# Enhancement of Neocortical-Medial Temporal EEG Correlations during Non-REM Sleep

Authors: Nikolai Axmacher; Christoph Helmstaedter; Christian E. Elger; Juergen Fell
Journal: Neural Plasticity
 (2008)

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## Abstract

Interregional interactions of oscillatory activity are crucial for the integrated processing of multiple brain regions. However, while the EEG in virtually all brain structures passes through substantial modifications during sleep, it is still an open question whether interactions between neocortical and medial temporal EEG oscillations also depend on the state of alertness. Several previous studies in animals and humans suggest that hippocampal-neocortical interactions crucially depend on the state of alertness (i.e., waking state or sleep). Here, we analyzed scalp and intracranial EEG recordings during sleep and waking state in epilepsy patients undergoing presurgical evaluation. We found that the amplitudes of oscillations within the medial temporal lobe and the neocortex were more closely correlated during sleep, in particular during non-REM sleep, than during waking state. Possibly, the encoding of novel sensory inputs, which mainly occurs during waking state, requires that medial temporal dynamics are rather independent from neocortical dynamics, while the consolidation of memories during sleep may demand closer interactions between MTL and neocortex.


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## Body

## 1. Introduction

Memory consolidation has been suggested to
occur in two subsequent steps: while initial encoding depends on the integrity
of the medial temporal lobe (MTL) (e.g., [1, 2]) and is linked to the formation
of transient assemblies via fast synaptic plasticity in the entorhinal cortex
and hippocampus, subsequent consolidation requires the transfer of information
to the neocortex, where more permanent networks are built [3–6]. During both
waking state and sleep, communication of the hippocampus with the neocortex
mainly proceeds via polymodal regions within the rhinal cortex [7]. The rhinal
cortex receives rich input from modality-specific regions in higher-order
sensory areas which are located in the inferior temporal neocortex. The
inferior temporal cortex is the final processing stage of the ventral visual
stream and comprises object-specific regions such as the fusiform face area [8, 9]. Recently, Klopp and colleagues [10] used intracranial EEG to show that
activity within this area was coherent with activity in widespread brain
regions selectively during face processing. Furthermore, fusiform and rhinal cortices are
synchronized during memory retrieval [11]. These data indicate that during
waking state, interactions between sensory and medial temporal regions are
required.In an influential model, Buzsáki [3]
hypothesized that during waking state, and particularly during exploratory
phases, information is transferred into the hippocampus and induces rapid
though transient forms of synaptic plasticity (see also [12]). Physiologically,
strong cholinergic inputs during waking state inhibit feedback excitation in
the CA3 region of the hippocampus and induce θ (4–8 Hz) and γ
(20–44 Hz)
oscillations; during sleep, a reduced level of acetylcholine leads to
disinhibition of hippocampal pyramidal cells, which consequently engage in
highly synchronized population bursts [4]. These bursts have been linked to
replay of previously acquired information and transfer into the neocortex,
where more stable representations are being built [5]. Taken together, these
studies suggest a bidirectional information flow between MTL and neocortex,
with transmission from the neocortex into the hippocampus during exploration
and from the hippocampus into the neocortex during rest and sleep.The close connection between memory
consolidation and sleep [13] suggests that interactions of neocortical and
medial temporal EEG activity also undergo circadian fluctuations. However,
there are very few data on the interaction between neocortical and medial
temporal EEG oscillations during waking state and sleep in humans, partly due
to the difficulty of obtaining EEG recordings from the human MTL. Neocortical as
well as MTL θ and γ oscillations were suggested to underlie declarative
memory encoding and retrieval (e.g., [12, 14–16].
On the other hand, neocortical slow wave activity (i.e., <4 Hz activity, which includes both δ
activity between 1 and 4 Hz and slow oscillations <1 Hz) as
well as sleep spindles in the β (12–20 Hz) range, was shown to be important for the consolidation of previously
acquired declarative memories during sleep (e.g., [17, 18]). It is unknown,
however, whether there are state specific correlations between the amplitudes
of neocortical and medial temporal EEG oscillations. To investigate this question, we analyzed scalp
and intracranial EEG recordings in patients with pharmacoresistant focal
epilepsy undergoing presurgical evaluation for exact localization of the
seizure onset zone.


## 2. Materials and Methods

During presurgical evaluation, polysomnography and intracranial EEG were
recorded from ten patients (six women; mean age40.1±22.6 years) with
pharmacoresistant unilateral temporal lobe epilepsy. Mean duration of epilepsy
was 21.4±11.3 years. Scalp EEG was recorded from positions Cz, C3, C4, and O1
(10–20 system). Electro-ocular activity was registered at the outer canthi of
both eyes, and submental electromyographic activity was acquired with
electrodes attached to the skin. Scalp as well as depth electroencephalograms was
referenced to linked mastoids, bandpass filtered (0.01 Hz (6 dB/octave) to 70 Hz
(12 dB/octave)), and recorded with a sampling rate of 200 Hz.Multicontact depth electrodes were implanted
stereotactically along the longitudinal axis of each MTL [19]. The placement of
electrode contacts within the hippocampus and the anterior parahippocampal
gyrus, which is covered by the rhinal cortex, was ascertained by magnetic
resonance imaging in each patient. For each
patient, one contact within the rhinal cortex, one within the anterior
part (anterior third), and one within the posterior part of the hippocampus
(posterior third) were selected. Only invasive EEG recordings of the MTL
contralateral to the zone of seizure origin were analyzed. These data were
compared with the central electrode of scalp EEG (C3/C4) ipsilateral to the
nonepileptic MTL.Visual sleep stage scoring was carried out for
each 20-second epoch according to Rechtschaffen/Kales criteria [20] by two
experts. Subsequently, epochs were divided into the following categories:
waking state, REM sleep, and non-REM sleep. All EEG epochs were visually
inspected for movement artifacts and epileptiform activity. Epochs containing
artifacts were discarded irrespective of the duration of artifacts.
Furthermore, all epochs with power values above 50 μV2 in the upper γ band (36–44 Hz)
were discarded, to avoid high-frequency contamination, which may survive
visual artifact rejection. In total, 53.0% of all EEG epochs were excluded from
further analysis (45.1% based on step one, 7.9% based on step two).Power spectra of all artifact-free epochs were
calculated for each 20 seconds epoch. To increase statistical reliability of
power estimates, we partitioned each 20 seconds EEG epoch into 16
nonoverlapping subsegments of 1.25 seconds duration. We used a fast Fourier
transform (cosine windowing), and the frequency range was divided up into the
following bands:δ (1–4 Hz), θ (4–8 Hz), α (8–12 Hz),
β1 (12–16 Hz), β2 (16–20 Hz),
γ1 (20–28 Hz), γ2 (28–36 Hz),
and γ3 (36–44 Hz). Pearson's correlations between
power values for scalp EEG (C3/C4) and all three locations of the medial
temporal depth electrodes were calculated. Correlation values were Fisher
z-transformed, and group differences against zero were evaluated with
two-tailed t-tests.


## 3. Results

Figure1 presents raw data from one exemplary
subject. Visually, there appeared to be an increased correlation of δ band
activity during non-REM sleep. To quantify the effect of different sleep stages
on interactions of EEG dynamics within neocortex and MTL, we performed a
three-way ANOVA with “locus” (C3/4 compared to rhinal cortex, anterior
hippocampus, or posterior hippocampus) and “sleep stage” (waking state, REM-sleep,
and non-REM sleep) as repeated measures and “frequency band” as independent
variable. Figure 2 depicts average values of Fisher z-transformed correlation
values for the different frequency bands during waking state, REM-sleep, and non-REM
sleep. We observed a highly significant effect of “sleep stage” (F2,144= 6.49; P= 0.002) and a near significant effect of “frequency band” (F7,72= 2.13; P= 0.051), but no effect of “locus” and no interaction. This
result indicates that interactions of EEG dynamics within neocortex and MTL
depended significantly on sleep stage. Neocortical and medial temporal EEG
oscillations were more closely correlated during sleep than during waking
state.Time course ofγ2 and δ band activity in scalp EEG and in the anterior hippocampus and sleep
stages during one night in one exemplary subject. S1: sleep stage 1, S2: sleep
stage 2, SWS: slow wave sleep.
(a)(b)(c)(d)(e)Pearson's correlation coefficient (Fisherz-transformed) between power
densities in scalp EEG (C3/C4) versus medial temporal locations; averages
across subjects are depicted. Both in the δ and in higher (α and γ2)
frequency range, correlation values were maximal during NREM sleep.
(a)(b)(c)Although we did not find a significant “sleep
stage”× “frequency band” interaction, we were
interested in frequency-specific effects and thus conducted two-way ANOVAs with
“locus” and “sleep stage” (waking state, REM sleep, and NREM sleep) as repeated
measures separately for the different frequency bands. We found a significant
effect of “sleep stage” (F2,18= 6.00; P= 0.010) and a trend
for a “sleep stage” × “locus”
interaction (F4,36= 2.14; P= 0.096) in the δ range, but
not in any other frequency bands. To identify differences between pairs of
sleep stages in the different frequency bands, we conducted two-way ANOVAs with
“locus” and “sleep stage” (either waking state and REM sleep; or waking state
and NREM sleep; or REM sleep and NREM sleep) as repeated measures separately
for the different frequency bands. In the δ band, we found a significant
effect of “sleep stage” for the comparison of waking state and NREM sleep (F1,9= 7.11; P= 0.026). While there was no significant difference between
waking state and REM sleep, the comparison of REM sleep and NREM sleep also
revealed a significant effect of “sleep stage” (F1,9= 7.95; P= 0.020) and a significant “sleep stage” × “locus”
interaction (F2,18= 3.99; P= 0.037). We thus calculated separate
one-way ANOVAs for the different pairs of electrodes. We found a significant
difference between NREM and REM sleep at the posterior hippocampus-Cz pair (F1,9= 16.76; P=
0.0027), and trends for the anterior hippocampus-Cz pair (F1,9= 4.27; P=
0.0687) and the rhinal-Cz pair (F1,9= 3.56; P=
0.091).Besides these effects in theδ frequency
range, we also conducted separate two-way ANOVAs with “locus” and pairs of
“sleep stage” as repeated measure in the other frequency bands. We found a
trend for a difference between NREM and REM sleep both in the α (F1,9= 3.83;
P= 0.0819) and in the γ2 range (F1,9= 3.39;
P= 0.0987).It might be argued that the effect of sleep
stage on power correlations is contaminated by differences of power in the
different sleep stages. Power values depend on sleep stage, and thus in stages
with low EEG power in a given frequency band, the noise may be too large to
detect the correlation. In other words, the increased correlation in theδ
band during NREM sleep as compared to waking state might be related to the fact
that δ power is maximal during NREM sleep. However, it is unlikely that the
effect of sleep stage on correlation observed in our data depends on power
values for the following reasons. In the three-way ANOVA with “sleep stage” and
“locus” as repeated measures and “frequency band” as independent variable
reported above, we observed a main effect of “sleep stage” but no “frequency
band” × “sleep
stage” interaction, indicating that the effect of sleep stage did not depend on
frequency band. Indeed, we did not only observe increased correlations during
NREM sleep as compared to waking state in the δ range, but also trends for
increased correlations in the α and γ2 range, which have a
lower power during NREM sleep than waking state. The effects of sleep stage on
power values are usually substantially different. δ and θ power
increase during NREM sleep as compared to waking state, whereas power in higher
frequency ranges decreases. This typical relationship occurred in our data as
well (see Figure 3). A three-way ANOVA of power values with “sleep stage” and
“locus” as repeated measures and “frequency band” as independent variable
revealed a significant “sleep stage” × “frequency band” interaction (F14,144= 9.771; P<10−10; ε=0.707), indicating that sleep had different
effects on power in the different frequency bands.Power values (mean and
standard deviation across epochs) in scalp EEG and medial temporal locations.
Please note that to improve visibility of effect of sleep stage, ordinate scaling
differs for low- (δ to β1) and high- (β2 to
γ3) frequency bands. In contrast to the effect of sleep on power
correlations (see Figure 2), power values in the high-frequency band were
maximal during waking state.
(a)(b)(c)(d)To directly assess whether the effect of “sleep
stage” on the correlation of power values depends on differences in power, we
calculated the correlation of (Fisherz-transformed) correlation values with
power across regions and frequency bands. None of these correlations reached
significance (Pearson's correlation values were <0.2 in each test,
corresponding to P values >0.6).


## 4. Discussion

Our findings indicate that oscillations within
the MTL and the neocortex are more closely correlated during sleep, in
particular during non-REM sleep, than during waking state. This is consistent
with the hypothesis that encoding of novel inputs into long-term memory,  which occurs mainly during waking state,
requires that medial temporal EEG dynamics are rather independent from
neocortical dynamics [3], with the exception of interactions in the θ and
γ range (e.g., [12, 14–16]. On the other hand, the consolidation of
declarative memories during sleep may demand closer correlation of neocortical
and medial temporal EEG dynamics [3], not only in the γ range, but also
with respect to δ and β (spindle) oscillations (e.g.,
[17, 18]). Interestingly, correlations in the spindle frequency range only
reached significance during slow-wave sleep, but not during the entire period
of non-REM sleep. This might suggest that neocortical-medial temporal
interactions in this frequency range are less prominent in sleep stage 2, which
is most commonly associated with sleep spindles, than in deeper sleep stages.Even though we only analyzed data from the
hemisphere contralateral to the seizure onset zone, a relatively large number
of epochs (53%) contained at least one epileptiform event or a movement artifact.
All EEG epochs were inspected twice for movement artifacts and epileptiform
activity. Artifact segments were discarded irrespective of artifact duration;
for example, if a single spike or movement artifact occurred during a 20-second
epoch, the entire epoch was discarded because artifacts might otherwise
spuriously contribute to power estimates. As a result, the number of discarded
epochs was relatively large; however, because we analyzed EEG during entire
nights, the remaining data set was still extensive (mean ± std.:178.3±117.3 minutes per night).Activity in the frequency range between 0.5 Hz
and 1 Hz, that is, below theδ frequency range, has been termed “slow
activity” (SA) and is probably due to different mechanisms than δ activity
[21]. In principle, it would have been interesting to analyze correlation of SA
between the neocortex and the MTL as well. However, the subsegments of 1.25 seconds
durations which were used to analyze power values (see Methods) contain only a
single cycle, or even less, of 0.5–1 Hz activity, which would have resulted in
imprecise estimations of power values. We thus decided to omit this frequency
range.Previous
studies on correlation of activity between hippocampus and neocortex found that
hippocampalθ oscillations occurred in brief bursts and were most abundant
during REM sleep, where they were independent of neocortical θ band
activity [22]. While they were virtually absent during SWS, there were also
longer θ bursts during transition from REM sleep to waking state, which
occurred simultaneously with neocortical θ activity. These results are
somewhat different from our findings that coupling was most pronounced during
NREM sleep. However, it should be noted that different measures were used.
While we calculated correlations of power values across 20 seconds epochs,
Cantero and colleagues [22] analyzed partial directed coherence. In another
study, Cantero et al. [23] reported a decrease of cortico-hippocampal coherence
during sleep in a γ band (35–58 Hz) corresponding roughly to (but exceeding)
our γ3 band (36–44 Hz). In our
study, the average correlation in the γ3 range between Cz and
the posterior hippocampus was also higher during waking state than during NREM
sleep (see Figure 2), although this difference did not reach significance and
was opposite between Cz and the other MTL locations. Further research is
necessary to explain these divergent findings.In particular, it would be interesting to
investigate cortico-hippocampal coupling during replay of previously acquired
information similar to results from animal studies. In rats, place cells in the
hippocampus represent spatial positions by their firing rate [24]. Various studies found that during sleep
periods following exploration of new environments (and thus following
development of new place representations; [25]), these activity sequences are
being replayed [26, 27]. Most importantly, such replay has been observed
not only in the hippocampus, but also simultaneously in the neocortex as well
[28]. In humans, stimulus-specific activity has only been observed in the
hippocampus [29], but not in the neocortex. It is unknown, however, if this
activity is replayed during consecutive sleep periods. Such relative
preservation of the mechanisms underlying memory consolidation across species
might be suggested by studies showing that medial temporal high-frequency
bursts (“ripples”), which appear to correspond to condensed information replay
[12], are coupled to neocortical sleep spindles both in rats [30–32] and humans
[33].In our analyses, we assessed functional connectivity by measuring the correlation
of power values averaged across episodes of 20 seconds. While this approach
lacks temporal resolution, it allows to clearly assign correlation values to
specific sleep stages. Of course,
correlations may depend on the investigated timescale. In principle, an
evaluation of correlations at shorter time scales might allow for more
mechanistic interpretations. Here, we intended to analyze state-related
correlations across 20 seconds epochs defined by Rechtschaffen and Kales
criteria [20], because classification of sleep stages on a smaller time scale
has not been validated. Previous bivariate measures of
intracranial EEG data utilized either power correlation [34, 35] or phase
synchronization [14, 36]. The latter approach is particularly well suited to
study interactions of intracranial EEG with a high temporal resolution. However, phase synchronization between scalp
and intracranial EEG is problematic because of the different properties of
scalp and intracranial EEG, and because scalp activity is transferred through
structures with strong low-pass filtering properties such as bone and skin
[37], which may lead to phase distortions. In contrast, the reported analysis
of power correlation is probably less hampered by these difficulties.
Further recordings in patients with both medial temporal depth electrodes and
subdural electrodes are required to calculate phase synchronization during different
sleep stages.Taken together, our findings support the idea
that medial temporal and neocortical dynamics are more integrated during sleep,
in particular NREM sleep, than during waking state [38].



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