.2. Paired Associates learning-dependent changes in sleep
Paired Associates learning also had a number of dissociable effects on post-learning sleep. As predicted, there was an
increase in theta power following Paired Associates learning,
but not after Pursuit Rotor or Mirror Trace learning during
REM sleep. While it is known that theta is typically higher
during REM sleep versus NREM sleep [7], this study provides the first evidence to indicate that REM sleep theta is
involved in the consolidation of declarative memory. Declarative memory is dependent on the hippocampus [59,51]. Theta
frequency activity is associated with LTP in the hippocampus
[37], and predominates during REM sleep [7]. In addition to
the increase in theta power, there were also unexpected changes
in the sigma band following Paired Associates learning. There
was a decrease in sigma power at Oz during Stage 2 sleep that
was limited to the low frequency sigma band during the second half of the night. On the other hand, during REM sleep,
there was a significant increase in Sigma power at Cz following Paired Associates learning that was limited to the low
frequency sigma band. These findings suggest that EEG in the
sigma band may be a marker for declarative memory consolidation during Stage 2 in the last half of the night and in REM
sleep. The Paired Associates learning-dependent increase in the
sigma band appears to be independent of the sleep spindle.
Three sources of information are consistent with this explanation: there was no change in sleep spindles following Paired
Associates learning during Stage 2 sleep, and by definition,
there is an absence of sleep spindles during REM sleep [58].
Furthermore, the topographic distribution of sigma power during REM sleep was circumscribed to occipital regions, unlike
the typical centro-parietal distribution observed during Stage 2
sleep.
Further analyses were conducted on REM sleep theta power
in the Paired Associates learning condition to characterize the
topography of the learning-dependent changes in EEG. It was
found that theta increased only during REM sleep, and only
following Paired Associates learning. It was found that theta
was maximal at Cz on the baseline and test nights, and the
largest increase in theta was observed at Cz. The change in theta
power during REM sleep was largest at the vertex following
Paired Associates learning which indicates that the areas of the
cortex underlying central regions (for example, somatosensory
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
and supplementary motor cortex, or projections from subcortical structures such as the hippocampus) may be involved in the
consolidation of new declarative information during REM sleep.
Additional studies involving source localization and imaging
techniques are needed to determine the source of this activity.
Despite being maximal at occipital regions on the baseline and
test night, sigma power had the largest increase at Cz during
REM sleep. It is possible that both theta and sigma frequencies
are involved in the consolidation of declarative memory, and
provide further support that central regions of the cortex may
be involved in declarative memory consolidation during REM
sleep.
Re-testing on procedural tasks requires the task to be
repeated, as performance is used to measure improvement. Normally, re-testing on Paired Associates tasks involves only recall.
However, for this study re-testing involved both training and
recall so that the memory scores would reflect similar changes in
performance as those measured on the procedural tasks. Thus the
memory scores reflect the amount of information retained from
the initial training, and the amount of additional information
learned at re-test.
4.3. Mirror Trace learning-dependent changes in sleep
Procedural learning on tasks such as the Mirror Tracing Task
has been memory linked to REM sleep in a number of studies.
In the current study, a Sign Test revealed that there was a significantly consistent increase in the density of REMs during REM
sleep, but no significantly consistent change in REM density in
the Pursuit Rotor, Paired Associates or control groups. It has
been found that skills that are learned implicitly which require
a logical set of rules, such as the Wff’n Proof task [36,64] are
REM dependent. Moreover, improved performance on the Mirror Tracing Task has been observed following intervals of sleep
in the second half of the night during which REM predominates
[56], and performance on the Mirror Tracing Task is impaired
following REM sleep deprivation, but not Stage 2 sleep disruption [2]. More recently, Smith et al. [70] investigated changes in
REM sleep following learning on the Mirror Tracing Task and
the Tower of Hanoi. It was found that the number of REMs and
REM density increased following learning on these two tasks
compared to controls, and that the increase was most robust in
individuals with a high IQ. The results from the current investigation support these findings and suggest that when the training
is less varied (including only one of the two tasks, the Mirror
Trace) the effect is less pronounced, however, very consistent
(in 8 of the 9 participants).
One limitation of the present research was the overrepresentation of female participants in the sample, which did
not allow for gender comparisons. There are gender differences
[39] in abilities such as spatial rotation that may be relevant to
the acquisition of tasks such as the Mirror Tracing Task. Furthermore, there are also gender differences in REM, Stage 2
sleep and sigma [18,31], but little variation in slow wave activity or other measures of sleep homeostasis [18]. Other studies
have found that women have more slow wave activity than
men [8,38,48]. It has been hypothesized that changes in REM
59
sleep across the menstrual cycle may be due to fluctuations in
core body temperature [17]. Importantly, some of the findings
reported here may be specific to women, or quantifiably different in men. Future studies including a more even distribution of
men and women could investigate if the gender differences in
sleep and in cognitive abilities are related.
5. Conclusion
One of the longstanding debates in the memory literature
has been over how long it takes memory consolidation to occur.
In the current study, learning-dependent changes in sleep were
observed over the course of one night of sleep, while other
research has indicated that even a small amount of sleep during
a daytime nap is sufficient to facilitate memory consolidation
[45,47]. It is not known, however, how many nights memory
consolidation will continue. Future research could address this
issue by measuring parameters such as sleep spindles, rapid eye
movements, sigma and theta power over several nights following an intense period of learning. In addition, this paradigm
could be used to determine if phasic and tonic markers of
sleep-related memory consolidation change across the lifespan.
These parameters may serve as indicators for brain development in children, and of memory deficits associated with
aging. Indeed, age-related changes in sleep quality may be
an important factor in age-related changes in memory performance. Furthermore, events in sleep may mark important stages
in development, or indicate developmental disorders such as
Asperger’s syndrome [27]. Memory deficits and reduced cognitive capacity associated with aging may be related to sleep
fragmentation, age-related decreases in sigma power [35], sleep
spindles [50] and possibly other phasic and tonic markers such
as rapid eye movements and theta power. The electrophysiological markers of learning identified in this experiment could also
be used to identify specific memory deficits associated brain
damage from stroke, head injury, Alzheimer’s and Parkinson’s
disease.
In conclusion, the current study has identified learningdependent changes in sleep architecture, sleep-stage specific
phasic EEG events, tonic EEG frequencies, and has characterized the topography of these changes. These findings show
that different types of learning (i.e., Pursuit Rotor, Mirror Tracing, Paired Associates) affect different sleep states (i.e., NREM,
REM) in different EEG frequency bands (i.e., low frequency
sigma, theta) in dissociable brain regions (i.e., occipital, central),
and have unique phasic markers (spindles, REMs). These findings suggest that brain plasticity during sleep does not involve a
unitary process. In other words, different types of learning have
unique sleep-related memory consolidation mechanisms that act
in dissociable brain regions at different times throughout the
night.
Acknowledgements
The Brock University Sleep Research Laboratory is funded
by the Natural Science and Engineering Research Council
(N.S.E.R.C.) of Canada.
60
S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61
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