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High Density Electroencephalography in Sleep Research: Potential, Problems, Future Perspective

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High density EEG (hdEEG) during sleep combines the superior temporal resolution of EEG recordings with high spatial resolution. Thus, this method allows a topographical analysis of sleep EEG activity and thereby fosters the shift from a global view of sleep to a local one. HdEEG allowed to investigate sleep rhythms in terms of their characteristic behavior (e.g., the traveling of slow waves) and in terms of their relationship to cortical functioning (e.g., consciousness and cognitive abilities). Moreover, recent studies successfully demonstrated that hdEEG can be used to study brain functioning in neurological and neuro-developmental disorders, and to evaluate therapeutic approaches. This review highlights the potential, the problems, and future perspective of hdEEG in sleep research.
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REVIEW ARTICLE
published: 14 May 2012
doi: 10.3389/fneur.2012.00077
High density electroencephalography in sleep research:
potential, problems, future perspective
Caroline Lustenberger 1,2 and Reto Huber 1,2,3*
1Child Development Center, University Children’s Hospital Zurich, Zurich, Switzerland
2Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
3Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
Edited by:
Linda J. Larson-Prior, Washington
University School of Medicine in
St. Louis, USA
Reviewed by:
Sara J. Aton, University of
Pennsylvania, USA
Valdas Noreika, Medical Research
Council, UK
*Correspondence:
Reto Huber, Child Development
Center, Children’s Hospital Zurich,
Steinwiesstrasse 75, 8032 Zurich,
Switzerland.
e-mail: reto.huber@kispi.uzh.ch
High density EEG (hdEEG) during sleep combines the superior temporal resolution of EEG
recordings with high spatial resolution. Thus, this method allows a topographical analy-
sis of sleep EEG activity and thereby fosters the shift from a global view of sleep to a
local one. HdEEG allowed to investigate sleep rhythms in terms of their characteristic
behavior (e.g., the traveling of slow waves) and in terms of their relationship to cortical
functioning (e.g., consciousness and cognitive abilities). Moreover, recent studies suc-
cessfully demonstrated that hdEEG can be used to study brain functioning in neurological
and neuro-developmental disorders, and to evaluate therapeutic approaches. This review
highlights the potential, the problems, and future perspective of hdEEG in sleep research.
Keywords:high density EEG, sleep slow waves, source localization, synaptic homeostasis, traveling waves,cortical
maturation, attention-deficit hyperactivity disorder, stroke
INTRODUCTION
The study of spontaneous neural activity during sleep offers
some important advantages for investigating brain function. Sleep
recordings minimize possible confounding factors related to wak-
ing activities, including changes in the level of attention and
distractibility, and issues of motivation or cognitive capacity. Such
a reduction in confounding factors might be especially relevant
for studies investigating changes in brain activity in children and
patients with cognitive and/or behavioral impairments. When we
go to sleep our brain activity changes dramatically. Since Hans
Berger has established the human EEG in 1929 we can visual-
ize this changed brain activity, which enables quantification and
qualification of sleep. Specific sleep EEG rhythms were shown. The
most prominent ones are slow waves,large amplitude waves below
4.5 Hz, and spindles, waxing, and waning oscillations between 12
and 15 Hz. Numerous studies, using intracellular recordings in
animals, have provided detailed insights into the ionic and synap-
tic mechanisms that are responsible for generating these sleep
rhythms (Steriade et al., 1993b). Slow waves are generated by
corticocortical and thalamocortical circuits. However, such slow
waves can be generated and sustained in the neocortex alone
(e.g., Steriade et al., 1993a;Amzica and Steriade, 1995;Shu et al.,
2003) since these waves were shown to be still present after thala-
mectomy and decortication (Steriade, 2003). The activity of slow
waves (slow wave activity, SWA, EEG power between 0.75 and
4.5 Hz) represents a well established electrophysiological mea-
sure of sleep homeostasis (Achermann and Borbély, 2011). The
homeostatic regulation of sleep reflects the time course of sleep
pressure, which increases during wakefulness and wanes during
non-rapid eye movement (NREM) sleep (Achermann and Bor-
bély, 2011). Numerous experiments have shown that SWA during
NREM sleep closely mirrors this time course of sleep pressure.
Sleep deprivation (SD) results in an increase of sleep pressure.
Correspondingly, sleep after SD shows increased levels of SWA
compared to a baseline night. On the other hand, a daytime nap
reduces sleep pressure and results in a decrease of SWA in the fol-
lowing sleep period. What functions this precise regulation of SWA
subserves is still a matter of debate, but includes cortical plasticity
processes (Diekelmann and Born, 2010).
Sleep spindles are initiated by a deep brain structure, the
thalamic reticular nucleus (Steriade, 2000), in connection with
principal thalamic nuclei, and are synchronized by corticocortical,
corticothalamic, and thalamocortical loops (Kandel and Buzsaki,
1997). Numerous studies investigated their relationship to mem-
ory, intellectual ability, and sleep maintenance. Thus, several
authors suggest a beneficial effect of sleep spindles for sleep-
dependent memory formation (Gais et al., 2002;Schabus et al.,
2006;Diekelmann and Born, 2010). Sleep spindle activity was also
shown to be elevated in subjects with higher intellectual abilities
(Bodizs et al., 2005;Schabus et al., 2006;Fogel et al., 2007;Geiger
et al., 2011). Finally, sleep spindles have been related to sleep main-
tenance by protecting the cortex from external influences (Steriade
et al., 1969;Elton et al., 1997;Dang-Vu et al., 2010).
In recent years novel EEG amplifiers for high density EEG
(hdEEG) with up to 256 electrodes became available (Figure 1).
This method allows overcoming the disadvantageous low spatial
resolution of standard EEG recordings. Therefore, new analysis
tools have become available for the investigation of trait- and state-
like activity in the sleep EEG topography. Thus, hdEEG fosters the
shift from a global view of sleep to a local one, allowing to inves-
tigate localized changes in the main sleep rhythms and thereby
revealing possible abnormalities of brain functioning and mat-
uration. So far, hdEEG studies during sleep exclusively focused
on NREM sleep, since the major characteristics of NREM sleep,
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Lustenberger and Huber High density EEG during sleep
FIGURE 1 | The photo shows an 11-year-old subject wearing a high
density EEG net with 128 electrodes (“dense net array” of Electrical
Geodesics Inc.).
spindles, and slow waves, are believed to be locally regulated.
Therefore, REM sleep is mentioned only marginally in this review.
In the next chapters we will highlight the potential of hdEEG
by showing the analysis of the sleep EEG with high spatial res-
olution in healthy and clinical populations. We will also discuss
current problems of this method. Finally, the review provides
future perspectives of hdEEG.
MAPPING EEG ACTIVITY
Spectral analysis of the sleep EEG allows a quantification of the
activity in specific frequency bands for single electrodes. The high
number of electrodes in hdEEG allows a precise mapping of the
spectral power distribution across the scalp. This enables the inves-
tigation of topographical power differences in specific frequency
bands (e.g., slow waves, <4.5 Hz) within and between healthy
subjects (see Figure 2) and in clinical populations.
LOCAL SLEEP
That sleep is not only a global brain process but shows also local
aspects has already been suggested in early studies using classical
EEG recordings with few electrodes. These studies found electrode
specific differences of the sleep EEG (Findji et al., 1981;Hori,
1985;Werth et al., 1996, 1997a;Werth et al., 1997b;Cajochen
et al., 1999). For example, including up to 27 electrodes, stud-
ies showed that the topographical distributions of the activity of
sleep EEG rhythms (Buchsbaum et al., 1982;Zeitlhofer et al., 1993;
Finelli et al., 2001b) are like individual “fingerprints.” Therefore,
such topographical fingerprints are supposed to reflect trait-like
activity (Finelli et al., 2001a). Figure 2B illustrates these topo-
graphical fingerprints in the SWA range for individuals recorded
with hdEEG during sleep. Most stable fingerprints, which persists
under experimental perturbations, as for instance by SD (De Gen-
naro et al., 2005), are found for the frequency band between 8 and
16 Hz. Moreover, a twin study revealed a high heritability of such
topographic “fingerprints” (De Gennaro et al., 2008b).
Besides this trait-like activity also state-like activity has been
found during sleep. For example, using extensive hand sensory
stimulation during waking and EEG recordings (eight electrodes)
during sleep, Kattler et al. (1994) showed that waking plasticity
results in a local increase of SWA during subsequent sleep. An
interesting paradigm to relate waking activity and sleep is the
investigation of learning task induced synaptic plasticity. HdEEG
recordings made it possible to discover such local changes in the
sleep EEG.
Various studies support a role of sleep for learning and mem-
ory(forareviewseeDiekelmann and Born, 2010). Especially,
sleep spindles and slow waves seem to be related to these ben-
eficial effects (Gais et al., 2002;Huber et al., 2004;Huber et al.,
2006;Schabus et al., 2006;Hill et al., 2008). Recent studies using
hdEEG were able to show that sleep SWA on a local level can
benefit learning (e.g., Huber et al., 2004;Landsness et al., 2009,
2011b;Maatta et al., 2010). These studies showed that SWA was
locally increased after a learning task involving a circumscribed
brain region. In other words, the plastic changes induced in a right
parietal region by visuomotor adaptation learning was reflected in
increased SWA during subsequent sleep in this region. Moreover,
this local increase of SWA correlated with the sleep-dependent
performance improvement in this learning task. These examples
illustrate the importance of hdEEG, because a re-analysis of this
data showed that a sufficient number of electrodes is needed to
detect this local increase of SWA after learning (see Figure 3).
Hence, these hdEEG scalp recordings were important to show
that SWA is locally regulated. However, what electrode number
is needed to discover local changes in brain activity depends on
the size of the target area and the individual variability of the
expected effect.
Several studies support the relationship between plastic changes
and the local regulation of SWA. Thus, a local depression of cor-
tical circuits through arm immobilization was followed by a local
decrease of SWA over the relevant cortical areas (Huber et al.,
2006). A next study provided evidence that it is indeed the poten-
tiation of local cortical circuits, which leads to a local increase of
SWA. Direct cortical potentiation by means of transcranial mag-
netic stimulation (TMS) resulted in a local increase of SWA during
subsequent sleep (Huber et al., 2007). Another method to directly
manipulate synaptic strength of cortical circuits is the paired asso-
ciative stimulation (PAS) protocol, in which electrical peripheral
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Lustenberger and Huber High density EEG during sleep
FIGURE 2 | Topographical distribution of slow wave activity (EEG
power between 0.75 and 4.5Hz) during the first hour of NREM sleep
for individuals within defined age-groups. Black dots represent all 109
EEG electrodes included in this analysis. Values are color coded (red:
maxima, blue: minima). Numbers plotted on the right side of each map
depict the maximal (red) and minimal (blue) value within each map.
Topographical maps are proportionally scaled and missing electrode
values were interpolated. Representative subjects up to 24years were
selected from the study by Kurth et al. (2010).Topographical plots of
healthy control subjects older than 60 years from Neumann et al.
(unpublished data) (A) Maturation of slow wave topography illustrated for
4- to >60-year-old subjects. Each plot includes data of one representative
subject. (B) Each column represents the SWA maps of two different
sleep sessions (at least 1 week apart) for one subject. The two maps of
each subject illustrate the topographical “fingerprint” of the power
distribution in the SWA range.
FIGURE 3 | Effect of electrode down sampling on the detection of
local changes in SWA during sleep. (A) Topographical maps
illustrate the size of the local SWA increase after the rotation learning
compared to the control condition (Huber et al., 2004) for different
electrode numbers. EffMax defines the maximal effect (ratio
learning/control) present in each topographical map. (B) Size of the
effect plotted as a function of the electrode number. An asymptotic
level of the effect is reached around 100 electrodes. When the
electrode number is reduced below 100 there is an exponential
decrease in the size of the effect.This pronounced decrease in the
effect decreases the probability to detect a local change in SWA in a
significant cluster of electrodes.
somatosensory stimuli are followed at different intervals by TMS
(Abbott and Nelson,2000;Classen et al., 2004). Depending on the
inter stimulus interval and the subject, this stimulation leads to
long term potentiation (LTP) or long term depression (LTD), as
measured by somatosensory and motor evoked potentials (Abbott
and Nelson, 2000;Stefanet al., 2000;Classen et al., 2004). Using this
paradigm combined with hdEEG during sleep, Huber et al. (2008)
was able to show that PAS induced LTD was followed by a local
decrease of sleep SWA and PAS induced LTP by a local increase
of SWA. It is important to note that PAS induced LTD and PAS
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Lustenberger and Huber High density EEG during sleep
induced LTP involved the exact same amount of stimuli. More-
over, the PAS induced change in synaptic strength during waking,
assessed by TMS-evoked potentials, was positively correlated with
the changes in SWA during sleep and localized to similar cortical
regions (Huber et al., 2008). Other studies supported this local
relationship between PAS induced plastic changes and sleep EEG
activity in the slow wave, but also the slow spindle frequency range
(Bergmann et al., 2008;De Gennaro et al., 2008a). In summary,
these studies suggest a close relationship between the homeostatic
regulation of sleep and changes in synaptic strength.
Several parameters of sleep slow waves, e.g., their slopes, seem
to be sensitive markers for changes in synaptic strength (Riedner
et al., 2007). These findings are also supported by studies investi-
gating such a relationship on a cellular level (Vyazovskiy et al., 2008,
2009,2011) and with large-scale computer models of the thalam-
ocortical system (Hill and Tononi, 2005;Esser et al., 2007;Olcese
et al., 2010). For instance, a recent study in rats provides both mol-
ecular and electrophysiological evidence for a close relationship
between the changes in synaptic strength and the homeostatic reg-
ulation of sleep (Vyazovskiy et al., 2008). This study demonstrated
that synaptic potentiation preferentially occurs during wakeful-
ness whereas synaptic depression takes place mainly during sleep
and thereby ensuring a balance of synaptic strength across 24 h
(Vyazovskiy et al., 2008). In the synaptic homeostasis hypothesis
proposed by Tononi and Cirelli (2006), the reduction of synaptic
strength during sleep is termed synaptic downscaling, reflecting
a generalized decrease of synaptic strength. Furthermore, this
synaptic plasticity, best measured by the slope of cortical evoked
responses, was related to changes in SWA. More specifically, synap-
tic potentiation during wakefulness was associated with the level
of SWA at sleep onset whereas synaptic depression in the course
of sleep correlated with the decline of SWA in the course of a sleep
period (Vyazovskiy et al., 2008).
As highlighted above, learning before sleep is reflected in a
local increase of SWA and spindle power that may benefit post-
sleep learning task performance (Gais et al., 2002;Huber et al.,
2004, 2006;Schabus et al., 2006;Hill et al., 2008;Landsness et al.,
2009). However, active encoding of learning a task is assumed
to be restricted to waking (Aarons, 1976;Hasselmo, 1999). This
assumption may not always be true: Even though newborns are
the majority of the time asleep, they need to adapt to the postna-
tal environment by learning associations, e.g., conditioned head
rotation reflexes (Papousek, 1961). Interestingly, a hdEEG study
during sleep in newborn infants demonstrated that they are capa-
ble of learning associations (conditioned eye movement response)
while asleep (Fifer et al., 2010). Moreover, they found a localized
change in cortical activation over frontal areas as measured by
event-related scalp potentials. This change is thought to reflect the
updating of incomplete memories (Pascalis et al., 1998;Fifer et al.,
2006).
Sleep deprivation leads to global activity changes during sleep
as can be measured by an overall increase in slow frequency NREM
sleep EEG power (Borbély et al., 1981). A recent investigation
aimed at comparing and separating such global effects in EEG
power after a SD from local effects achieved by specific behav-
ioral manipulations during SD (Sarasso et al., 2011). To do so,
16 subjects underwent two 24-h SD protocols. In one protocol
subjects were engaged with extensive audiobook listening, known
to activate left fronto-temporal networks. In the other SD proto-
col subjects extensively played a driving simulator game, which
is known to activate mainly occipito-parietal cortices. Recovery
and baseline nights were recorded with a 256-channel hdEEG.
Global effects were seen by a power increase in frequencies below
12 Hz after both manipulations. Notably, when comparing the two
manipulations, significant SWA and spindle power increases were
found for the regions known to be task-specifically activated dur-
ing waking. Thus, SD reveals global and local effects (Sarasso et al.,
2011).
MATURATION
It is needless to say that the most dramatic remodeling of neural
connections takes place during childhood, which is accompanied
by increased sleep need (Iglowstein et al., 2003) and by increased
levels of SWA (Jenni and Carskadon, 2004). Studies introduced in
previous paragraphs propose that hdEEG during sleep provides
information about cortical plasticity on a local level. Therefore,
hdEEG might be a promising non-invasive method to address
the question whether a functional relationship between sleep and
brain maturation exists. To address this question we obtained
high density sleep EEG and structural magnetic resonance images
(MRI) in children and adolescents. Indeed, we found a tight rela-
tionship between sleep SWA and several MR derived markers of
brain maturation, e.g., gray matter volume (Buchmann et al.,
2011). More specifically, this relationship between the decrease
in gray matter volume and SWA was most pronounced in areas
maturing during adolescence–parts of the prefrontal cortex and
the medial temporal lobe. In another study we used hdEEG to map
cortical activity during sleep from early childhood to late adoles-
cence (Kurth et al., 2010). This analysis showed that the maximal
SWA undergoes a shift from posterior to anterior regions across the
first two decades of life. Figure 2 gives an impression of this shift
from posterior to anterior regions for single individuals. Notably,
the maximal SWA decreases with age. In conclusion, SWA paral-
lels the time course of cortical maturation (Shaw et al., 2008) and
might reflect cortical plasticity during development (Kurth et al.,
2010).
MAPPING EEG ACTIVITY IN CLINICAL POPULATIONS
Slow waves and spindles, the two main sleep rhythms, mirror
the functioning and integrity of the thalamocortical system and
corticocortical connections (Steriade and Timofeev, 2003). More-
over, slow waves seem to reflect cortical maturation (Kurth et al.,
2010;Buchmann et al., 2011). Thus, alterations in these rhythms
might be used as an indicator for thalamocortical or cortico-
cortical dysfunctioning found in neuro-degenerative and neuro-
developmental disorders. Therefore, hdEEG is a well-suited tool
to investigate localized changes during sleep in these disorders.
Attention-deficit hyperactivity disorder
Attention-deficit hyperactivity disorder (ADHD) is the most com-
mon psychiatric disorder in childhood (Olfson, 1992). Several
studies provide evidence that the underlying cause of ADHD is
a maturational delay (e.g., Kinsbourne, 1973;Shaw et al., 2007).
Considering that slow waves, and their topographical distribu-
tion, reflect cortical maturation (Kurth et al., 2010;Buchmann
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Lustenberger and Huber High density EEG during sleep
et al., 2011), it seems worthwhile to investigate the high density
sleep EEG in ADHD children. Preliminary results from our group
provide evidence that there are topographical differences in the
SWA distribution over central regions between ADHD children
and age-matched healthy controls (Ringli et al., under revision).
Moreover, in healthy children we found a positive correlation
between daytime motor activity, as assessed by actigraphy, and
SWA over motor cortical areas (Ringli et al., under revision). This
local SWA increase may reflect the use dependent plasticity of the
motor cortex. However, this correlation was not found in ADHD
patients. Thus, it seems that hyperactivity or increased stimulation
of the motor cortex in ADHD can not explain the increase in SWA
over motor areas, supporting the conclusion that differences in
SWA topography found in ADHD children reflects a maturational
delay (Ringli et al., under revision).
Schizophrenia
Another example showing the fruitfulness of applying hdEEG
sleep recordings in clinical populations are studies in schizo-
phrenic subjects by Ferrarelli et al. (2007, 2010b). These studies
uncovered topographical differences in sleep spindle activity of
schizophrenic subjects. The reduced sleep spindle activity over
centroparietal areas might reflect a dysfunction of thalamic retic-
ular and thalamocortical mechanisms and could represent a bio-
logical marker of illness. In healthy subjects the topographical
distribution of sleep spindle activity is characterized by a frontal
maximum in the slow (<13 Hz) spindle frequency range and a
centroparietal maximum in the fast (>13 Hz) spindle frequency
range(forareviewseeDe Gennaro and Ferrara, 2003). In schiz-
ophrenic patients the centroparietal reduction of sleep spindle
activity was restricted to the fast spindle frequency range (Ferrarelli
et al., 2007). A subsequent analysis including whole-night hdEEG
during sleep in schizophrenic patients showed that the activity in
the spindle frequency range is affected throughout the night (Fer-
rarelli et al., 2010b). This difference in spindle activity is unlikely
a result of antipsychotics as a third group of non-schizophrenic
patients treated with the same medication showed no such differ-
ences in spindle activity (Ferrarelli et al., 2010b). The diminished
sleep spindle activity points to a dysfunction of the thalamocorti-
cal system since sleep spindles are generated by the thalamocortical
system. A recent study of the same group investigated the prop-
erties of the thalamocortical circuits in schizophrenic patients
and healthy control subjects using a combination of TMS and
hdEEG during wakefulness (Ferrarelli et al., 2008). Comparing the
induced gamma-oscillation by TMS between schizophrenics and
healthy controls, they found a pronounced decrease in this evoked
oscillation especially in a fronto-central region in schizophrenic
patients. The authors concluded that patients with schizophre-
nia might show a dysfunction in frontal thalamocortical circuits
(Ferrarelli et al., 2008).
Interestingly, spindle activity is also associated with general
intellectual abilities (Bodizs et al., 2005;Schabus et al., 2006;Fogel
et al., 2007;Geiger et al., 2011).A recent study investigated the asso-
ciation between regional aspects of the sleep EEG especially in the
spindle frequency range and different measures of intellectual abil-
ity (e.g., fluid intelligence) in children (Geiger et al.,2012). Notably,
for frequencies in the slow and fast spindle frequency range, the
correlations with intellectual ability was over central and pari-
etal areas (Geiger et al., 2012). Many schizophrenic patients suffer
from cognitive impairments (for a review see O’Carroll, 2000) that
might be reflected in spindle deficits over the centroparietal cortex
(Ferrarelli et al., 2007, 2010b). The study by Geiger et al. (2012)
cannot rule out that spindle activity over the frontal cortex relates
to intellectual abilities since only children were included in the
analysis. The relationship between sleep spindles and intelligence
might change in the course of cortical maturation.
Depression
Slow wave sleep abnormalities are prominent in depression (Bor-
bély and Wirz-Justice, 1982;Benca et al., 1992). SD provides
therapeutic benefits in depressed patients (for a review see Hem-
meter et al., 2010). It has been proposed that these benefits
of SD relate to renormalizing the abnormal slow wave home-
ostasis (Borbély, 1987). Using hdEEG a recent study directly
investigated the role of slow wave homeostasis in the antide-
pressant action of SD by using a selective slow wave deprivation
(SWD) technique (Landsness et al., 2011c). SWD of slow waves in
fronto-central regions was associated with a decrease in depres-
sive symptom ratings suggesting a localized effect of SWD. A
previous study using source localization of hdEEG data provide
evidence that the anterior cingulate cortex, a critical brain struc-
ture in major depression, is indeed linked to generation of sleep
slow waves (Murphy et al., 2009). In addition to the investiga-
tion of spontaneous brain activity, the analysis of evoked brain
responses (e.g., auditory evoked potentials,AEP) are used to mea-
sure changes in cortical excitability, which likely reflect changes in
synaptic strength. A recent investigation demonstrated that the
homeostatic decrease of SWA in the course of a sleep period
is paralleled by a significant reduction of the AEP amplitude
from pre- to post-sleep (Hulse et al., 2011). This reduction of
AEP amplitude was most prominent over fronto-central cortical
regions. Goldstein et al. (2011) have shown that this reduction
in AEP amplitude is absent in patients suffering from major
depressive disorder (Goldstein et al., 2011). This finding sup-
ports the proposal of a diminished sleep homeostasis in depressed
patients.
Several results show regional differences, e.g., the local differ-
ences in the amplitude of AEPs. These local differences might
be overseen with an insufficient number of electrodes. Collec-
tively, these studies demonstrate that hdEEG is a powerful tool to
study brain functioning in psychiatric disorders and to evaluate
therapeutic approaches.
Rehabilitation after stroke
Preliminary hdEEG data during sleep provides evidence that
patients with a hemispheric stroke exhibit a local fronto-central
reduction of the slope of slow waves in the affected compared
to the unaffected hemisphere and also compared to healthy con-
trols (Neumann et al., unpublished data). Since the slope of slow
waves presumably reflects synaptic strength (Esser et al., 2007),
this finding indicates a reduction of synaptic strength and a cor-
responding reduction in neuronal synchronization over regions
roughly matching the location of the ischemic damage (Neumann
et al., unpublished data). Rehabilitative therapy after stroke aims
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Lustenberger and Huber High density EEG during sleep
to foster recovery of the brain by taking advantage of the brain’s
increased potential for synaptic plasticity and reorganization after
stroke (Brown et al.,2007;Murphy and Corbett, 2009;Kalra,2010).
As mentioned before, SWA seems to be a sensitive marker for local
synaptic changes. Therefore, hdEEG during sleep might be a good
method to assess local cortical reorganization after stroke ther-
apy and consequently evaluate therapeutic effects. Sarasso et al.
(2010) investigated the neural reorganization after speech therapy
in one representative patient with left hemisphere stroke lesions.
As a post-stroke aphasia rehabilitation they used a computer based
speech therapy, which is suggested to reorganize a language based
core network (IMITATE; Lee et al., 2010). Preliminary results of
this subject showed that after one session of IMITATE training,
SWA was mainly increased in regions activated during execution
of IMITATE. Therefore, these local changes of SWA might reflect
the effectiveness of the therapy (Sarasso et al., 2010).
TRAVELING WAVES
Slow oscillations are the fundamental phenomenon that organizes
other sleep rhythms as spindles and slow waves (Steriade et al.,
1993c). Using hdEEG, Massimini et al. (2004) explored the spa-
tiotemporal dynamics of sleep slow oscillations in eight adults.
To do so, they used a 256-channel hdEEG and co-registered the
electrodes to individual MRI. According to this study, slow oscil-
lations periodically sweep the cerebral cortex with a definite site
of origin and specific pattern of propagation (Massimini et al.,
2004). This data showed that slow oscillations behave like trav-
eling waves. More specifically, the timing of the negative peak
of slow waves, a clear-cut feature of slow waves, varies system-
atically across electrodes. The origin of slow waves is defined as
the location of the electrode showing the earliest negative peak.
Thereafter, the location of the negative peak rapidly spreads to
neighboring regions (Massimini et al., 2004). Though slow waves
could originate from practically every cortical site, they prefer-
entially originated from anterior cortical regions and propagated
in an antero-posterior direction (Massimini et al., 2004). Follow-
ing the pattern of origin and propagation of hundreds of sleep
slow oscillations provides a blueprint of cortical connectivity. Why
there is a predominant origin of slow waves over frontal areas
is not known. It might be speculated that this frontal predomi-
nance is due to the increased excitability seen in frontal regions
of adults (Horne, 1993;Couyoumdjian et al., 2010), which is
also reflected in the frontal predominance of SWA (Werth et al.,
1997a;Cajochen et al., 1999;Finelli et al., 2001b;Massimini et al.,
2004;Kurth et al., 2010). Since there is a shift of maximal SWA
from occipital to frontal regions (Kurth et al., 2010), it might
be interesting to explore the traveling behavior of slow waves in
children.
SOURCE LOCALIZATION
High density EEG allows to map the distribution of EEG activity.
However, under certain conditions, it might be difficult to relate
scalp potentials to the activity of the underlying cortex because
local potentials can be generated by sources that are distant from
the recording sites (Wendel et al., 2009). Source modeling of EEG
activity overcomes this problem (Wendel et al., 2009). Several sleep
studies performed LORETA (low resolution brain electromagnetic
tomography) source analysis of sleep spindles using 19 electrodes
(Anderer et al., 2001;Mander et al., 2011;Saletin et al., 2011).
Depending on the sleep spindle frequency they showed different
cortical contributions (Anderer et al., 2001). Moreover, cortical
sources seem to change across the spindle time series (Mander
et al., 2011;Saletin et al., 2011). For an accurate source modeling
of EEG activity a large number of electrodes is required (Michel
et al., 2004). However, up to now, only few hdEEG studies dur-
ing sleep performed source localization. The mapping of SWA
using hdEEG suggests that regions of the cortex are differentially
involved and that slow waves behave like traveling waves (Wer th
et al., 1997a;Cajochen et al., 1999;Finelli et al., 2001b;Massi-
mini et al., 2004;Kurth et al., 2010). Using source modeling of
sleep slow waves, Murphy et al. (2009) explored cortical sources
that are involved in the origin and propagation of slow waves.
Even though each spontaneous slow wave has its unique pattern,
the probability that a slow wave originates from a certain area
was highest in the left hemisphere, in the insula and cingulate
gyrus. Moreover,slow waves preferentially propagated via the cin-
gulate gyrus. These analysis also showed that slow waves are not
always largest where they originate from (Murphy et al., 2009)
as observed with topographical hdEEG analysis (Massimini et al.,
2004). Murphy et al. (2009) performed source modeling using
three different source localization methods: LORETA,local autore-
gressive average (LAURA) and the Bayesian minimum norm. All
three methods revealed similar results. Nevertheless, Murphy et al.
(2009) preferred LAURA since the Bayesian minimum norm was
difficult to implement for large amounts of data, as is typically
the case in sleep EEG studies. Furthermore, the LORETA con-
straints rely on a biological assumption that neighboring areas
have similar activations whereas LAURA constraints derive from
known properties of the decay of current from the source (Mur-
phy et al., 2009). Murphy et al. (2011) also used LAURA to localize
the source of the plasticity related induction of SWA over spe-
cific cortical areas, since they found this method to be the most
effective one to analyze slow waves (Murphy et al., 2009). They
showed that source localization might not be needed to assess
the local regulation of SWA during sleep. Indeed, Murphy et al.
(2011) confirmed the link between waking cortical plasticity and
sleep SWA, and revealed the same regions that were previously
found in topographical hdEEG analysis (Huber et al., 2004, 2006,
2007).
SOURCE LOCALIZATION IN A CLINICAL POPULATION–EPILEPSY
In cats it was shown that spike-wave seizures can emerge from cor-
tical slow oscillations during sleep (Steriade and Amzica, 1994).
Thus, source localization of hdEEG data might be helpful for
the identification of epileptic sources during sleep. This find-
ing implies that spike-wave seizures may originate from cortical
sources (Tucker et al., 2009) and that they are not generated
by thalamocortical circuits as previously believed (Gloor, 1978).
Moreover, hdEEG studies during wakefulness revealed that spike-
wave discharges are not generalized, as believed so far, but are
rather localized to frontal areas (Holmes et al., 2004;Tucker
et al., 2007). A recent study applied hdEEG in a patient suffer-
ing from spike-wave seizures during sleep (Tucker et al., 2009).
They found that each seizure was preceded by a series of cortical
Frontiers in Neurology | Sleep and Chronobiology May 2012 | Volume 3 | Article 77 | 6
Lustenberger and Huber High density EEG during sleep
slow oscillations. Moreover, source localization analysis using the
LORETA and LAURA approach showed that the seizures and the
cortical slow oscillation originated from the same frontopolar
region (Tucker et al., 2009). These results suggest that cortical
slow oscillations may facilitate the pathological synchronization
of the seizure (Vanhatalo et al., 2004;Tucker et al., 2009). On
a cellular level cortical slow oscillations are characterized by an
alternation of down- and up-states (Steriade et al., 1993a;Amz-
ica and Steriade, 1998;Destexhe et al., 1999;Steriade et al.,
2001;Vyazovskiy et al., 2009). The down-state reflects hyperpo-
larization of cortical neurons. This hyperpolarization suppresses
spiking activity of the cortical neurons. The up-state mirrors
depolarization of these neurons. The duration of these up and
down states is a good marker for cortical synchronization and
excitability (Vyazovskiy et al., 2009). The comparison of the
sleep slow oscillations of epileptic with non-epileptic subjects
using source localization of hdEEG data during sleep provides
preliminary evidence that the suppression of neuronal spiking
activity during the down states of epileptic cortical slow oscil-
lations is reduced compared to non-epileptic subjects (Gilbert
et al., 2011). Thus, the modulation of cortical excitability may
be the pathological basis of this type of seizures (Gilbert et al.,
2011).
EFFECTIVE CONNECTIVITY DURING SLEEP
Even though our brain is active during NREM sleep, conscious-
ness fades during this vigilance state. The fading of consciousness
might be related to an impairment of effective cortical connectivity
reflecting the causal interactions of different systems (Gerstein and
Perkel, 1969;Lee et al., 2003). Some studies investigated this pre-
diction using hdEEG and navigated TMS (Massimini et al., 2005;
Ferrarelli et al., 2010a). The TMS/hdEEG combination allows to
assess the ability of cortical areas to interact. Thus, this method can
be used to track the propagation of an induced activity from one
part of the brain to the rest of the brain and compare this propaga-
tion between sleep and wakefulness (Massimini et al., 2005). The
study showed that during wakefulness the TMS activation prop-
agated across the entire cortex along known pathways. However,
during deep NREM sleep, the response to TMS did not propagate
beyond the stimulation site (Massimini et al., 2005). This find-
ing suggests a breakdown of long-range and transcallosal effective
connectivity during deep NREM sleep. To further proof the con-
cept that the fading of consciousness is reflected in a breakdown
of cortical effective connectivity, Ferrarelli et al. (2010a) induced
a pharmacological loss of consciousness by means of midazo-
lam. After injection of the anesthetic TMS-evoked brain responses
were changed compared to wakefulness showing striking simi-
larity to the ones obtained during deep NREM sleep (Ferrarelli
et al., 2010a). Thus, the breakdown of effective cortical connec-
tivity as assessed by the combination of TMS and hdEEG might
be a promising indicator of unconsciousness (Massimini et al.,
2005).
EFFECTIVE CONNECTIVITY IN A CLINICAL POPULATION –
COMA
An interesting clinical population to further investigate conscious-
ness are patients with altered states of consciousness after severe
brain damage, as for example patients in vegetative state or min-
imal conscious state. Unlike coma patients, vegetative state or
minimal conscious state patients are awake (Kaplan and Bauer,
2011). Patients in the minimal conscious state show, in contrast
to patients in the vegetative state, minimal signs of awareness and
attention, but they are unable to communicate effectively (Giacino
et al., 2002). However, the differential diagnosis between these two
states is rather difficult and often associated with misdiagnosis
(Schnakers et al., 2009). Recent studies report differences in brain
functioning (Boly et al., 2008;Vanhaudenhuyse et al., 2010;Land-
sness et al., 2011a) and prognosis (Laureys and Boly, 2007;Luaute
et al., 2010) between these patient groups. Minimally conscious
patients are thought to exhibit conscious cognitive processing
(Schiff, 2007;Kaplan and Bauer, 2011) whereas vegetative state
patients do not. Thus, the measuring effective cortical connectiv-
ity may allow to differentiate these two groups of subjects. Using
TMS/hdEEG Rosanova et al. (2012) demonstrated a breakdown
of effective cortical connectivity in vegetative state patients as seen
during NREM sleep in healthy subjects. In contrast, minimally
conscious patients showed a widespread TMS-evoked response
similar to locked-in, conscious patients. Interestingly,longitudinal
data in patients, who gradually recovered consciousness, showed
that this change in the breakdown of effective cortical connec-
tivity precedes significant modification of the spontaneous EEG
(Rosanova et al., 2012). Moreover this clear-cut modification of
effective connectivity was detected before the patient recovered
the ability of functional communication (Rosanova et al., 2012).
Thus, TMS/hdEEG might be used as an indicator for conscious-
ness independent of the patients ability to communicate and may
serve as a diagnostic tool for the differentiation between the veg-
etative and the minimal conscious state at the individual patient’s
level (Rosanova et al., 2012).
PROBLEMS USING hdEEG
VOLUME CONDUCTION
Volume conduction is defined as the process of current flow
through the tissues between the electrical generator and the
recording electrode (Olejniczak, 2006). This process is inherent to
any measure of scalp EEG. Volume conductance might be prob-
lematic for hdEEG since it may lead to blurring of the topography
of a brain’s electrical fields (Gevins et al., 1994). Such a blurring
may question the ability of hdEEG to detect localized brain activ-
ity. However, recent studies provide good evidence that hdEEG
can be used to localize changes in EEG activity from small target
areas (Huber et al., 2004, 2006, 2007, 2008;Maatta et al., 2010;
Landsness et al., 2011b). Moreover, hdEEG allows to follow the
slow waves while they travel across the cortex in the millisecond
range (Massimini et al., 2004).
ELECTRODE BRIDGING
An increasing density of electrodes, as used in hdEEG, raises
the possibility for electrolyte spreading and bridging (Greischar
et al., 2004). There is recent evidence that hdEEG shows a lower
signal-to-noise ratio compared to low-density EEG (Kayser et al.,
2000;Greischar et al., 2004). Moreover, there exists a signifi-
cant relationship between this power decrease and electrolyte
spreading/bridging (Greischar et al., 2004). Therefore, electrolyte
www.frontiersin.org May 2012 | Volume 3 | Article 77 | 7
Lustenberger and Huber High density EEG during sleep
bridges should be prevented. Two factors may help to reduce
such electrolyte bridges. First, electrolyte spreading is less com-
mon in the electrode housings used in a cap system compared to
aspongeelectrodes(Greischar et al., 2004). Second, electrolyte
bridges can be detected and removed using an on-line bridge
detection software before the recording (e.g., Electrical Geodesics
Inc., 2003).
AMOUNT OF DATA AND STATISTICS
All-night sleep EEG with up to 256 electrodes produces huge
amounts of data (in the Terabyte range for an experiment). There-
fore, high-capacity workstations are needed to process, store, and
backup such data. The question arises whether it is worth the
effort and whether the superior spatial resolution is really needed.
As has been shown in Figure 3, localized changes of SWA following
visuomotor adaptation learning were only observed when using a
high number of electrodes. The number of electrodes needed for
a reliable EEG measure, for example to perform source localiza-
tion or to reveal topographical differences, is a crucial question.
Theoretically, more electrodes lead to a better spatial resolution.
The number of electrodes needed to show a local increase depends
on the size of the target area and the magnitude of change. Thus,
very local changes in small target areas might be neglected with
an insufficient number of electrodes. To date, electrode nets with
128 and 256 electrodes are most often used in sleep research. We
believe that 256 electrodes will not relevantly enhance spatial res-
olution compared to 128 electrodes. But it is worth to mention
that 256-electrode caps have other advantages compared to 128
electrodes. First, it is important to uniformly and accurately cover
the scalp surface. During each experiment, some electrodes need
to be excluded due to artifacts. It is a less pronounced problem to
exclude them with the 256-electrode caps, since there is a spatial
over sampling and therefore a uniform and accurate covering is
guaranteed.
Second, more electrodes provide a better distribution of the
pressure, when the subject lies on the electrodes. Thus, 256-
electrode caps are more comfortable to wear than 128-electrode
nets. This point is especially important in sleep research to pro-
vide good sleep quality. Disadvantages are that 256-electrode nets
are more expensive than 128-electrode nets and that they need
more time for preparation (approximately 45 vs. 30min). Fur-
thermore, twice as much data is generated. Thus, the decision to
use 128-electrode or 256-electrode caps depends on cost–benefit
calculation.
EEG studies with a high number of electrodes require a sta-
tistic that accounts for multiple comparisons. As for other neu-
roimaging techniques (e.g., functional MRI, fMRI) statistical non-
parametric mapping (SnPM; Nichols and Holmes, 2002) can be
used to control for multiple testing. In these analyses, a permu-
tation test is used to determine the significances of clusters of
supra-threshold electrodes and to explore the size of the clus-
ter whose incidence probability is lower than a certain threshold
(e.g., 5%).
CONCLUSION AND FUTURE PERSPECTIVES
High density EEG is a non-invasive, low-cost method that has
several advantages to investigate sleep on the cortical level. This
method overcomes the poor spatial resolution of EEG montages
with only few electrodes and allows the investigation of regional
aspects of sleep. Even though fMRI has a better spatial resolution
and additionally allows the examination of subcortical structures,
the rather poor time resolution is insufficient to track changes in
cortical activity related to sleep characteristics as slow waves or
sleep spindles (Volkow et al., 1997;Dang-Vu et al., 2008;Murphy
et al., 2009). Intracranial recordings are important to investigate
cellular mechanisms of sleep, but are limited to animal studies or
clinical populations. Furthermore, they are limited to small corti-
cal areas and neglect the large cortical areas that are for example
involved in the generation and propagation of slow waves (Mur-
phy et al., 2009). Numerous studies prove the potential of hdEEG
to precisely investigate sleep. Beyond tracking sleep in healthy
subjects, this method can be used to study brain functioning in
neurological and neuro-developmental disorders, and to evalu-
ate therapeutic approaches. Future studies should provide further
proof and enhance the ability of hdEEG during sleep as a diagnos-
tic tool in disorders with brain dysfunction, e.g., schizophrenia.
Beside the clinical relevance of hdEEG, this method might be
helpful for the understanding of basic mechanisms of sleep. For
instance, process S, the homeostatic process of sleep regulation
potentially reflecting synaptic downscaling was long believed to
be a global process. However, local differences in SWA raised the
question of whether process S is also locally regulated. So far, one
study investigated the regional aspects of process S by investigating
topographical aspects of the dynamics of this process (Rusterholz
and Achermann, 2011). Their analysis showed clear regional dif-
ferences in the dynamics of the homeostatic process. For example,
the increase and decline of process S were slowest in fronto-central
areas. A good explanation for these regional differences in the
dynamics of process S is still missing. However, these differences
might be related to region-specific plastic brain processes occur-
ring during wakefulness and sleep (Rusterholz and Achermann,
2011). HdEEG may shed further light on this issue. An interesting
possibility might be investigating the dynamics of the global and
local aspects of the homeostatic process using specific manipula-
tions during SD in combination with hdEEG, as done in the study
by Sarasso et al. (2011).
Present studies about sleep and hdEEG focused on NREM sleep.
A possible explanation for this is the assumption that slow waves
and spindles are locally regulated. So far, there is little evidence
that REM sleep is locally regulated on a cortical level. However,
van der Helm et al. (2011) showed a negative correlation between
the overnight decrease in emotion reactivity (emotion regulation)
and gamma activity during REM sleep. A topographical analysis
using 19 electrodes indicated that this association is restricted to
prefrontal areas. This region is also known to play an established
role in emotion regulation (e.g., Quirk and Beer, 2006). Therefore,
in future studies it might be interesting to study the distribution
and changes of cortical activity during REM sleep with hdEEG.
ACKNOWLEDGMENTS
We thank Rositsa Neumann,Claudio Bassetti, Maya Ringli, Salomé
Kurth, and Monique LeBourgeois for providing unpublished
data. This work was supported by the Swiss National Science
Foundation (P00A-114923 to Reto Huber).
Frontiers in Neurology | Sleep and Chronobiology May 2012 | Volume 3 | Article 77 | 8
Lustenberger and Huber High density EEG during sleep
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Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any com-
mercial or financial relationships that
could be construed as a potential con-
flict of interest.
Received: 20 January 2012; paper pending
published: 03 February 2012; accepted:
20 April 2012; published online: 14 May
2012.
Citation: Lustenberger C and Huber R
(2012) High density electroencephalogra-
phy in sleep research: potential, problems,
future perspective. Front. Neur. 3:77. doi:
10.3389/fneur.2012.00077
This article was submitted to Frontiers in
Sleep and Chronobiology, a specialty of
Frontiers in Neurology.
Copyright © 2012 Lustenberger and
Huber. This is an open-access article
distributed under the terms of the Cre-
ative Commons Attribution Non Com-
mercial License, which permits non-
commercial use, distribution, and repro-
duction in other forums, provided the
original authors and source are credited.
www.frontiersin.org May 2012 | Volume 3 | Article 77 | 11
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... In recent years, high-density (hd)EEG has evolved as a new powerful pediatric imaging 26 method as it is non-invasive and provides good spatial and temporal resolution 27 (Lustenberger and Huber, 2012). The increased use of hdEEG led to the discovery that (Lassonde et al., 2016). ...
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Adequate sleep is critical for development and facilitates the maturation of the neurophysiological circuitries at the basis of cognitive and behavioral function. Observational research has associated sleep problems in early life with worse later cognitive, psychosocial, and somatic health outcomes. Yet, the extent to which day-to-day sleep habits in early life relate to neurophysiology - acutely and long-term - remains to be explored. Here, we report that sleep habits in 32 healthy 6-month-olds assessed with actimetry are linked to fundamental aspects of their neurophysiology measured with high-density electroencephalography (hdEEG). Our study reveals four key findings: First, daytime sleep habits are linked to EEG slow wave activity (SWA). Second, habits of nighttime movement and awakenings from sleep are connected with spindle density. Third, habitual sleep timing is linked to neurophysiological connectivity quantified as Delta-coherence. And lastly, Delta-coherence at age 6 months predicts nighttime sleep duration at age 12 months. These novel findings widen our understanding that infants’ sleep habits are closely intertwined with three particular levels of neurophysiology: sleep pressure (determined by SWA), the maturation of the thalamocortical system (spindles), and the maturation of cortical connectivity (coherence). Our companion paper complements this insight in the perspective of later developmental outcomes: early thalamocortical connectivity (spindle density) at age 6 months predicts later behavioural status at 12 and 24 months. The crucial next step is to extend this concept to clinical groups to objectively characterize infants’ sleep habits “at risk” that foster later neurodevelopmental problems. Highlights Infant’s habitual sleep behavior (actimetry) is linked with their sleep neurophysiology (EEG) Habits of daytime sleeping (naps) are related to slow wave activity Infant’s movements and awakenings at nighttime are linked to their sleep spindles Sleep timing (infant’s bedtimes) is associated with cortical connectivity in the EEG
... High-density EEG during sleep may provide further insights into topographical brain activity during sleep [55] and how regional deficits in sleep EEG activity in untreated OSA [56] may relate to statistical learning processes. Discrete slow oscillations (<1 Hz) and spindle events and their coupling appear to be important for consolidation of declarative memories [57], but it is currently unclear how implicitly learnt information is governed by these dynamics and is thus an area for future work. ...
Article
Objective/Background The aim of this study was to examine the relationship between overnight consolidation of implicit statistical learning with spindle frequency EEG activity and slow frequency delta power during non-rapid eye movement (NREM) sleep in obstructive sleep apnea (OSA). Patients/Methods Forty-seven OSA participants completed the experiment. Prior to sleep, participants performed a reaction time cover task containing hidden patterns of pictures, about which participants were not informed. After the familiarisation phase, participants underwent overnight polysomnography. 24 hours after the familiarisation phase, participants performed a test phase to assess their learning of the hidden patterns, expressed as a percentage of the number of correctly identified patterns. Spindle frequency activity (SFA) and delta power (0.5-4.5 Hz), were quantified from NREM electroencephalography. Associations between statistical learning and sleep EEG, and OSA severity measures were examined. Results SFA in NREM sleep in frontal and central brain regions was positively correlated with statistical learning scores (r=0.41 to 0.31, p=0.006 to 0.044). In multiple regression, greater SFA and longer sleep onset latency were significant predictors of better statistical learning performance. Delta power and OSA severity were not significantly correlated with statistical learning. Conclusions These findings suggest spindle activity may serve as a marker of statistical learning capability in OSA. This work provides novel insight into how altered sleep physiology relates to consolidation of implicitly learnt information in patients with moderate to severe OSA.
Article
Infancy represents a critical period during which thalamocortical brain connections develop and mature. Deviations in the maturation of thalamocortical connectivity are linked to neurodevelopmental disorders. There is a lack of early biomarkers to detect and localize neuromaturational deviations, which can be overcome with mapping through high-density electroencephalography (hdEEG) assessed in sleep. Specifically, slow waves and spindles in non-rapid eye movement (NREM) sleep are generated by the thalamocortical system, and their characteristics, slow wave slope and spindle density, are closely related to neuroplasticity and learning. Recent studies further suggest that information processing during sleep underlying sleep-dependent learning is promoted by the temporal coupling of slow waves and spindles, yet slow wave-spindle coupling remains unexplored in infancy. Thus, we evaluated three potential biomarkers: 1) slow wave slope, 2) spindle density, and 3) the temporal coupling of slow waves with spindles. We use hdEEG to first examine the occurrence and spatial distribution of these three EEG features in healthy infants and second to evaluate a predictive relationship with later behavioral outcomes. We report four key findings: First, infants’ EEG features appear locally: slow wave slope is maximal in occipital and frontal areas, whereas spindle density is most pronounced frontocentrally. Second, slow waves and spindles are temporally coupled in infancy, with maximal coupling strength in the occipital areas of the brain. Third, slow wave slope, spindle density, and slow wave-spindle coupling are not associated with concurrent behavioral status (6 months). Fourth, spindle density in central and frontocentral regions at age 6 months predicts later behavioral outcomes at 12 and 24 months. Neither slow wave slope nor slow wave-spindle coupling predict behavioral development. Our results propose spindle density as an early EEG biomarker for identifying thalamocortical maturation, which can potentially be used for early diagnosis of neurodevelopmental disorders in infants. These findings are complemented by our companion paper that demonstrates the linkage of spindle density to infant nighttime movement, framing the possible role of spindles in sensorimotor microcircuitry development. Together, our studies suggest that early sleep habits, thalamocortical maturation, and behavioral outcome are closely interwoven. A crucial next step will be to evaluate whether early therapeutic interventions may be effective to reverse deviations in identified individuals at risk.HighlightsSlow waves and spindles occur in a temporally coupled manner in infancySlow wave slope, spindle density, and slow wave-spindle coupling are not related to concurrent behavioral developmentSpindle density at 6 months predicts behavioral status at 12 and 24 monthsSlow wave slope and slow wave-spindle coupling are not predictive of behavioral development
Preprint
Infancy represents a critical period during which thalamocortical brain connections develop and mature. Deviations in the maturation of thalamocortical connectivity are linked to neurodevelopmental disorders. There is a lack of early biomarkers to detect and localize neuromaturational deviations, which can be overcome with mapping through high-density electroencephalography (hdEEG) assessed in sleep. Specifically, slow waves and spindles in non-rapid eye movement (NREM) sleep are generated by the thalamocortical system, and their characteristics, slow wave slope and spindle density, are closely related to neuroplasticity and learning. Recent studies further suggest that information processing during sleep underlying sleep-dependent learning is promoted by the temporal coupling of slow waves and spindles, yet slow wave-spindle coupling remains unexplored in infancy. Thus, we evaluated three potential biomarkers: 1) slow wave slope, 2) spindle density, and 3) the temporal coupling of slow waves with spindles. We use hdEEG to first examine the occurrence and spatial distribution of these three EEG features in healthy infants and second to evaluate a predictive relationship with later behavioral outcomes. We report four key findings: First, infants’ EEG features appear locally: slow wave slope is maximal in occipital and frontal areas, whereas spindle density is most pronounced frontocentrally. Second, slow waves and spindles are temporally coupled in infancy, with maximal coupling strength in the occipital areas of the brain. Third, slow wave slope, spindle density, and slow wave-spindle coupling are not associated with concurrent behavioral status (6 months). Fourth, spindle density in central and frontocentral regions at age 6 months predicts later behavioral outcomes at 12 and 24 months. Neither slow wave slope nor slow wave-spindle coupling predict behavioral development. Our results propose spindle density as an early EEG biomarker for identifying thalamocortical maturation, which can potentially be used for early diagnosis of neurodevelopmental disorders in infants. These findings are complemented by our companion paper that demonstrates the linkage of spindle density to infant nighttime movement, framing the possible role of spindles in sensorimotor microcircuitry development. Together, our studies suggest that early sleep habits, thalamocortical maturation, and behavioral outcome are closely interwoven. A crucial next step will be to evaluate whether early therapeutic interventions may be effective to reverse deviations in identified individuals at risk. Highlights Slow waves and spindles occur in a temporally coupled manner in infancy Slow wave slope, spindle density, and slow wave-spindle coupling are not related to concurrent behavioral development Spindle density at 6 months predicts behavioral status at 12 and 24 months Slow wave slope and slow wave-spindle coupling are not predictive of behavioral development
Article
Sleep plays a critical role in neural neurodevelopment. Hallmarks of sleep reflected in the electroencephalogram during nonrapid eye movement (NREM) sleep are associated with learning processes, cognitive ability, memory, and motor functioning. Research in adults is well-established; however, the role of NREM sleep in childhood is less clear. Growing evidence suggests the importance of two NREM sleep features: slow-wave activity and sleep spindles. These features may be critical for understanding maturational change and the functional role of sleep during development. Here, we review the literature on NREM sleep from infancy to preadolescence to provide insight into the network dynamics of the developing brain. The reviewed findings show distinct relations between topographical and maturational aspects of slow waves and sleep spindles; however, the direction and consistency of these relationships vary, and associations with cognitive ability remain unclear. Future research investigating the role of NREM sleep and development would benefit from longitudinal approaches, increased control for circadian and homeostatic influences, and in early childhood, studies recording daytime naps and overnight sleep to yield increased precision for detecting age-related change. Such evidence could help explicate the role of NREM sleep and provide putative physiological markers of neurodevelopment.
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“My whole mental power has disappeared, I have sunk intellectually below the level of a beast”(a patient with schizophrenia, quoted by [Kraepelin, 1919][1], p. 25). Traditionally, significant cognitive impairment was thought to be evident only in elderly deteriorated patients with
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Full-text available
We report here that unlike adults, infants have the capacity to learn during sleep. Bioelectrical activity from face and scalp electrodes was recorded from 34 healthy neonates during an eye movement conditioning procedure. Unlike the control group, the experimental group increased their rate of conditioned eye movement responses. Infants who learned displayed a frontally-maximum positive EEG slow wave during training. The perinatal capacity to learn while sleeping may play an essential role in an infant’s rapid physiological and behavioral adaptation to the postnatal sleep environment. Furthermore, since such conditioning is likely mediated via the cerebellum, this method provides a novel approach for early identification of infants at risk for a range of developmental disorders including autism and dyslexia.
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Full-text available
Study objectives: Prolonged wakefulness leads to a progressive increase in sleep pressure, reflected in a global increase in slow wave activity (SWA, 0.5-4.5 Hz) in the sleep electroencephalogram (EEG). A global increase in wake theta activity (5-9 Hz) also occurs. Recently, it was shown that prolonged wakefulness in rodents leads to signs of "local sleep" in an otherwise awake brain, accompanied by a slow/theta wave (2-6 Hz) in the local EEG that occurs at different times in different cortical areas. Compelling evidence in animals and humans also indicates that sleep is locally regulated by the amount of experience-dependent plasticity. Here, we asked whether the extended practice of tasks that involve specific brain circuits results in increased occurrence of local intermittent theta waves in the human EEG, above and beyond the global EEG changes previously described. Design: Participants recorded with high-density EEG completed 2 experiments during which they stayed awake ≥ 24 h practicing a language task (audiobook listening [AB]) or a visuomotor task (driving simulator [DS]). Setting: Sleep laboratory. Patients or participants: 16 healthy participants (7 females). Interventions: Two extended wake periods. Measurements and results: Both conditions resulted in global increases in resting wake EEG theta power at the end of 24 h of wake, accompanied by increased sleepiness. Moreover, wake theta power as well as the occurrence and amplitude of theta waves showed regional, task-dependent changes, increasing more over left frontal derivations in AB, and over posterior parietal regions in DS. These local changes in wake theta power correlated with similar local changes in sleep low frequencies including SWA. Conclusions: Extended experience-dependent plasticity of specific circuits results in a local increase of the wake theta EEG power in those regions, followed by more intense sleep, as reflected by SWA, over the same areas.
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To investigate the brain topography of the human sleep EEG along the antero-posterior axis, spectra (0.25-25 Hz; 1 Hz bins) were computed from all-night EEG recordings (n = 20 subjects) obtained from an anterior F3-C3) and a posterior (P3-O1) derivation. State-dependent and frequency-dependent topographic differences were observed. In non-rapid eye movement (REM) sleep, power in the anterior derivation was higher than in the posterior derivation in the 2 Hz bin, and lower in the 4-10 Hz bins. In REM sleep, a posterior dominance was present in most bins below 18 Hz. The 2-6 Hz bins exhibited an antero-posterior shift of power over consecutive non-REM sleep episodes. Consistent shifts of power were also present within non-REM sleep episodes. The results suggest that anterior and posterior cortical regions may be differently involved in the sleep process.
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The aim of the study was to investigate the relationship between regional aspects of the children's sleep electroencephalogram (EEG) (high-density EEG recordings) and their intellectual ability. The spectral power in the α, σ, and β frequency ranges of 109 EEG derivations was correlated with the scores of full-scale intelligence quotient, fluid intelligence quotient, and working memory (14 participants, mean age: 10.5±1.0 years; six girls). The previously reported relationship (derivation C3/A2) between spectral band power and intellectual ability could further be refined, particular spatial patterns over central and parietal areas with positive correlations were found. Thus, neurobiological correlates of intelligence during sleep may exhibit brain region-specific patterns.
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This paper discusses the synaptic homeostasis hypothesis of sleep. The main claim of the hypothesis is that plastic processes occurring during wakefulness result in a net increase in synaptic strength in many brain circuits, and that role of sleep is to downscale synaptic strength to a baseline level that is energetically sustainable, makes efficient use of gray matter space, and is beneficial for learning and memory. Thus, sleep is the price we have to pay for plasticity, and its goal is the homeostatic regulation of the total synaptic weight impinging on neurons. In this chapter we review evidence pro and contra the hypothesis, discuss similarities and differences with other hypotheses that focus on the role of sleep in neural plasticity, and mention ongoing and future experiments to test it directly.
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A monograph communicating the current realities and future possibilities of unifying basic studies on anatomy and cellular physiology with investigations of the behavioral and physiological events of waking and sleep. Steriade established the Laboratory of Neurophysiology at Laval U., Quebec; McCarl