International Journal of Psychophysiology 89 (2013) 273–283
Contents lists available at ScienceDirect
International Journal of Psychophysiology
journal homepage: www.elsevier.com/locate/ijpsycho
Review
Pathophysiology of sleep-dependent memory consolidation processes
in children
C. Urbain a,b,c,⁎, S. Galer a,b,c, P. Van Bogaert b,c, P. Peigneux a,c,⁎
a
b
c
UR2NF, Neuropsychology and Functional Neuroimaging Research Group at CRCN — Center for Research in Cognition and Neurosciences, Université Libre de Bruxelles, Brussels, Belgium
LCFC — Laboratoire de Cartographie Fonctionnelle du Cerveau, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
UNI-ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
a r t i c l e
i n f o
Article history:
Received 8 February 2013
Received in revised form 13 June 2013
Accepted 17 June 2013
Available online 26 June 2013
Keywords:
Sleep
Memory consolidation
Development
Children
Epilepsy
ADHD
a b s t r a c t
Cognitive impairments are often associated with abnormal sleep activity in developmental disorders and
pathologies of childhood. Besides, accumulated evidence indicates that post-training sleep benefits to the
consolidation of recently learned information in healthy adults and children. Although sleep-dependent
consolidation effects in children are clearly established for declarative memories, they remain more debated
in the procedural memory domain. Nowadays, recent experimental data suggest close interactions between
the development of sleep-dependent plasticity markers, cortical maturation and cognition in children. In the
present review, we propose that studying sleep and memory consolidation processes in developmental
disorders and acquired childhood pathologies can provide novel, enlightening clues to understand the
pathophysiological mechanisms subtending the disruption of long-term cerebral plasticity processes eventually leading to cognitive and learning deficits in children.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
Novel information is not immediately stored at the time of learning in
its final, enduring form. Rather, fresh memories undergo a series of transformations subtended by the capacity of the brain to modify its structure
and function over time, i.e. brain plasticity (Kolb and Whishaw, 1998).
Nowadays, accumulated evidence suggests that post-learning sleep
participates in the dynamic consolidation processes by which novel
memories are gradually incorporated into pre-existing sets of mnemonic
representations (Bontempi et al., 1999; Karni et al., 1998; McClelland et
al., 1995) or subjected to forgetting (Peigneux et al., 2010). In children,
sleep disturbances have been often considered as the epiphenomena of
an underlying maturational disorder leading to cognitive impairments.
However, cortical maturation and sleep-dependent mechanisms of
brain plasticity follow similar developmental trajectories, suggesting
closest interactions between these plastic processes (Buchmann et al.,
2011; Kurth et al., 2010b, 2012). In this framework, abnormal sleep
activity in children might be a causal, or at least a contributing factor in
cognitive and learning impairments (Chan et al., 2011; Van Bogaert et
al., 2011). In the present review, we propose that studying sleep
and memory consolidation processes in developmental disorders
⁎ Corresponding authors at: UR2NF, Neuropsychology and Functional Neuroimaging
Research Unit, Université Libre de Bruxelles, Campus du Solbosch CP191, Avenue F.D.
Roosevelt 50, B-1050 Bruxelles, Belgium. Tel.: +32 2 650 26 39; fax: +32 2 650 22 09.
E-mail addresses: [email protected] (C. Urbain), [email protected]
(P. Peigneux).
0167-8760/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.ijpsycho.2013.06.022
(e.g. specific language impairment) and acquired pathologies (e.g.
childhood epilepsies) can provide novel, enlightening clues to
understand the pathophysiological mechanisms subtending the
disruption of long-term cerebral plasticity processes eventually
leading to cognitive and learning deficits.
First, we will shortly introduce the reader to the dominant theoretical models of sleep-dependent memory consolidation and supporting
evidences from adult studies, then discuss how sleep-dependent
plasticity markers evolve throughout development and interact with
cognitive functioning and/or cortical maturation. Second, we will
review those studies having investigated sleep and memory consolidation processes in healthy children. We will discuss the specificity of
these processes in children as compared to adults, and how their study
may deepen our understanding of the fundamental interactions between
sleep and memory consolidation mechanisms. Finally, we will review
several clinical syndromes associating sleep disturbances and memory
consolidation deficits, especially including attention deficit/hyperactivity
disorder (ADHD) and idiopathic childhood epilepsy syndromes.
2. Sleep-dependent memory consolidation in adults
Studies conducted in healthy adult participants have provided compelling evidence that sleep actively participates in the consolidation of
long-term memories (for recent reviews see Born and Wilhelm, 2012;
Diekelmann et al., 2009; Maquet, 2001; Peigneux and Smith, 2010;
Stickgold, 2005). In the declarative memory domain (i.e. the memory
for facts and events, usually verbalizable and explicit), beneficial effects
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of post-learning sleep on performance have been highlighted using
mostly verbal (word pairs) and visuospatial (e.g. memory for pictures
or objects' location, virtual navigation) hippocampus-dependent learning tasks. For instance, cued recall of learned pairs of words was consistently shown better after post-learning sleep than after a similar period
of time spent awake (e.g. see Fowler et al., 1973; Gais and Born, 2004;
Gais et al., 2006; Plihal and Born, 1997, 1999; Wilhelm et al., 2008; for
reviews see also Diekelmann et al., 2009; Peigneux and Smith, 2010).
Additionally, sleep may help protecting recently learned memories
against retroactive interference due to the acquisition of a novel and
related verbal material (Ellenbogen et al., 2006, 2009). Therefore,
sleep would participate in memory consolidation processes more than
by merely protecting novel memories from ongoing, nonspecific
daytime interferences and memory decay as previously advocated
(Jenkins and Dallenbach, 1924; see also Wixted, 2004).
Likewise in the non-declarative or procedural memory domain,
beneficial effects of post-learning sleep have been evidenced for the
consolidation of novel skills and habits, although results are more
controversial. Sleep-dependent improvements in visual discrimination
skills have been consistently demonstrated using the texture discrimination task (TDT; Gais et al., 2000, 2008; Karni et al., 1994; Stickgold
et al., 2000). Furthermore in this task, performance deteriorates over
repeated practice sessions within a day reflecting the saturation of the
underlying neural circuits, unless sleep is allowed between sessions
(Mednick et al., 2002). In this case, performance stabilizes or even
increases depending on the duration of the sleep episode and the availability of slow wave sleep (SWS) and rapid eye movement (REM) sleep
(Mednick et al., 2003). However, results have been less reliable in the
perceptual auditory modality, with some studies reporting a positive
effect of post-learning sleep for the consolidation of auditory discrimination skills (Gaab et al., 2004) or the generalization of phonological categories (Fenn et al., 2003), and others concluding that post-training time
alone (i.e. either sleep or wakefulness) is sufficient for the development
of auditory perceptual skills (Collet et al., 2012; Gottselig et al., 2004;
Roth et al., 2005). Elementary motor skills also benefit from subsequent
sleep, as evidenced using the finger-to-thumb opposition task (FOT) or
its variant the finger tapping task (FTT). Performance in this task is
enhanced in the absence of further practice after post-training sleep
but not wakefulness (Fischer et al., 2005; Korman et al., 2003; Nishida
and Walker, 2007; Walker et al., 2002, 2003, 2005), or stabilized against
the effect of an interfering training on a different motor sequence
(Korman et al., 2007). On the other hand, results gained using visuomotor
tasks are disputable. Using the rotation adaptation task (Ghilardi et al.,
2000), studies found that post-training sleep improves (Hill et al., 2008;
Huber et al., 2004) or stabilizes (Albouy et al., 2012) motor adaptation
skills, whereas other authors concluded that time alone is actually sufficient to achieve a similar outcome (Debas et al., 2010; Donchin et al.,
2002; Doyon et al., 2009). Likewise using the serial reaction time (SRT)
task, time- but not sleep-dependent post-training effects were reported
for implicit visuomotor sequence learning (Nemeth et al., 2010; Song et
al., 2007), eventually leading to the claim that both conscious awareness
and attention are necessary components for the development of sleepdependent memory consolidation (Robertson et al., 2004; Song, 2009).
However, these findings are in contradiction with other behavioral results
(Fischer et al., 2007) and neuroimaging findings yielding evidence for the
reactivation of learning-related neuronal ensembles during REM sleep
after extended practice on a probabilistic SRT task (Maquet et al., 2000;
Peigneux et al., 2003) or for a sleep-dependent reorganization of the
neuronal activity underlying performance in this task (Urbain et al., in
press).
3. Models of memory consolidation during sleep
Sleep-dependent memory consolidation processes and their
underlying neurophysiological mechanisms have been conceptualized in the framework of two main theoretical models. According to the
theory of the hippocampo-neocortical dialogue initially proposed by
Buzsaki (1996), sleep consolidates memories by reactivating learningdependent neuronal patterns during post-training sleep. This allows
the reorganization and redistribution of novel information into preexistent set of mnemonic representations with a progressive transfer
of the stored information from hippocampal to neocortical regions
(Born and Wilhelm, 2012; Diekelmann and Born, 2010; Frankland and
Bontempi, 2005). On the other hand, the synaptic homeostasis theory
proposes that the activation of learning-related neural structures during
training leads to a local, use-dependent synaptic potentiation phenomenon. Sleep would then globally downscale synaptic potentiation to a
baseline level allowing renewed plasticity, indirectly leading to the consolidation of the newly acquired information (Tononi and Cirelli, 2003,
2006).
3.1. The hippocampo-neocortical dialogue
As illustrated in Fig. 1, the hippocampo-neocortical dialogue model
proposes that sleep-dependent memory consolidation relies on a complex dialogue between hippocampal and neocortical regions under the
feed-forward control of slow oscillations (b1 Hz). These slow oscillations originating in the prefrontal cortex during SWS (Massimini et al.,
2004; Murphy et al., 2009) feature a hyperpolarization phase during
which neurons are virtually silent (down state), followed by a depolarization phase (up state) during which neuronal activity fires in a micro
wake-like activity pattern known to facilitate neuronal interactions
(Steriade, 2006; Steriade et al., 1993a, 2001). The succession of hyperpolarization and depolarization phases progressively synchronizes
neuronal activity into a unified oscillatory machine encompassing thalamic (Steriade et al., 1993b) and hippocampo-cortical regions (Mölle
et al., 2009) known to be crucial in consolidation processes (Born and
Wilhelm, 2012). According to this model, the depolarization phase of
the slow oscillations is involved in the repeated reactivation of memory
traces temporarily stored in the hippocampus, due to the co-activation
of thalamo-cortical spindles and fast hippocampal oscillations named
sharp-wave ripples (SPWs). This synchronized activity allows the
formation of spindle-ripple events into which SPWs and freshly
reactivated hippocampal information are combined then transferred
to the neocortex during the depolarization phase of the slow oscillation (Clemens et al., 2011; Mölle et al., 2009; Siapas and Wilson,
1998; Sirota and Buzsaki, 2005; Wierzynski et al., 2009). The oscillatory mechanisms by which sleep spindles reach the neocortex may
also facilitate the putative gradual integration of reactivated hippocampal information into pre-existing sets of mnemonic representations stored over neocortical regions (Diekelmann and Born, 2010).
The hippocampo-neocortical dialogue model has been widely supported by human and animal studies showing that reactivations of
learning-related cerebral activity occur during post-training sleep. Moreover, these reactivations and/or the progressive transfer from learningrelated hippocampal to cortico-sub-cortical networks days to months
later are specifically involved in sleep-dependent memory consolidation
processes. In humans, post-training reactivation of hippocampal activity
during non-REM (NREM) sleep or SWS after a spatial navigation task
correlates with overnight performance improvement (Peigneux et al.,
2004). Furthermore, successful recall of word pairs is associated with a
sleep-dependent increase in neuronal activity first in the hippocampus
a few days after learning then in the medial prefrontal cortex (mPFC)
several months later (Gais et al., 2007; Takashima et al., 2006). Subsequent studies also found a sleep-dependent hippocampo-cortical or
sub-cortical transfer of learning-related neural activity, although sometimes in the absence of overt behavioral changes (Orban et al., 2006;
Rauchs et al., 2008; Sterpenich et al., 2007; Urbain et al., in press). Additionally, other studies showed that triggering slow oscillations (Marshall
and Born, 2011; for a review) or cueing the learned information (e.g.
using associated odors or sounds) during post-training SWS strengthens
the consolidation of novel declarative memories (Rasch et al., 2007;
C. Urbain et al. / International Journal of Psychophysiology 89 (2013) 273–283
275
Fig. 1. The hippocampo-neocortical dialogue. (A) Recent memories are progressively transferred from temporary hippocampal to long-term neocortical stores. (B) Sleep-dependent
consolidation processes rely on a complex dialogue between hippocampal and neocortical regions under the feed-forward control of slow oscillations (red) during SWS. The depolarization phase of the slow oscillations drives the repeated reactivation of memory traces temporarily stored in the hippocampus, due to the co-activation of
thalamo-cortical spindles (blue) and sharp-wave ripples (SPWs, green). This synchronized activity allows the formation of spindle-ripple events (shown at larger scale) into
which SPWs and freshly reactivated hippocampal information are combined, then transferred during the depolarization phase of the slow oscillation into neocortical long term
memory stores.
Adapted from Born and Wilhelm (2012).
Rudoy et al., 2009) and protects novel representations against retroactive
interference (Diekelmann et al., 2011). Finally, many studies have
highlighted a relation between NREM stage N2 sleep spindle activity
and various aspects of cognition including intellectual abilities, learning
and memory consolidation processes (for a review see Fogel and Smith,
2011). Spindles can be further differentiated between slow spindles
(12–14 Hz) widely spread over frontal regions and more focal fast
spindles (14–16 Hz) localized over centro-parietal regions, with these
two types of spindles being subtended by different functional neuroanatomical networks (Schabus et al., 2007) and possibly associated with
different cognitive processes (Clemens et al., 2007; Schabus et al.,
2004, 2008).
3.2. The synaptic homeostasis hypothesis
The synaptic homeostasis theory (illustrated in Fig. 2) is based on the
hypothesis that learning modifies the strength and/or the amount of synaptic connections between neurons (Silva, 2003; Ying et al., 2002). Learning therefore potentiates the synaptic weights in related cerebral
networks (Silva, 2003), eventually leading to neuronal saturation or
neuronal depression and impaired plasticity. During NREM sleep, low
frequency oscillations (slow wave activity [SWA] b 1 Hz) known to
induce synaptic depotentiation (Kemp and Bashir, 2001) would
homeostatically decrease synaptic weights to baseline level. That
is, SWA will be locally higher in learning-related, saturated synaptic
networks, eventually allowing renewed plasticity while at the same
time consolidating the learned information (Tononi and Cirelli, 2003,
2006). In this framework, synaptic downscaling (or depotentiation)
induced by slow oscillations would indirectly lead to memory
consolidation.
Huber et al. (2004) have substantiated the synaptic homeostasis
theory using high density EEG recording in participants trained to
incidentally adapt reaching movements to a systematic deviation of
the cursor (i.e. the rotation adaptation task Ghilardi et al., 2000).
Results showed not only a sleep-dependent increase of NREM sleep
slow oscillations (i.e. SWA) over the right parietal regions (BA 40,
BA 7) subtending this visuo-motor adaptation process (Cohen and
Andersen, 2002), but also a positive correlation between increased
SWA during post-training sleep and overnight gains in performance
(Huber et al., 2004). In the framework of the synaptic homeostasis
hypothesis (Tononi and Cirelli, 2003, 2006), the overnight gain in
performance is indirectly due to the synaptic downscaling process
occurring during sleep, decreasing the strength of each synapse by a
proportional amount until the total amount of synaptic weights impinging on each neuron returns to baseline level. Less effective synapses contributing to an imprecise movement (i.e. noise) will then reach
a threshold of inefficiency and cease to interfere in the execution, indirectly inducing the strengthening of relevant synaptic connections
for appropriate movements and improving the signal to noise ratio.
Altogether, this theory proposes that the synaptic homeostasis induced by slow oscillations (b1 Hz) might link cellular mechanisms
underlying learning processes.
The hippocampo-neocortical dialogue and the synaptic homeostasis
models are not mutually exclusive. In both cases they are based on
physiological processes taking place during NREM sleep, which leaves
open the question of the modeling of REM sleep contributions in memory consolidation. However, further theoretical advances are also needed, since none of the two theories are currently able to satisfactorily
explain all of the experimental observations in the field of sleep and
memory. As it stands, the hippocampo-neocortical dialogue theory
seems better fitted to explain the sleep-dependent reorganization
processes subtending the consolidation of hippocampus-dependent
declarative memories, whereas the synaptic homeostasis hypothesis
would better explain the consolidation of procedural memories through
the optimization of synaptic activity in neocortical regions necessary for
learning and subsequent performance. In any case, both theories are
“working models” that can be used as reference frames to understand
the neurophysiological modifications underlying the evolution
of sleep-dependent memory consolidation processes in children and
the impairments potentially associated with abnormal sleep activity.
4. Sleep, cognition and memory consolidation processes
in childhood
4.1. Cognition-related sleep parameters: developmental features
Childhood is characterized by an intense maturation in sleep regulation processes, especially during the first months of life (Jenni et al.,
2004) and until the age of 4–5 years (Grigg-Damberger et al., 2007).
From the age of 5 to 6 years, the architecture of sleep becomes progressively closer to that of young adults and sleep stages (REM and NREM)
become well established (Grigg-Damberger et al., 2007; Ohayon et al.,
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2004). Recent studies have suggested that sleep might be associated
with intellectual and cognitive abilities in children. For instance, there
is a positive correlation between sleep duration and intellectual scores
in children (Geiger et al., 2010; Gruber et al., 2010). Looking at sleep
features, three specific oscillatory parameters of sleep (i.e. the sleep
spindles, the cyclic alternating pattern [CAP] and the SWA) have been
found to follow at least partially an age-dependent developmental
trajectory that may be associated with the evolution of plasticity and
maturation processes (Buchmann et al., 2011). Moreover, one recent
study evidenced similar age-dependent evolution between sleep SWA,
cortical maturation and cognitive abilities (Kurth et al., 2012). Therefore,
it might be surmised that cortical maturation as well as SWS-dependent
mechanisms of brain plasticity contributes to the development of cognitive functions supporting intellectual, learning and memory consolidation abilities.
4.1.1. Sleep spindles
Sleep spindles have been proposed to be an index of cortical maturation that could indicate the severity of mental retardation and abnormal
maturational processes (De Gennaro and Ferrara, 2003). Supporting this
assumption, higher intelligence scores in school-aged children correlate
with either the amount of sleep stage 2 (Busby and Pivik, 1983) or
individual relative sigma power (Geiger et al., 2011, 2012) mainly over
central and parietal areas (Geiger et al., 2012). Chatburn et al. (2013)
also found a positive association between the total number of fast
spindles (N13 Hz) and memory for narrative in children whereas,
contrary to Geiger et al.'s (2011) study, no association was found
between overall spindle activity and full-scale IQ scores (Chatburn
et al., 2013). Additionally, negative associations between sleep spindle frequency and IQ test performance have been consistently found
in children's literature (Geiger et al., 2011; Gruber et al., 2013; Chatburn
et al., 2013), suggesting that higher spindle frequency is associated with
poorer information processing, while lower frequency would be associated with better processing. These latter findings are in contradiction with
adult findings in whom increased spindle frequency was associated with
better cognitive functioning (Bodizs et al., 2005; Brière et al., 2000; Fogel
et al., 2007; Gaillard and Blois, 1981; Nader and Smith, 2001; Schabus et
al., 2006).
Sleep spindles also develop with age. There is a gradual and moderate evolution at centro-parietal locations from 2 to 25 years, whereas
frontal spindles mature with a sudden increase of frequency during
puberty (Nagata et al., 1996; Shinomiya et al., 1999). Spindle length
and density follow an age-dependent maturation curve (Scholle et
al., 2007) predominating around 8 to 14 years over frontal regions
(Nagata et al., 1996; Shibagaki et al., 1982). These modifications are
likely to reflect developmental changes in thalamocortical structures
involved in the generation of sleep spindles (De Gennaro and
Ferrara, 2003; Shibagaki et al., 1982). In line with this assumption,
increased slow sigma power (11–12 Hz) and spindle activity in
dyslexic children was proposed to reflect both the enhancement of
cortico-cortical coupling and an over-activation in thalamo-cortical
and hippocampo-cortical circuits recruited to transfer information
between cortical posterior and anterior areas (Bruni et al., 2009a).
In these dyslexic children, the information to be read would be transferred from a disrupted posterior system to an ancillary anterior
frontal system, ultimately allowing them to decode words (Bruni et
al., 2009a), but slowly and not automatically (Shaywitz and Shaywitz,
2008).
Overall, children studies suggest that reduced spindle frequency
might be associated with better cognitive abilities (Gruber et al.,
2013; Geiger et al., 2011; Chatburn et al., 2013) whereas higher spindle frequency would be associated with cognitive difficulties (Bruni et
al., 2009a). These observations are at odds with adult studies showing
that increased spindle frequency is associated with better cognitive
functioning (Bodizs et al., 2005; Brière et al., 2000; Fogel et al.,
2007; Gaillard and Blois, 1981; Nader and Smith, 2001; Schabus et
al., 2006), and studies having investigated mainly sigma power or
amount of sleep stage 2 in children. Further research is needed to
Fig. 2. The synaptic homeostasis theory. During wakefulness (yellow), noradrenaline (NA) promotes the storage of novel information. The activation of learning-related neural
structures during training leads to a local, use-dependent synaptic potentiation phenomenon. Sleep (blue) then globally downscales synaptic potentiation to a baseline level,
allowing renewed plasticity. This SWA-related process eventually leads to the consolidation of the newly acquired information.
Adapted from Tononi and Cirelli (2006).
C. Urbain et al. / International Journal of Psychophysiology 89 (2013) 273–283
clarify the relationships between sleep spindle frequency, maturation
and cognitive abilities, and whether the age-related increase in sleep
spindle frequency may be associated with better cognitive abilities.
4.1.2. Cyclic alternating patterns (CAPs)
The cycling alternating pattern (CAP) is an endogenous rhythm
present in NREM sleep that exhibits dramatic changes with age and
allows the quantification of sleep stability (Terzano and Parrino,
2000) (for a comprehensive definition and a documented developmental evolution of CAP, see Bruni et al., 2010). Subsequent analyses
conducted in the same sample of dyslexic and age-matched control
children than in Bruni et al.'s (2009a) study discussed above have
highlighted in dyslexic children a positive correlation between verbal
IQ, full-scale IQ and reading test measures and cycling alternating
pattern (CAP) sleep parameters, namely the CAP rate and the A1
index (Bruni et al., 2009b). The latter reflecting synchronized EEG
patterns during SWS is also known to have a clear prevalence over
anterior frontal regions (Ferri et al., 2005). Bruni et al. (2009b) argued
that increased CAP rate and A1 index during SWS might be the consequence of the transfer of information between cortical-posterior and
anterior areas used by dyslexic children to overcome their reading
difficulties (Bruni et al., 2009b).
Likewise, a positive correlation between intellectual abilities and
CAP parameters was found in children and adolescents with an
Asperger's syndrome (Bruni et al., 2007). Furthermore in adults,
a positive relationship was disclosed between CAP and memory
performance (Ferini-Strambi et al., 2004). Finally, relationships
between CAP and sleep-dependent gains in memory have been
reported (Ferri et al., 2008), with a significant increase of the
slow CAP A1 component during the post-training night following
practice on a motor adaptation task (Ghilardi et al., 2000), as
compared to a non-rotation control condition. Moreover, changes
in the A1 index were positively correlated with the overnight gain
in performance, suggesting that CAP A1 linked to slow synchronized oscillations reflects underlying sleep-dependent consolidation processes (Ferri et al., 2008).
4.1.3. Slow wave activity (SWA)
As discussed above, SWA plays an important role in sleepdependent memory consolidation processes in adults. The development of SWA appears to parallel that of cortical and cognitive maturation in children. The amplitude and the slope of slow waves (Kurth et
al., 2010a) as well as SWA sharply increase until the age of 6 to 8 years
then start declining (Campbell and Feinberg, 2009; Feinberg and
Campbell, 2012; Jenni and Carskadon, 2004; Kurth et al., 2010b) with
a sudden and marked drop around 12–16 years (Feinberg and
Campbell, 2012). Buchmann et al. (2011) showed that decreased SWA
activity when entering adolescence is highly correlated with a decrease
in the cortical maturation observed during childhood and adolescence
(mostly over the medial parietal lobe and in parts of the prefrontal
cortex), reflecting the characteristic synaptic pruning processes occurring at this period (Huttenlocher, 1979; Huttenlocher et al., 1982;
Rakic et al., 1986; Zuo et al., 2005) and allowing the development of
efficient cortical networks (Buchmann et al., 2011). Additionally, using
high-density EEG, anatomical magnetic resonance imaging and behavioral assessments in a wide population of children and adolescents,
Kurth et al. (2012) evidenced a direct relationship between sleep
SWA, anatomy and skills development. Results showed that the location of maximal SWA which, according to previous results (Kurth et
al., 2010b; Shaw et al., 2008), underwent a shift from posterior to anterior regions across childhood and adolescence, was associated to the
maturation of cortically-related skills. Indeed, complex motor skills
(assessed through sequential finger tapping and reaction time tasks)
positively correlated with the age-dependent local maxima of SWA
over regions where such abilities were supposed to be grounded. Interestingly, the “back to front” SWA maturation generally preceded both
277
skill and cortical maturation by a few years (3.7 years), suggesting
that SWA might, at least to some extent, contribute to skills development, and thus putatively to long-term plasticity processes during
development. The fact that age-related changes are highly specific for
sleep parameters as well as for brain morphology and cognitive
development suggests that those parallel maturation processes are
related to common underlying mechanisms. Besides, childhood features an intense learning period. During this crucial period of development, fantastic amount of plastic processes is at play allowing children
to acquire, store and sort novel information according to their relevance
(Brehmer et al., 2007; Li et al., 2006). In this respect, investigating the
plastic processes subtending sleep-dependent memory consolidation
processes in children is of crucial importance. However, notwithstanding the utmost importance of sleep for the development of cognitive
and behavioral abilities, the studies considering the interactions between sleep and memory consolidation processes from a developmental perspective are surprisingly scarce, as reviewed in the next section.
4.2. Sleep-dependent consolidation of declarative memories in children
Although only few studies have investigated sleep-dependent memory consolidation processes in children, a beneficial impact of sleep on
the consolidation of declarative memories has been consistently demonstrated using different learning tasks (Backhaus et al., 2007;
Prehn-Kristensen et al., 2009; Wilhelm et al., 2008; Henderson et al.,
2012). Using a classical word pair learning task, two studies showed
that children aged 6–8 years (Wilhelm et al., 2008) and 9–12 years
(Backhaus et al., 2007) exhibit better recall performance after a night
of sleep than after a similar period of wakefulness, likewise in adolescents (Potkin and Bunney, 2012) and adults (e.g. see Fowler et al.,
1973; Gais and Born, 2004; Gais et al., 2006; Plihal and Born, 1997,
1999; Wilhelm et al., 2008; for reviews see also Diekelmann et al.,
2009; Peigneux and Smith, 2010). Moreover, overnight recall performance was found positively correlated with the percentage of
post-training NREM sleep, and negatively correlated with the percentage of REM sleep (Backhaus et al., 2007). A correlation between
post-training SWA and overnight gains in performance in a picture recognition task was also found in controls but not in children with attention deficit/hyperactivity disorder (ADHD) (Prehn-Kristensen et al.,
2011a). Additionally, using both a word pair verbal learning task and
a 2D object location visuo-spatial learning task (“Memory” game) to
compare sleep-dependent consolidation between children and adults,
Wilhelm et al. (2008) found that overnight changes in performance
were similar in the two tasks, but also had the same amplitude in
young and adult participants. This latter effect is worth noticing considering that the amount of SWS during post-training sleep was almost
twice as large in children. Given the crucial role of SWS for memory consolidation as evidenced in adult studies, it would have been conceivable
that children exhibiting larger amounts of SWS during the post-training
night would have obtained proportionally larger gains in performance
over a night of sleep than adults. A tentative explanatory hypothesis
for this lack of effect is that weaker and/or less extensive preexistent
declarative knowledge in children might modify the sleep-dependent
integration/consolidation of novel memories, somehow negating the
potential benefit that children could have obtained compared to adults
(Wilhelm et al., 2012a). In line with this proposal, rodent studies have
shown that the integration of novel memories into pre-existing activated cortical schemata proceeds very rapidly (Tse et al., 2007) reflecting
the influence of prior knowledge on the consolidation rate (Osada et
al., 2008). At variance with the latter hypothesis, however, it was
shown that children and adults similarly benefited from sleep to integrate novel non-word representations (e.g. “biscal”) into the mental
lexicon, even though pre-existent lexical knowledge into which novel
non-words had to be integrated (e.g. “biscuit”) was adapted (i.e. highly
familiar words) and well-balanced for each population (Henderson et
al., 2012; Dumay and Gaskell, 2007). Although sleep patterns were
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not recorded in those studies, it suggests that larger amounts of SWS in
children than in adults do not automatically entail proportionally larger
gains in performance over a night of sleep. Nevertheless, complementary studies should probe the impact of pre-existent memory
representations on memory consolidation in humans. Alternative
hypotheses would be (a) that the plus-value of obtaining greater
amounts of SWS in children is only observable after a sufficient
integration time offline, which emphasizes the necessity to conduct
delayed testing in children studies and/or (b) that sleep effectively
promotes the reorganization of cortical activity in children, but that
memory retrieval can be operated using different cerebral strategies
leading to a similar behavioral outcome, similarly to results from
adult studies (Orban et al., 2006; Rauchs et al., 2008).
4.3. Sleep-dependent consolidation of procedural memories in children
Since birth, we have developed an impressive amount of fundamental abilities through the acquisition of procedural skills and
habits, both in the sensorimotor verbal and non-verbal domains
(Ullman, 2004). Even more than in adults, studying how and whether
post-training sleep impacts on the development and consolidation of
procedural skills in children is therefore crucial to understand fine
and gross motor learning but also language acquisition processes during childhood. As it stands, only few experimental studies have investigated this issue. Initial studies failed to find a sleep-dependent
contribution in the consolidation of procedural memories in children.
Indeed, visuo-motor sequence learning was found less improved in
children after post-training sleep than in wakefulness (Wilhelm et
al., 2008) or even deteriorated over sleep (Fischer et al., 2007), unlike
in adults in whom sleep improved sequential skill abilities. Likewise,
procedural mirror tracing abilities were not more improved after
sleep than wakefulness in 10–13 year old boys (Prehn-Kristensen et
al., 2009), whereas consistent sleep-dependent effects have been observed in adults using the same task (Fogel et al., 2007; Plihal and
Born, 1997). To explain these discrepancies, it has been hypothesized
that the competition between learning-related explicit and implicit
representations on sleep-dependent memory consolidation processes
may be higher in children than in adults (Fischer et al., 2007; Wilhelm
et al., 2008, 2012a, 2012b). According to this proposal, the larger
amounts of SWA in children's sleep might preferentially favor the
strengthening of the explicit components of sensorimotor representations, which would eventually counter sleep-dependent gains in
motor performance due to declarative interference (Brown and
Robertson, 2007a, 2007b; Robertson, 2009). In line with this hypothesis, children gained more explicit knowledge than adults after a
night of sleep following implicit learning of a repeated motor sequence (Wilhelm et al., 2013).
Alternatively, a reduced impact of post-training sleep on the consolidation of procedural memories in children might be explained
considering differences between children and adults in learning
curves and performance levels achieved prior to sleep. Substantiating
this hypothesis, Wilhelm et al. (2012b) found comparable overnight
gains in performance when children and adult populations reached
similar learning levels in a sequential visuo-motor learning task. The
authors surmised that in previous studies conducted in children, the
learned representations of the procedural material were not sufficiently
robust to counteract the competitive impact of the explicit components
in the consolidation of implicit motor representations, therefore possibly flattening overnight gains in performance (Wilhelm et al., 2012a,
2012b).
Furthermore, it can be surmised that sleep-dependent reorganization of procedural learning-related cerebral activity occurs in
children in a covert manner, i.e. without behaviorally observable
differences in performance on the learned material. This equality in
performance on the learned material with modified underlying
cerebral processes would be actually subtended by the development
of different behavioral strategies, as shown in rodents (Hagewoud et
al., 2010). Similar covert sleep-dependent consolidation effects have
been also evidenced in adults both for declarative (Gais et al., 2007;
Orban et al., 2006; Rauchs et al., 2008; Sterpenich et al., 2007) and procedural (Atienza et al., 2004; Urbain et al., in press) memories. In
this framework, it was found in 15-month old infants that their
memory for the features of an artificial language is decreased
after sleep, but that their ability to extract abstract rule-like
atterns of a novel verbal material is actually enhanced (Gomez et al.,
2006). This result is reminiscent of effects observed in young zebra
finch birds. Indeed, even if the structure of the learned song is actually
deteriorated after nocturnal sleep, the birds showing the strongest
post-sleep deterioration also eventually achieve the best song learning
over time on the long term (Deregnaucourt et al., 2005). These results
suggest that sleep might benefit the development of procedural
memories already at an early developmental stage, although in an indirect manner. Accordingly, we have found that whereas performance
equally improves in 10–12 year old children after sleep or wakefulness
for the learned deviation in a visuo-motor adaptation task (adapted
from Huber et al., 2004), the presentation of the opposite, unlearned
deviation elicits twice as large interference effects after post-training
sleep than wakefulness (Urbain et al., under revision). It indicates that
sleep has contributed to the consolidation and automation of the
learned visuomotor adaptation skill in children in such a way that it
indirectly makes the learning of a novel but related motor skill slower,
due to higher proactive interference effects from the learned onto the
untrained material.
As compared to other classical procedural tasks (e.g. FFT, SRTT), a
peculiarity of the rotation adaptation task is that its consolidation has
been strongly linked with post-training NREM sleep slow oscillations
in adults (Hill et al., 2008; Huber et al., 2004) in the framework of the
synaptic homeostasis theory (Tononi and Cirelli, 2003, 2006), with
positive correlations between overnight gains in performance and
local increases in post-training SWA (Huber et al., 2004). Considering
this relationship and the presence of consistent slow wave sleepdependent consolidation effects in declarative memory in children
(Backhaus et al., 2007; Prehn-Kristensen et al., 2009; Wilhelm et al.,
2008), children may actually benefit from post-training sleep only for
SWS-dependent procedural memory tasks. In line with this reasoning,
other procedural tasks previously studied in children and having
found time- more than sleep-dependent effects actually mostly benefit
from REM and/or light non-REM (stage 2) sleep in adults. For instance,
post-training sleep spindle density was associated with the consolidation of motor and visuomotor learning (Fogel et al., 2007; Peters et al.,
2008; Smith and MacNeill, 1994) whereas REM nocturnal sleep (Fogel
et al., 2007) and diurnal stage 2 sleep were related to the development
of mirror tracing abilities (Backhaus and Junghanns, 2006). Also, it
should be considered that adaptive visuo-motor consolidation processes might be qualitatively different between children and adults. For
instance, 9 and 12 year old children, but not 17 year old young adults
exhibit delayed gains in performance at 24 h post-learning for the
trained sequence in the finger tapping task (FTT), even when exposed
to an intermediate (retroactive) interfering sequence (Dorfberger et
al., 2007). Reduced retroactve interference effects in 9 and 12 year old
children could reflect the consolidation of general motor abilities,
instead of the sequential features. However, a subsequent study showed
that consolidation was only effective for the trained sequence in the
three age groups (Dorfberger et al., 2012), undermining this hypothesis.
Altogether, available studies conducted in healthy children populations suggest the presence of sleep-dependent memory consolidation
mechanisms during the developmental phase. However, numerous
questions and inconsistencies remain to be solved, considering for instance how these mechanisms support (or not) the consolidation of
procedural memories. Dedicated studies are now needed combining
polysomnographic recordings, assessment of memory performance at
short and delayed long-term retrieval using a comprehensive array of
C. Urbain et al. / International Journal of Psychophysiology 89 (2013) 273–283
direct and indirect (e.g. interference effects) behavioral measures, as
well as systematic comparisons between children and adults to prove
or disprove the explanatory schemes discussed above. Additionally,
there is a need for neurophysiological and neuroimaging studies in
children, which will hopefully enlighten our understanding of the
post-training neural mechanisms underlying sleep-dependent memory
consolidation processes in children, to the same extent than their adult
counterparts. Finally, neurophysiological data are fundamental to
understand the pathophysiological underpinnings of developmental
disorders associating abnormal sleep activity, cognitive disturbances
and impaired sleep-dependent plasticity processes which will be
discussed in the following section.
5. Pathophysiology of sleep-dependent memory consolidation
processes in children
The studies developed in the previous sections have stressed the
close relationships existing between the development of sleep features
(e.g. spindles, CAP, SWA), brain maturation and various cognitive processes. Additionally, many studies have demonstrated a prominent
impact of sleep-dependent oscillatory mechanisms on memory consolidation processes. In this respect, sleep disturbances in children with or
without developmental disorders may contribute to the pathogenesis of
cognitive disabilities and impaired memory consolidation in these
populations. Accordingly, performance on a pictorial memory task
was characterized by a slower learning curve and decreased overnight
performance in 6–16 year old children with obstructive sleep apnea
[OSA] resulting in highly fragmented sleep, as compared to healthy controls who have stabilized performance over sleep (Kheirandish-Gozal et
al., 2010). Although this result suggests an effect of sleep perturbations
on cognitive processes, the fact that performance was already
compromised during the acquisition phase, together with the absence
of a post-training control wake condition does not allow to conclude
to a sleep-dependent impairment of memory consolidation in children
with OSA.
5.1. Sleep, memory and attention deficit/hyperactivity disorder (ADHD)
At variance, relationships between sleep-dependent memory consolidation and sleep maturation have been highlighted in children
suffering from an attention deficit/hyperactivity disorder (ADHD)
syndrome. Whereas recognition of explicitly learned pictures was
better after sleep than wakefulness in both 10–16 year old children
with ADHD and matched controls, sleep-dependent gains in performance were found markedly reduced in ADHD children. Additionally,
overnight gains in performance were positively correlated with
post-training SWA in controls but not in children with ADHD
(Prehn-Kristensen et al., 2011a). The ADHD syndrome features an
atypical functioning of the prefrontal cortex and striate brain regions
(Bush et al., 2005; Rubia et al., 1999; Shaw and Rabin, 2009; Zang et
al., 2005) as well as of connectivity patterns between these areas
(Massat et al., 2012). In this framework, it can be hypothesized that
a reduced sleep-dependent enhancement of declarative memories
in children with ADHD is linked to frontal dysfunctions in this specific
population (Wilhelm et al., 2012a). Furthermore during sleep, the
topographical distribution of SWA appears to present a maturational
delay with an increase of SWA power over central regions as compared to age- and sex-matched healthy controls (Ringli et al., 2013),
despite a globally similar topographic distribution (Kurth et al.,
2010b). In healthy subjects, the topographical evolution of SWA is
characterized by an age-dependent shift of local maxima from occipital to central areas during the transition from early to late childhood,
then to frontal areas in late adolescence (Kurth et al., 2010b, 2012). In
children with ADHD, local maxima of SWA located posteriorly to that
of control subjects along the posterior–anterior axis can be interpreted
as a delayed progression of SWA to frontal regions reflecting a
279
neuro-maturational delay. Besides, the prefrontal cortex may contribute to a “motivational” tagging of memories (Miller, 2000). This tagging
would make the memories that are the most relevant for the subject's
future a preferential target for sleep-dependent consolidation processes, as recently suggested in adults (Wilhelm et al., 2011). In this context,
it was hypothesized that this prefrontal-related process might be
altered in children with ADHD preventing them to create effective
prospective markers, eventually leading to reduced sleep-dependent
enhancement in declarative performance (Wilhelm et al., 2012a).
The consolidation of procedural memories has been also investigated
in children with ADHD using an adapted version of the SRT task
(Prehn-Kristensen et al., 2011b). Interestingly, children with ADHD
obtained a better performance after a night of sleep than after a similar
period of wakefulness, whereas sleep was not more beneficial than
wake in healthy control children, according to previous studies
(Fischer et al., 2007; Prehn-Kristensen et al., 2009; Wilhelm et al.,
2008). More precisely, performance in children with ADHD was deteriorated after wakefulness but kept at the same level than controls after
sleep. Nonetheless, overnight gains in performance were correlated
with the amount of SWS and with REM sleep density in ADHD children
only, suggesting that sleep plays an active role in the consolidation of
procedural memory. Likewise, adolescents having presented iron deficiency anemia in infancy affecting the dopaminergic neurotransmission
system have been found to improve performance on the finger-tapping
task after nocturnal sleep, which was not the case for the control population (Algarin et al., in preparation). In children with ADHD, it has been
suggested that a benefit of post-learning sleep for the consolidation of
procedural memory might be associated with a weaker capacity to benefit from sleep for the consolidation of declarative/explicit representations of motor memories (Prehn-Kristensen et al., 2011a). A reduced
weight of explicit representations would therefore diminish the impact
of the competitive interaction between explicit and implicit components
of motor memories, eventually resulting in offline gains in performance
for procedural memories unlike in healthy controls (Prehn-Kristensen et
al., 2011b; Wilhelm et al., 2012a). It has been generally assumed that
children with ADHD experience problems in initiating and maintaining
sleep (Cortese et al., 2009; Konofal et al., 2010; Wiebe et al., 2013)
with a reduced sleep duration and an increased rate of stage shifts
suggesting a hypoarousal state (Miano et al., 2006). Therefore, sleep difficulties in the ADHD have been mostly viewed as an epiphenomenon of
an underlying maturational disorder. The studies discussed above suggest that the borders between cortical and sleep maturation are blurred
in ADHD, and that further investigations are needed to better understand how sleep disturbances are contributing factors of their cognitive
deficits.
5.2. Pathophysiology of sleep-dependent memory consolidation in
epilepsy
Pathophysiological sleep-dependent memory consolidation processes have been recently evidenced in some types of childhood epilepsies. It has been hypothesized that chronically abnormal sleep activity
might be a causal factor of cognitive and learning disabilities in epileptic
children (Billard et al., 2009; Chan et al., 2011; Van Bogaert et al., 2011),
besides the possible contribution of ongoing seizures and/or
antiepileptic drug (AED) treatments (Berg et al., 2008; Fastenau et al.,
2009). In particular, the impact of interictal (infra-clinical) epileptic activity (IEA) on cognition is well recognized in the epilepsy with continuous spike and waves during slow wave sleep (CSWS) (Tassinari et al.,
2009), in which considerable cognitive improvements are observed
after pharmacological normalization of the sleep EEG activity (Aeby et
al., 2005; Buzatu et al., 2009). Similar conclusions need to be confirmed
in benign epilepsy of childhood with centro-temporal spikes (BECTS),
also characterized by abundant nocturnal interictal activity (IEA) and
recognized by some authors as the mild end of a spectrum of idiopathic
focal epilepsies, CSWS being at the other end (Parisi et al., 2010;
280
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Stephani and Carlsson, 2006). Notwithstanding, an association was suggested between the frequency and diffusion of IEA during slow-wave
sleep and the severity of behavioral and cognitive deficits (for a review
see Van Bogaert et al., 2011). Focusing on sleep-dependent memory
consolidation processes, we have investigated the specific impact of
IEA during post-training sleep on the consolidation of declarative memories in 4 cases of idiopathic focal epilepsy and a population of
age-matched control children (Urbain et al., 2011). In line with prior
studies (Backhaus et al., 2007; Prehn-Kristensen et al., 2009, 2011a;
Wilhelm et al., 2008), the recall of learned word pairs was higher after
sleep than after wakefulness in the control population. Although
the children with idiopathic focal epilepsy reached same levels of
performance than controls at the end of the learning phase, their
performance was dramatically deteriorated after a night of sleep,
suggesting impaired sleep-dependent memory consolidation. This
result was in line with studies conducted in adults and children
with epilepsy, demonstrating accelerated long term forgetting
(ALF) effects over days despite normal levels of learning and retention on the short term (Blake et al., 2000; Butler and Zeman, 2008;
Cronel-Ohayon et al., 2006; Gallassi et al., 2011; but see McGibbon
and Jansari, 2013). To test the hypothesis that IEA during posttraining sleep plays a potentially causal role in memory consolidation disturbances, the experiment was repeated in 2 children with
CSWS after one month of hydrocortisone treatment that aimed at reducing IEA. Sleep EEG was completely normalized after treatment in
one child, and partially improved in the other. Noticeably, overnight
gain in performance was back to the level of healthy controls only in
the child totally devoid of sleep-related IEA, suggesting that interictal
epileptic discharges indeed disrupt sleep-dependent memory consolidation processes (Urbain et al., 2011).
Children with CSWS present an abnormal metabolic pattern for glucose, characterized by focal hypermetabolism at the epileptic focus and
hypometabolism at distant connected areas, which normalizes in parallel with EEG abnormalities (De Tiège et al., 2008, 2009). This pattern
suggests that IEA-induced cognitive deficits are related not only to the
functional repercussions of epileptic activity at the site of the epileptic
focus, but also to the remote functional inhibition of brain areas
connected to the epileptic focus. Hypermetabolic areas are mainly located over centro-parietal region, whereas hypometabolic patterns
encompass the frontal lobes. This metabolic imbalance may impair the
sleep-dependent reorganization of cerebral activity subtending memory consolidation processes (Gais et al., 2007; Takashima et al., 2006) in
the framework of the model of the hippocampo-neocortical dialogue
(Born and Wilhelm, 2012), and have an impact on cognitive performances in epileptic children (De Tiège et al., 2008, 2009).
Finally, a recent study (Bolsterli et al., 2011) has substantiated the
hypothesis that pathophysiological sleep-related conditions in children
with idiopathic focal epilepsy might contribute to the impairment of the
neurophysiological mechanisms subtending memory consolidation
processes. This study was conducted in the framework of the Synaptic
Homeostasis Hypothesis proposing that sleep-dependent memory consolidation results from use-dependent synaptic downscaling processes
triggered by NREM sleep slow oscillations, eventually increasing the
signal-to-noise ratio of the newly encoded information (Tononi and
Cirelli, 2003, 2006). To test the impact of IEA on synaptic downscaling
processes in 9 children with CSWS (3–11 year old) as compared to
controls, they analyzed the time course of the slope of slow waves
(0.5–2 Hz). In line with previous studies (Kurth et al., 2010a; Riedner
et al., 2007), healthy controls exhibited an overnight decrease in the
slope of slow waves. At variance, SWA during NREM sleep and the
slope of slow waves remained unchanged from the first to the last
hour of sleep in children with CSWS (Bolsterli et al., 2011), indicating
a disruption of synaptic downscaling processes in epileptic children.
In this respect, the studies described in this section suggest a reduced
plasticity during sleep that may impact on sleep-dependent consolidation processes and contribute to the cognitive and learning deficits
observed in children with epilepsy. Further investigations are needed
to investigate this promising and exciting avenue of research.
6. Conclusions
Over the past decade, accumulated evidence has shown that sleep
contributes to the consolidation of declarative memories in children.
How and whether sleep helps in consolidating verbal and non-verbal procedural skills in this population remains a matter of debate and deserves
further investigations. Dedicated studies combining comprehensive behavioral measures, neurophysiological and/or neuroimaging recordings
in healthy and pathological populations are crucially needed to unravel
the mechanisms subtending the evolution of sleep-dependent memory
consolidation processes during childhood. Especially, we believe that
neurophysiological and neuroimaging investigations may contribute to
enlighten the pathophysiological associations linking abnormal sleep
patterns, cognitive disturbances and impaired sleep-dependent plasticity
processes throughout the developmental phase. These investigations
should be conducted in parallel with the study of pathological conditions
in which children present abnormal sleep patterns and cognitive deficits,
such as, for a few instances, ADHD, specific language impairments and
epileptic syndromes. In this respect, comparing the development of
sleep-dependent plasticity markers (i.e. spindles, CAP, SWA) in children
with or without cognitive disorders, and how this evolution interacts
with cognitive functioning and/or cortical maturation, constitutes a
promising field of research to understand the pathophysiological conditions subtending the long-term disruption of cerebral plasticity processes
involved in memory consolidation during sleep.
Acknowledgments
CU is supported by the ULB-ARC project “Pathophysiology of Memory
Consolidation Processes”. SG is a Research Fellow at the FRS-FNRS,
Belgium.
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