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Phil. Trans. R. Soc. A (2011) 369, 3708–3729
doi:10.1098/rsta.2011.0078
REVIEW
Large-scale functional brain networks in human
non-rapid eye movement sleep: insights from
combined electroencephalographic/functional
magnetic resonance imaging studies
B Y V ICTOR I. S POORMAKER1, *, M ICHAEL C ZISCH1 , P IERRE M AQUET2
AND L UTZ J ÄNCKE3
1 RG
Neuroimaging, Max Planck Institute of Psychiatry, Kraepelinstrasse 2-10,
80804 Munich, Germany
2 Cyclotron Research Centre, University of Liege, Bât. B30 Allée du 6 août
no. 8, 4000 Liège, Belgium
3 Institute of Psychology, University of Zurich, Binzmühlestrasse 14/25,
8050 Zürich, Switzerland
This paper reviews the existing body of knowledge on the neural correlates of
spontaneous oscillations, functional connectivity and brain plasticity in human non-rapid
eye movement (NREM) sleep. The first section reviews the evidence that specific sleep
events as slow waves and spindles are associated with transient increases in regional
brain activity. The second section describes the changes in functional connectivity during
NREM sleep, with a particular focus on changes within a low-frequency, large-scale
functional brain network. The third section will discuss the possibility that spontaneous
oscillations and differential functional connectivity are related to brain plasticity and
systems consolidation, with a particular focus on motor skill acquisition. Implications for
the mode of information processing per sleep stage and future experimental studies are
discussed.
Keywords: non-rapid eye movement sleep; spindle; functional connectivity; graph theory;
small-world; plasticity
1. Introduction
The brain is endowed with the remarkable ability to generate very different
dynamics, ranging from transient versatile oscillations underpinning cognition
to circadian and seasonal rhythms preserving the long-term adaptation to the
environment. Among this wealth of rhythms, the inescapable recurrence of sleep
suggests that its characteristic neural dynamics exert important functions and
potentially help in maintaining the integrity of brain physiology. A number of
important advances were made in our understanding of sleep rhythms by careful
*Author for correspondence ([email protected]).
One contribution of 11 to a Theme Issue ‘The complexity of sleep’.
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neurophysiological studies conducted in animals [1]. In humans, the study of sleep
has long been restricted to scalp electroencephalographic (EEG) recordings with
a limited number of sensors. These studies were critical in characterizing the
regulation of sleep by circadian and homeostatic processes [2,3], but provided little
information about the underpinning of neural activity within the brain. Recently,
advanced neuroimaging (high-density EEG, magneto-encephalography (MEG),
combined EEG and functional magnetic resonance imaging (fMRI)) allowed for
the first characterization of the neural correlates of human sleep rhythms [4–9].
This paper focuses on the neural correlates of non-rapid eye movement
(NREM) sleep. NREM sleep is characterized by a coalescence of rhythms whereby
a slow oscillation (about 1 Hz) organizes the occurrence of other rhythms, such
as spindles, theta or gamma oscillations [1,10]. Such nested frequencies have also
been observed in non-harmonic fluctuations during NREM sleep [11]. The first
section reviews the evidence that transient increases in regional brain activity
are associated with specific sleep events as slow waves and spindles. The second
section describes the changes in functional connectivity associated with sleep
onset and NREM sleep, and illustrates that ‘small-world’ network properties as
observed in wakefulness [12] change in the descent from wakefulness to slow wave
sleep (SW). The third section will discuss whether spontaneous oscillations and
differential functional connectivity are related to brain plasticity and systems
consolidation, with a particular focus on motor skill learning.
2. Neural correlates of non-rapid eye movement sleep oscillations
(a) Neural correlates of slow oscillations
The slow rhythm (<1 Hz) constitutes the fundamental rhythm that characterizes
NREM sleep. Intracellular recordings in cats showed that neuronal membrane
potential oscillates at low frequency (around 1 Hz). This oscillation shapes
neuronal activity, by alternating a depolarizing phase, associated with important
neuronal firing (‘up state’), and a hyperpolarizing phase, during which cortical
neurons remain silent for a few hundred milliseconds (‘down state’) [13,14].
This so-called slow oscillation (<1 Hz) is recorded during NREM sleep in all
major types of neocortical neurons (both excitatory and inhibitory) and occurs
synchronously in large neuronal populations. At the population level, the activity
is therefore made up of the alternation of ‘ON’ states and ‘OFF’ states. Because
these events represent massive and synchronous changes in large neuronal
populations, they can be reflected in EEG recordings as large-amplitude lowfrequency waves [15]. The slow oscillation is generated by the cortex as it can be
observed after thalamic destruction [15], in cortical slabs isolated from thalamic
influence [16] or in cortical slices [17]. However, two intrinsic conditional thalamic
oscillators also participate in the generation of the slow oscillation [18].
In humans, the taxonomy of slow waves is not always clear. A slow rhythm
was initially identified in scalp EEG recordings as the recurrence of spindles [19]
or their grouping by slow waves [20]. More recently, high-amplitude slow waves
themselves were taken as realization of the slow rhythm [21]. On the other hand,
historically, the power density in the 0.75–4 Hz frequency band, usually referred
to as ‘slow-wave activity’ (SWA), has proved a very useful and popular parameter
because it quantifies the dissipation of homeostatic sleep pressure during NREM
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sleep [22]. The frequency bounds of SWA do not respect the dichotomy between
slow (<1 Hz) and delta rhythms (1–4 Hz), which is based on differences in the
respective cellular correlates of these rhythms in animals [15]. In the temporal
domain, the amplitude of slow waves is classically larger than 75 mV [23], but
only the largest waves (>140 mV) were taken as realizations of the slow oscillation
(<1 Hz) [20,21]. This approach suggests that relatively smaller waves (amplitude
between 75 and 140 mV) correspond to delta waves (1–4 Hz). These faster waves
of smaller amplitude would also be an expression of the slow oscillation, but arise
when the synchronization in the network is less marked [24,25].
In scalp EEG recordings, the power density corresponding to slow waves (0.75–
4 Hz) predominates over frontal areas [26], where indeed the largest waves are
typically recorded. However, an analysis of individual waves demonstrated the
spatial variability of slow waves. Each wave originates at a specific site and
travels over the scalp following a particular trajectory [21]. Waves originate more
frequently in frontal regions and travel backwards to posterior areas. Beyond
this variability, slow waves seem to recruit systematically various brain regions.
Early studies based on cerebral blood flow measurement by positron emission
tomography (PET) reported that the power density of delta waves (1.5–4 Hz)
during NREM sleep was negatively correlated with regional cerebral blood flow in
the ventromedial prefrontal cortex, the basal forebrain, the striatum, the anterior
insula and the precuneus [27]. The interpretation was that the decrease in blood
flow predominated in areas that generate prominent synchronous oscillations at
low frequency. Using simultaneous EEG and event-related fMRI, it was possible
to show that slow waves were associated with transient increases in regional brain
activity, thus reconciling human physiology with animal data [6]. Slow waves were
associated with significant increases in activity in pontine tegmentum, midbrain,
cerebellum, parahippocampal gyrus, inferior frontal gyrus, middle frontal gyrus,
precuneus and posterior cingulate cortex. When compared with baseline activity,
the largest waves (>140 mV) were associated with significant activity in the
parahippocampal gyrus, cerebellum and brainstem, whereas delta waves were
related to frontal responses.
These results are remarkable for two reasons. First, they show that the slow
oscillation recruits a widespread set of brain areas, not only in the cortical
but also in the subcortical structures. The transient responses observed in the
brainstem are consistent with rhythmic neuronal firing patterns time-locked to the
cortical slow oscillation recently reported in latero-dorsal tegmenti nucleus [28]
and the locus coeruleus [29]. Collectively, these data raise the possibility that the
brain activity associated with slow oscillations represent microwake states [30],
characterized by both a high activity in thalamo-cortical (TC) loops but also
by the neuromodulatory context arising from brainstem activity. Second, these
results demonstrate that a limited number of cortical areas are consistently
recruited during slow waves, which potentially represent preferential initiation or
propagation sites for slow oscillations. Source reconstruction of scalp high-density
EEG recordings confirmed this interpretation. Although slow waves originate
more frequently in the insula and cingulate gyrus, they preferentially involve the
precuneus, the posterior cingulate, ventro-lateral and medial frontal areas [31].
It is currently believed that these areas constitute a preferred propagation
pathway because they correspond to major structural connectivity nodes in the
human brain [31].
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(b) Neural correlates of sleep spindles
Spindles constitute the hallmark of light NREM sleep, although they can
still be detected in lower amounts during deep NREM sleep. In humans,
spindles consist of waxing-and-waning 11–15 Hz oscillations, lasting 0.5–3 s. At
the cellular level, spindles arise from the cyclic inhibition of TC neurons by
reticular thalamic neurons. Post-inhibitory rebound spike bursts in TC cells
entrain cortical populations in spindle oscillations [1]. In addition, two kinds of
spindles are described in humans: slow spindles (centred around 12 Hz) and fast
spindles (centred at 14 Hz). These two spindling activities differ by their circadian
and homeostatic regulations, pharmacological reactivity, development in infancy,
evolution during ageing, modulation during menstrual cycle and pregnancy [32],
and intriguingly, by their association with general cognitive capabilities [33] and
memory processing [34]. Despite these functional differences, it is still debated
whether slow and fast spindles reflect the activity of different neural networks or
the differential modulation of a single generator.
Little is known on the cerebral correlates of human spindles. Scalp multichannel EEG recordings showed that slow spindles exhibit a variable topography,
primarily over the frontal cortex [35]. Fast spindles are topographically and
dynamically limited to the superior central and parietal cortex [35]. Source
reconstruction of scalp EEG recordings identified two sources, one for slow
spindles in a mesial frontal region and another for fast spindles in the
precuneus [36]. Early PET studies reported a negative relationship between
thalamic cerebral blood flow and the power spectrum in the spindle frequency
band [37]. Taking advantage of the high temporal resolution of combined EEG
and fMRI, it was later shown that human spindles were also associated with
transient surges in activity in the thalami, paralimbic areas (anterior cingulate
and insular cortices) and superior temporal gyri [7]. Slow spindles were further
associated with increased activity in the superior frontal gyrus. By contrast, fast
spindles recruited a set of cortical regions involved in sensorimotor processing, as
well as the mesial frontal cortex and hippocampus. The recruitment of partially
segregated cortical networks for slow and fast spindles further supports the
existence of two spindle types during human NREM sleep, with potentially
different functional significance. However, one has to keep in mind that slow and
fast spindles detected by EEG recordings might represent only a fraction of human
spindles, potentially related to the recruitment of the distributed TC system.
In contrast, MEG sleep recordings identified multiple asynchronous and focal
generators of associated sleep spindles, which would correspond to the recruitment
of the core TC system [38].
3. Functional connectivity in sleep: a large-scale network approach
(a) Resting network activity throughout non-rapid eye movement sleep
Ultraslow spontaneous fluctuations (<0.1 Hz) of the blood oxygen level dependent
(BOLD) signal as measured with fMRI show temporal correlations in functionally
related networks [39]. The neural circuitry of these networks is consistent
throughout various time-series analyses, such as correlation against the time
course of a particular seed region [39–41] or spatiotemporal independent
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component analysis [42,43]. Such networks are referred to as resting state
networks as they can be derived from spontaneous BOLD fluctuations during
rest, without engaging in a particular task. Nonetheless, the neural circuitry
of these networks matches fMRI activations in specific sensory, motor and
cognitive tasks [39]. Accordingly, networks can be divided into primary sensory
resting networks (e.g. sensorimotor, auditory and visual networks) and into
cognitive networks such as the dorsal and ventral attention networks, the
frontoparietal networks and the ‘default mode network’ (DMN) [43,44]. The
DMN, consisting of the medial prefrontal cortex, posterior cingulate, precuneus
(and retrosplenial cortex and hippocampal complex) and the bilateral inferior
parietal lobules, has received most attention because of its task-negative
activity pattern and because of its proposed involvement in episodic memory
retrieval [45], imagination of future outcomes [46] and internal versus external
awareness [47], and its possible role as a marker for psychiatric [48] and
neurological disorders [49].
Previous EEG/fMRI studies have examined these resting networks also during
sleep and reported that, in general, functional connectivity was maintained in
light sleep. Horovitz et al. [50] observed that DMN connectivity persisted in
light sleep, and also found an increase in BOLD signal fluctuations in the
visual and auditory cortices and the precuneus, among others [50]. An increase
of activity in the left precuneus and bilateral inferior parietal lobules has
been observed in early sleep stage 1 specifically [51]. Larson-Prior et al. [44]
reported no significant changes in functional connectivity in various sensory and
cognitive resting networks from wakefulness to light sleep, while an increase in
functional connectivity was observed in the dorsal attention network. Sämann
et al. [52] noted the continued presence of the DMN in light sleep, but with
reduced contribution from the posterior cingulate, retrosplenial cortex and
parahippocampal gyri. These results are largely in line with the EEG studies,
which reported increased neocortical connectivity (EEG synchronization) in
specific frequency bands during sleep [53–55].
A breakdown of intra-network connectivity was observed in SW, specifically
in the correlation between the anterior and posterior nodes of the DMN [52,56],
with the posterior areas maintaining or even strengthening their connectivity [56].
This breakdown of cortico-cortical connectivity in SW parallels a combined
high-density EEG and transcranial magnetic stimulation (TMS) study during
sleep [57], which reported that TMS-induced activation spread over the cortex
during wakefulness but remained local in SW.
The breakdown in functional connectivity during SW is in accord with
the fading of consciousness in NREM sleep [58], but the preservation or
even increase in functional connectivity in light sleep stages appears more
puzzling and counterintuitive. However, according to an information integration
of consciousness, not the connectivity per se but the capacity of a network to
integrate information is proposed critical for high complex cognitive functions
such as consciousness [59]. This would mean that a possible increase in
connectivity in light sleep could be compatible with fading consciousness if
the network’s capacity to integrate information would be reduced, which may
occur owing to exclusion of pivotal nodes (such as the thalamus) or increased
randomness in the network. In order to examine such hypotheses, a focus on a
large-scale functional brain network is required.
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(b) A large-scale functional brain network throughout sleep
Large-scale functional brain networks as derived from fMRI time series can
be examined by graph-theoretical analysis; such analysis has revealed a smallworld organization of human functional brain networks during wakefulness [60],
with high local clustering and short path length [61]. A small-world topology
is a promising model for large-scale brain networks [62] as it supports both
specialized processing in local clusters and integrated processing over the entire
network [63]. Small-world properties of large-scale brain networks have helped in
characterizing ageing processes [64,65], intelligence [66], psychiatric disorders like
schizophrenia [67] and neurodegenerative disorders [68,69].
In a recent study [70], we applied graph-theoretical analysis on the correlations
between extracted BOLD signal time courses of 90 atlas-defined cortical and
subcortical regions [71] in wakefulness, light sleep stages 1 and 2 (S1, S2) and
SW, and we observed three patterns in the descent from wakefulness to SW.
First, we found a widespread increase in cortico-cortical connectivity in light
sleep stages S1 and S2, but a strong reduction of cortico-cortical connectivity in
SW. Second, TC correlation values sharply decreased in light sleep stage S1 and
were partially restored in deeper sleep stages. Third, there was also a significant
effect of sleep on small-world properties, in particular on local clustering values,
which were highest in SW and lowest in S1 and S2 when compared with values
of randomly rewired networks as a reference. See figure 1 for the average wavelet
correlation values per stage.
The decrease in TC correlation values was specific to light sleep stage 1.
This stage has been proposed to signify a transition period rather than a sleep
stage per se [72,73]; sleep stage 2 would reflect more consolidated sleep. Here,
the subclassification of stage 1 into early stage 1 sleep (stage 1a) and a deeper
stage 1 sleep (stage 1b) may be informative, as auditory detection levels in stage
1a are comparable with waking levels, whereas in stage 1b sleep, they appear
more reduced to levels typical for consolidated sleep [74]. An increase in TC
correlation values in later sleep stages could be a consequence of sleep-specific
phenomena either originating in the thalamus or mediated by the thalamus,
such as sleep spindles [7,75]. A question is whether the thalamus becomes anticorrelated with neocortical regions in light sleep, as for instance observed in
propofol-induced anesthesia [76]. One recent study observed a positive correlation
during sleep between EEG spectral power in the 0.05–0.1 Hz frequency band and
BOLD signal fluctuations in the thalamus, and a negative correlation between this
ultraslow EEG fluctuation and BOLD signal fluctuations in mostly neocortical
association areas [77]. In the fMRI data reported in Spoormaker et al. [70]
and in figure 1, about 30 per cent of the correlations of the left and right
thalamus were (weakly) negative in S1, whereas this percentage was 15–20%
in deeper sleep stages and 0 per cent in wakefulness. Note that no global
mean regression was used during preprocessing of the fMRI-data, which may
induce artificial anti-correlations [78,79], and that the reported correlations do
not represent linear correlations but coherence measures. A negative sign in
non-global mean-regressed wavelet correlations, albeit weak, can be considered
truly negative, however more theoretical and experimental work is needed to
understand the nature of negative correlations in BOLD signal fluctuations [80,81]
and how these may be affected by the deactivation of the thalamus at sleep
onset [82,83].
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S0
S1
caudate
frontal
hipp.
occipital
pallidum
centroparietal
putamen
temporal
thalamus
SW
S2
caudate
0.8
frontal
0.6
hipp.
occipital
pallidum
centroparietal
putamen
0.4
0.2
0
temporal
thalamus
Figure 1. Average group correlation matrices in wakefulness and sleep stages. The matrices provide
the correlations of maximum overlap discrete wavelet transform coefficients at a frequency band
of 0.03–0.06 Hz of 90 (sub)cortical regions as defined in the Automated Anatomical Labeling
(AAL) atlas [71], listed in alphabetical order. BOLD signal time courses were extracted from 92
epochs of one continuous sleep stage, measured in forty 26.7 min runs in 25 subjects, reported in
Spoormaker et al. [70]. Note that the correlation values are highest in sleep stages 1 and 2, although
the same functional modules are visible in the matrices, such as occipital (visual), frontal and
para-/precentral (pre-motor) cortices. The subcortical basal ganglia regions were the only regions
with (nominally) stronger connectivity in wakefulness than in sleep. In deep sleep, one functional
module had moderate to high correlation values: frontal regions, which only show within-module
correlations and no between-module correlations as obvious in other stages. This could be related
to the role of prefrontal cortices in the generation of slow oscillations [6,21]. S0, wakefulness; S1,
sleep stage 1; S2, sleep stage 2; SW, slow wave sleep; hipp., hippocampus. The colour bar depicts
wavelet correlation values. (Online version in colour.)
Moreover, the counterintuitive increase in cortico-cortical connectivity in light
sleep is accompanied by a move towards randomness of the large-scale functional
brain network. This is reflected in critical network properties such as the
clustering coefficient that were closest to random network values in light sleep.
The unspecific increase in functional connectivity in light sleep, paralleled with
a more random network organization, limits the entropy of a network and the
capacity to integrate information, in line with an information integration theory of
consciousness [59]. In contrast, SW was characterized by a breakdown in corticocortical connectivity and by an increase in local clustering compared with random
values. Although there were no significant differences in characteristic path
length (topological distance between nodes), the correlation values of physical
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S0 > S1
S1 > S2
S2 > SW
S1 > S0
S2 > S1
S0 > SW
S0 > S2
S1 > SW
S2 > S0
SW > S1
ventral–dorsal
SW > S0
posterior–anterior
Figure 2. (Caption opposite.)
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SW > S2
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Figure 2. (Opposite.) Differential connectivity graphs illustrating the differences in connections of
90 cortical and subcortical regions in the transition from wakefulness (S0) to light sleep stages 1
(S1) and 2 (S2) and finally to SW, with lines depicting a differential correlation value more than
0.20 between two stages. Linear mixed models revealed that connectivity differences were robustly
significant across sleep stages for neocortical regions (association, primary sensory and paralimbic
cortices) but not for limbic regions [70]; the only subcortical region showing (multiple test corrected)
significant differences across sleep stages was the thalamus. The reduction in TC correlation values
is illustrated in the upper left panel, while the panels S2 > S1 and SW > S1 illustrate the partial
(trendwise) restoration of TC connectivity in light sleep stage 2 and SW. S1 > S0 and S2 > S0
visualize the statistically robust increase in neocortical connectivity in light sleep stages, and the
three S0/S1/S2 > SW panels illustrate the subsequent breakdown of neocortical connectivity in SW.
Interestingly, prefrontal cortical regions had a trend for increased connectivity in SW compared
with wakefulness. Note that the current depiction threshold is an arbitrary yet consistent threshold
as z-values for correlation differences were not significant in univariate tests owing to modest group
sizes. Reproduced with permission from the Society for Neuroscience [70].
Table 1. Hubs per stage.
stage
hubsa
wakefulness
superior parietal lobule (L), precentral gyrus (R), inferior and middle temporal
gyrus (L,R)
superior frontal gyrus (L; dorsal), paracentral gyrus (L,R), postcentral gyrus
(L,R), precentral gyrus (L,R), precuneus (L,R), inferior, middle and superior
temporal gyrus (L,R)
calcarine gyrus (L), inferior frontal gyrus (L; orbital), medial frontal gyrus (L),
superior frontal gyrus (L,R; dorsal), lingual gyrus (L,R), postcentral gyrus
(L,R), precentral gyrus (L,R), precuneus (L,R), supramarginal gyrus (L),
superior temporal gyrus (L,R)
inferior frontal gyrus (L,R; orbital), precuneus (L), supramarginal gyrus (L)
sleep stage 1
sleep stage 2
deep sleep
a Hubs are defined as nodes with ≥45 connections (out of 89 possible connections) in a binary
matrix that was generated at a connection probability of 30%. This threshold was chosen because
the network was both sparse and fully connected (no fragmentation). L, left; R, right.
long-distance connections (more than 75mm) were sharply reduced in SW. As a
result, the large-scale functional brain network in SW demonstrated both high
‘cliqueness’ and fewer physical long-range connections, which reflects a move
towards a regular network [12].
(c) An unspecific increase in neocortical connectivity in light sleep
The increase in connectivity in light sleep stages was accompanied by a network
organization that was notably different from the organization of the largescale functional brain network in wakefulness or in SW. Examining the sleepspecific correlation matrices reveals an unspecific increase in correlation strength
(figure 1), and we computed the differential correlations (figure 2) to visualize the
differences between the correlation matrices of wakefulness and sleep stages.
Both wakefulness and deep sleep are characterized by fewer hubs than light
sleep stages (table 1), whereas wakefulness is the only stage with a parietal hub.
Light sleep hubs are predominantly located in the (pre-)motor and temporal
cortices.
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These results are relevant to the debate on whether low-frequency BOLD signal
fluctuations allow us to describe changes in consciousness [84,85] and may shed
light on reorganization of a large-scale functional network during sleep. A move of
the large-scale functional brain network towards more regularity in SW provides
a functional brain state optimal for reprocessing information in segregated
functional systems, which is more in accord with a memory-reprocessing
hypothesis than with a memory-transfer hypothesis of SW [86]. The functional
relevance of a move towards more randomness in light sleep needs further
attention as it may comprise a stage suitable for global information transfer,
which could be relevant in the light of systems consolidation of memories (see
below). In short, network reconfiguration in sleep may well be a key mechanism
for our understanding of brain plasticity taking place during sleep [54], and may
have particular relevance for neuroimaging studies on sleep impairments [87–90].
4. Brain plasticity and connectivity
(a) Brain plasticity in general
Human beings rely on experience, learning and memory to shape their culture
and to adapt to the particular environment they live in. For this, learned and
stored information gathered from previous experience is vital in guiding the
control of appropriate behaviour. The importance of being able to adapt to one’s
surroundings is reflected in the extraordinary plasticity of the human brain [91].
With the advent of modern brain imaging methods and advancements in the
measurement of electrical brain activity, human brain plasticity has been explored
extensively using neuroanatomical and neurophysiological techniques.
Brain areas involved in the control of a task for which subjects demonstrate
particular expertise exhibit different neuroanatomical and neurophysiological
features. For example, professional (and semi-professional) musicians have specific
anatomical features in the motor system (for controlling the hands with which
the musical instruments are manipulated), in the entire auditory system (for
processing the musical sounds) and in the cognitive system (for controlling
memory and attention functions) [92]. Such distinctive neuroanatomical
features are not restricted to musicians. Other groups with specific expertise
reveal associated specific neuroanatomical features, including professional golf
players [93], academic mathematicians [94], professional taxi drivers [95], subjects
with a long background of bilingual experience [96] and even typists with extensive
practical experience [97].
The impact of these experience and practice-related adaptations can be
seen at macro-anatomical levels. This is reflected in increased volume and
grey matter density in brain areas involved in the control of the practiced
task. In addition, anatomical measures that index inter- and intra-hemispheric
connectivity demonstrate experience-dependent change. For example, fractional
anisotropy (FA) and diffusivity, which are measures that reflect the integrity of
the fibre tract system, indicate differences in experts compared with non-experts.
In pianists, there is a linear correlation between the number of practice hours and
the FA and diffusivity in several fibre tracts (including the corpus callosum, the
longitudinal fasciculus and other fibre tracts) [98]. In addition, the corticospinal
tract has been shown to have different FA and diffusivity values correlated with
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the commencement of musical training [99]. More recent evidence shows that the
fasciculus arcuatus, connecting temporal and frontal brain areas, is differently
organized in professional musicians [100].
Experience and practice-related adaptations can also be identified at the
level of neurophysiological activation, as indexed by changes in haemodynamic
responses in EEG–MEG-based measures of brain areas responsive to the task.
Typically, activation in the involved brain areas decreases with increasing
experience, thus indicating increased neural efficacy with increasing practice
(optimization of neurophysiological activation) [101,102]. In addition to
optimization of neural activity, a change in the activation pattern occurs with
new areas becoming active in the experienced state [103,104]. The more efficient
those brain areas that were primarily engaged in a given task become, the more
other secondary brain areas engage with these primary areas in the control of
behaviour. This means that over the course of learning, change in activation
pattern reflects a change in the contribution of psychological functions to the
control of the particular task.
Structural and neurophysiological remodelling has been observed in the human
brain over periods of weeks to months. However, the fastest plastic changes
have been identified at the microscopic level, with dendritic spines sprouting not
later than 30 min after presynaptic tetanic stimulation [105]. Using multi-modal
pharmaco-neuroimaging, acute D2 receptor blockade has been demonstrated to
induce reversible striatal volume changes and structural–functional decoupling
in motor circuits within several hours [106]. In addition, these anatomical and
neurophysiological changes were strongly related to motor deficiencies.
In summary, the human brain is highly plastic and can adapt rapidly to
environmental changes. A major finding of plasticity research is that experiencedependent anatomical and neurophysiological changes can fade away as practice
stops, indicating that plasticity is possible in both directions. This leads to the
metaphor ‘use it or lose it’ in emphasizing the reliance of the neural networks on
use-dependent influences. Although brain plasticity is an important mechanism
for the human brain, it is also associated with the same practical problems.
For example, rapid changes in the brain need to be monitored to ensure that
only those changes are maintained that are necessary for further cognitions
and behavioural control. Sleep, with its associated neurophysiological activation
stages, is one such mechanism that most probably facilitates the retention of more
important and loss of less important plastic changes.
(b) Sleep and learning: theoretical models
The brain passes through several stages during sleep, with each stage being
associated with a particular neurophysiological activation pattern. Human NREM
sleep (and particularly SW) has been demonstrated to be beneficial for the
consolidation of several types of memory [86,107]. Evidence supporting a memory
consolidation hypothesis of NREM sleep has been converging for declarative
memory tasks (for a recent review, see [86]). Although evidence for the role of
sleep in the consolidation of motor memory has been accumulating [107–114],
some design and task-induced confounds have been highlighted that challenge the
main hypothesis [115]. A recent study that controlled for circadian rhythm and
massed practice effects [116] found no beneficial effects of sleep on motor memory.
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Another recent study [117] specifically controlled for task-related inhibition
owing to massed practice in a finger-tapping task (with 30 s blocks of tapping
and rest), and observed a pattern of skill deterioration during the day and
stabilization during the night. This is inconsistent with the typically observed
pattern of enhancement during sleep, however, it is not in disagreement with a
network reconfiguration and systems consolidation perspective of sleep. We will
focus on how NREM oscillations and functional connectivity may be related to
brain plasticity and systems consolidation of motor memories, and we will first
briefly recapitulate two current models of information transfer and consolidation
during sleep.
According to the so-called synaptic homeostasis hypothesis [118], synaptic
strength is changed during the slow wave sleep stage (SWS). The basic
assumption is that there is a net-increase in synaptic strength during wakefulness.
This increase of synaptic strength is the result of experience and learning
during wakefulness. As mentioned earlier, even short-term exposure to some
environmental stimuli or even short-term motor training induces changes in
synaptic strength in some networks. Therefore, many necessary and unnecessary
synaptic connections are strengthened during wakefulness. Slow oscillating brain
activity during the SWS is proposed to facilitate downscaling of the synaptic
strengths. This downscaling is proposed to be necessary to keep energy and
volume demands constant and enable reuse of synapses that are no longer
required. Downscaling of synaptic strengths is thought to be associated with
the elimination of weak synaptic connections. The net result of this is a kind of
an enhanced signal-to-noise-ratio between strong and weak synaptic connections.
This means that memory engrams, which are stored in networks with strong
synaptic strengths, may become stronger, while memory information stored in
networks with weak synaptic strengths is eliminated.
An alternative model focuses on explicit memory processes and argues
that there is also a specific consolidation process operative during the SWS
(reprocessing and systems consolidation hypothesis) [86]. The basic assumption of
this hypothesis is that information during waking is encoded in both neocortical
and hippocampal networks. In subsequent SWSs, the representations in the
hippocampus are reactivated. In addition, the slow oscillations present during SW
are thought to enable an effective hippocampus-to-neocortex transfer of the reactivated information. However, sleep-related plasticity and systems consolidation
in cerebello-striato-cortical circuitry subserving motor skills [119] have not yet
been integrated into this model. It has been hypothesized that persisting synaptic
changes are induced by SW but are nevertheless supported by the following
rapid eye movement (REM) sleep. Thus, the succession of SW and REM sleep
is proposed to be crucial for the formation of new memory engrams and for
transferring new information to the neocortical memory network. This is in
accord with studies that observed reactivation of visual and premotor cortices
in REM sleep after visuomotor skill acquisition [120], with increased connectivity
between the visual cortex and striatum during REM sleep when the visuomotor
task involved a predictable (but implicit) sequence [121]. These two models are
not mutually exclusive as each seeks to explain memory consolidation during
sleep. Evidence for memory consolidation as an active process is increasing
(see [86]), and considerations about sleep-related memory consolidation will
probably further develop with the growing availability of experimental data.
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V. I. Spoormaker et al.
(c) Connectivity and plasticity in light and slow wave sleep
Could it be that the alteration of different functional connectivity modes
informs us about the mechanisms operative during memory (re)processing?
During sleep, there is a constant alteration between a distributed functional
network (light sleep) and strongly separated functional modules (SW). As they
are more or less disconnected from other brain areas, they most probably operate
independently of other modules. Undisturbed processing bears the advantage that
particular memory information may be activated and maintained in this network
without the interfering influence of activity from neighbouring networks or
modules. Some of these modules, which have been shown to become active during
slow oscillations in SW, strongly overlap with brain areas that are also strongly
involved in the control and consolidation of complex motor learning (see below).
Thus, motor learning before sleep may well lead to transient motor memory
engrams being partly stored in these areas and reactivated and strengthened
during sleep but without interfering neural activity.
The parietal areas (inferior parietal lobule (IPL) and superior parietal lobule
(SPL)) are strongly involved in the control of complex motor functions, especially
in situations when movements are related to external or internal reference
information [122,123]. It is conceivable that the motor-related networks located in
these areas are reprocessed in sleep too. Neuroimaging evidence for this hypothesis
has been provided by Walker and colleagues [109]. Following sleep (relative to
wake), the right primary motor cortex (M1), the medial prefrontal lobe, the
hippocampus and the left cerebellum demonstrated increased haemodynamic
responses on a motor task. In contrast, signal decreases were identified in
parietal cortices, the left insular cortex, temporal pole and fronto-polar region.
While the former regions are part of the motor control network (except the
hippocampus, which obviously plays a special role here), the latter group of
brain areas is more involved in conscious spatial monitoring and the processing
of emotional associations (e.g. emotional burden to perform the task). Thus,
increased activation in the motor areas after a night of sleep can support faster
motor output and more precise mapping of key presses. The decreased parietal
activation following sleep corresponds to decreasing parietal activation identified
during motor skill learning [123], a phenomenon believed to reflect improved
sequence automation. In addition, daytime learning of a motor task resulted
in an increased amount of NREM SWA over the parietal cortex [124]. Huber
et al. [124] showed in a high-density EEG experiment that a visuomotor task
(rotation adaptation) triggered a local increase in SWA in the parietal areas
(on the delta and fast spindle frequency bands) and that this local increase was
correlated to improved performance of the task after sleep. These considerations
lead to the view that SWA synaptic strengthening might occur during NREM in
parietal networks and be instrumental in improving the functions controlled by
this network.
Moreover, sleep spindles could play a mediating role in network reconfiguration
and overnight improvement of motor skills [34], as fast spindles have been shown
to be associated with activity in the sensorimotor cortices and supplementary
motor areas, besides activity in the thalamus, anterior cingulate, insula that
was also observed in association with slow spindles [7]. Although spindles
are particularly increased in humans after declarative memory tasks [125–128]
and also associated with increases in performance on visuospatial memory
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tasks [129,130], several studies have noted an increase in spindles after motor skill
learning [131–133]. A relationship between sleep spindles and improvements on a
motor task has been observed ([134,135] but also see [136]), and this relationship
appeared to be mediated by premotor and parietal regions [134]. These findings
may relate to the pre- and postcentral gyri being network hubs in stage 2
(table 1), although only the precuneus (not the parietal lobule) is a parietal hub
in SW. Here, it is also worth noting that although sleep spindles are a typical
characteristic of light sleep stage 2, they also occur in SW—their interplay with
hippocampal ripples during an upstate of a slow oscillation has been proposed to
be essential for brain plasticity [86]. One study on humans has provided evidence
that a finger-tapping task led to an increase of hippocampal (intracranial) EEG
power in the low-frequency range and that this was correlated with performance
enhancement in epileptic patients [137]. More research is needed to examine how
slow and ultraslow fluctuations organize higher frequency fluctuations during
sleep [10,11]—and how this is related to skill acquisition in wakefulness.
(d) What needs to be shown in future experiments
To delineate the influence of sleep on motor memory consolidation, one has to
detail what is consolidated during motor memory consolidation, as motor memory
consolidation is in itself a complicated issue.
Motor memory consolidation can be demonstrated using different tasks.
For example, repetitive finger tapping is enhanced after practising for several
hours, with M1 being the main brain area involved in improving tapping
performance [138]. Serial reaction time tasks (SRT; also termed motor sequence
learning tasks) are widely used in sleep research. Mostly, these tasks are implicitly
controlled, but they partly require the contribution of explicit control (for
example, conscious insight into the sequence). The control of SRT requires
the processing of response-, stimulus- and effector-based information. Effectorindependent information (e.g. the visual–spatial coordinates of the successive
locations in SRT) is coded in the IPL. On the other hand, coding of effectorspecific information in egocentric space (e.g. motor-response coordination) is
identified in early activations within M1 [139]. And the particular stimulus
modality (visual, tactile or auditory) plays a specific role too [140]. Thus,
motor control for SRT is much more complex than for simple finger tapping
and involves, besides M1, premotor areas, the IPL, SPL and, in part, the
hippocampus [141]. A further typical and widely implemented motor task setting
uses variants of visuomotor association or visuomotor tracking tasks [142,143].
These tasks are much more complicated than the other motor tasks discussed.
Most importantly, they require the participation of brain areas that are involved
in sensorimotor transformation (IPL and the intraparietal sulcus), stimulus-motor
mapping (managed by the lateral premotor cortex), sensory processing and the
recall of motor goals from memory (hippocampus and IPL) [139].
A further distinction has to be made as to whether we have to learn a particular
motor task anew (motor learning) or adapt a previously learned motor behaviour
to changing environmental circumstances (motor adaptation). While motor
learning requires the establishment of a motor programme, motor adaptation
requires the improvement of motor parameters inserted into the previously
acquired motor programmes. It has been shown that motor learning more strongly
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V. I. Spoormaker et al.
relies on the cortico-striatal system, while motor adaptation is more strongly
dependent on the cortico-cerebellar system [144,145]. It has been proposed that
sleep benefits tasks involving cortico-striatal systems (through interaction with
hippocampal systems) rather than cortico-cerebellar systems [119]. Moreover,
motor learning and motor adaptation can take place in an early or late training
phase. The neurophysiological changes during early learning are different from
those obtained during late learning [144]; the hippocampus has been shown
to be more strongly involved during late learning [139]. Finally, it makes a
difference whether motor learning involves coordination of both hands as opposed
to unimanual movements [146].
Taken together, motor consolidation is quite complex. Most motor learning
and motor memory tasks investigated thus far in the context of motor
memory consolidation require sensory and cognitive information. Thus, sensory
and cognitive brain areas have been recruited during most motor memory
consolidation studies in the context of sleep. Future experiments should use
experimental designs that allow separation of ‘true’ motor memory consolidation
from the more ‘sensory and cognitive’ aspects of motor learning and motor
adaptation. It has to be demonstrated whether the so-called cognitive brain areas
(and the activation pattern therein) and classical motor areas benefit directly or
indirectly from particular sleep phases. Current data support the notion that the
cognitive areas should benefit more from various sleep phases, but the potential
benefit of sleep on classical motor areas is in need of investigation. A further
problem that has still to be solved is whether time of day effects, or effects
of circadian rhythm are important variables in modulating the sleep-dependent
motor memory effects.
5. Conclusion
In the previous sections, we discussed the neural correlates of sleep spindles and
slow oscillations during human NREM sleep, and that the large-scale functional
brain network moves towards a globally distributed network in light sleep and
towards a network consisting of segregated modules in SW. These alterations
in network topology during sleep may point to critical network reconfiguration
relevant to brain plasticity, with the network in light sleep being optimal for
the global transfer of information and the network in SW optimal for isolated
reprocessing of information. Sleep-specific oscillations are thought to play a
critical role in the transfer and reprocessing of information during sleep, and
evidence suggests that regions involved in motor activity and planning are hubs
in light sleep stage 2 and are strongly associated with spindles. In conclusion, we
propose that a network approach to functional connectivity in sleep may provide
greater understanding of the relationship between sleep-specific events and brain
plasticity.
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