Dissociated wake-like and sleep-like electro-cortical activity during sleep

NeuroImage 58 (2011) 612–619

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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g

Dissociated wake-like and sleep-like electro-cortical activity during sleep Lino Nobili a, b, c,⁎, Michele Ferrara d, Fabio Moroni e, f, Luigi De Gennaro e, Giorgio Lo Russo a, Claudio Campus c, Francesco Cardinale a, Fabrizio De Carli c a

Centre of Epilepsy Surgery “C. Munari”, Niguarda Hospital, Milan, Italy Center of Sleep Medicine, Niguarda Hospital, Milan, Italy Institute of Bioimaging and Molecular Physiology, Genoa Unit, National Research Council, Genoa, Italy d Department of Health Sciences, University of L'Aquila, Italy e Department of Psychology, University of Rome “Sapienza”, Roma, Italy f Department of Psychology, University of Bologna, Italy b c

a r t i c l e

i n f o

Article history: Received 5 April 2011 Revised 11 June 2011 Accepted 13 June 2011 Available online 21 June 2011 Keywords: Motor cortex Local sleep Prefrontal cortex Activations Arousal Sleep regulation

a b s t r a c t Sleep is traditionally considered a global process involving the whole brain. However, recent studies have shown that sleep depth is not evenly distributed within the brain. Sleep disorders, such as sleepwalking, also suggest that EEG features of sleep and wakefulness might be simultaneously present in different cerebral regions. In order to probe the coexistence of dissociated (wake-like and sleep-like) electrophysiological behaviors within the sleeping brain, we analyzed intracerebral electroencephalographic activity drawn from sleep recordings of five patients with pharmacoresistant focal epilepsy without sleep disturbances, who underwent pre-surgical intracerebral electroencephalographic investigation. We applied spectral and wavelet transform analysis techniques to electroencephalographic data recorded from scalp and intracerebral electrodes localized within the Motor cortex (Mc) and the dorso-lateral Prefrontal cortex (dlPFc). The Mc showed frequent Local Activations (lasting from 5 to more than 60 s) characterized by an abrupt interruption of the sleep electroencephalographic slow waves pattern and by the appearance of a wake-like electroencephalographic high frequency pattern (alpha and/or beta rhythm). Local activations in the Mc were paralleled by a deepening of sleep in other regions, as expressed by the concomitant increase of slow waves in the dlPFc and scalp electroencephalographic recordings. These results suggest that human sleep can be characterized by the coexistence of wake-like and sleep-like electroencephalographic patterns in different cortical areas, supporting the hypothesis that unusual phenomena, such as NREM parasomnias, could result from an imbalance of these two states. © 2011 Elsevier Inc. All rights reserved.

Introduction Sleep has been traditionally defined in terms of whole-animal behavioral state on the basis of the concept that it is imposed to the whole brain by specialized sleep networks. However, this dominant “top-down” paradigm has been recently challenged by a “bottom-up” approach according to which sleep is a fundamental property of local neuronal networks in different brain structures (Krueger et al., 2008). In this view, sleep is orchestrated, but not fundamentally driven, by central mechanisms (Rector et al., 2005). Global (behaviorally and electroencephalographically defined) sleep emerges when a large number of neuronal groups are in the altered input-output state that characterizes the sleep-like state at the local network level (Krueger et al., 2008).

⁎ Corresponding author at: Centre of Epilepsy Surgery “C. Munari”, Center of Sleep Medicine, Niguarda Hospital, Piazza Ospedale Maggiore, 3, 20162, Milan, Italy. E-mail address: [email protected] (L. Nobili). 1053-8119/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2011.06.032

Several evidences indeed support the idea that sleep intensity is not a spatially global and uniform state. Topographical EEG studies in normal subjects shows that during sleep there are large regional frequency-specific EEG differences (Finelli et al., 2001; Ferrara et al., 2002; Marzano et al., 2010). These differences are stable, as the frontal predominance of slow wave activity (SWA), the main indicator of sleep depth. From a temporal point of view, there is also evidence that the sleep process is not necessarily present simultaneously in the entire brain. The coexistence of wake-like and sleep-like EEG patterns has been long recognized in birds and aquatic mammals, such as in dolphins (Mukhametov, 1984; Lyamin et al., 2008). However, strong support to the temporal acceptation of the local sleep theory comes also from quantitative EEG studies in humans. Indeed, in normal subjects it has been shown that different brain areas can fall asleep with a different timing (De Gennaro et al., 2001). Clinical evidence on patients with parasomnias (status dissociatus), e.g. sleepwalking, suggests that these individuals are awake (evidenced by their ability to negotiate around objects), and asleep (indicated by their lack of awareness of

L. Nobili et al. / NeuroImage 58 (2011) 612–619

their actions) simultaneously (Mahowald and Schenck, 2005). Therefore, according to these observations, vigilance states seem not necessarily temporally discrete states. To date, in humans the observation of the coexistence of dissociated sleep-like and wake-like EEG patterns in well-defined and restricted cortical areas has been provided only in patients with parasomnia (Terzaghi et al., 2009). Therefore, in order to investigate with a higher spatial resolution local (cortical) EEG dynamics during sleep, here we analyzed intracerebral stereo-EEG data drawn from overnight sleep recordings of five epileptic patients without sleep disturbances, who underwent intracerebral EEG acquisition during preoperative investigation. Stereo-EEG recordings allow in part to overcome the low spatial resolution of the human sleep studies with surface EEG which, even with high-density arrays, necessarily record the activity of relatively large groups of neurons. Our analysis focused on NREM sleep, since we were interested in dissociated states showing EEG activation on one region accompanied by clear sleep EEG patterns (e.g., delta waves) on the other derivations. We found that during NREM sleep some parts of the cortex can be electrophysiologically fully activated while others are strongly deactivated. Indeed, we observed local activations (LAs) occurring in the Motor cortex (Mc) that were accompanied by a deepening of sleep in other cortical regions, as expressed by the simultaneous increase of delta waves measured in the dorso-lateral Prefrontal cortex (dlPFc) and at the surface of the scalp. Materials and methods Patients and data recording Intracerebral electroencephalographic (EEG) data were recorded from five patients (3 males, 2 females; mean age 26.8 ± 5.9) with pharmacoresistant focal epilepsy. None reported any clinically evaluated sleep disorder. Patients underwent an intracerebral EEG investigation (Stereo-EEG, SEEG) in order to accurately localize the epileptogenic zone before surgical treatment. EEG activity was recorded from platinum–iridium, semiflexible intracerebral electrodes with a diameter of 0.8 mm, 5–18 contacts 2 mm in length and 1.5 mm intercontact distance. Position and number of electrodes varied according to individual condition: patients included in this study shared the presence of contacts pairs unequivocally localized within the Mc and the dlPFc, as confirmed by postimplantation magnetic resonance imaging (see Fig. 1 and Table 1), and intracerebral electrical stimulation (Cossu et al., 2005). Bipolar EEG from both regions showed no EEG abnormalities. Scalp EEG activity was recorded from two platinum needle electrodes placed during surgery at 10–20 positions Fz and Cz. Electroocular activity was registered at the outer canthi of both eyes, and submental electromiographic activity was acquired with electrodes attached to the chin. All signals were recorded using a polygraphic recording system (XLTEK, Trex™) with a sampling rate of 512 Hz. Before stereotactic electrode implantation, patients gave written informed consent for participation in research studies and for publication of data. Sleep was recorded during the fifth night after electrode implantation and no seizures were observed during the night. The EEG channels here analyzed were free of inter-ictal epileptic discharges and were not involved in the ictal discharge during seizures. During the SEEG recording period, patients continued taking their standard doses of anticonvulsant medications (for details, see Table 1). Acquisition files were transformed to EDF (European Data Format) to be handled by means of a MatLab software (MatLab 7.0, The Matworks Inc., Natick, MA, USA). We used a bipolar montage between adjacent electrode contacts and between Fz–Cz scalp electrodes, EOG and EMG derivations. EEG and SEEG channels were 0.1–40 Hz band pass filtered, EOG channel was 0.16–15 Hz band pass filtered and EMG channel was 5–150 Hz band pass filtered. The same custom software allowed us to score the sleep stages according to the standard criteria

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(Rechtschaffen and Kales, 1968). EEG artifacts were visually selected and removed from the analysis. The overall rejection rate was 9.1% (SE: 2.5%). Local activation scoring We defined the transient electroencephalographic activations appearing only on one of the three considered derivations as Local Activations (LAs). For LAs scoring we considered the standard definition of EEG arousals (American Sleep Disorders Association, 1992) as transient phenomena disrupting sleep, characterized by an abrupt increase in EEG frequency, which may include theta, alpha and/or beta rhythm, and we manually scored them independently in each derivation. Since we were interested in dissociated states during which SEEG and scalp EEG channels could show opposite (wake-like and sleep-like) electrophysiological behaviors, no maximum duration criterion was considered. A local Activation Index (AI) was then calculated as the number of events per hour of NREM sleep. Finally, for each cortical LA we visually scored the presence and duration of concomitant EMG activations. An activation was considered as an increase of muscle tone of at least twice the EMG level in the 20 s epoch preceding the LA. Spectral analysis The time course of SWA across the night was assessed by spectral analysis of EEG signal: the Fast Fourier Transform (Welch method) was applied to 2-second overlapping epochs preprocessed by a Tukey window. A mean spectrum was estimated for 1-minute intervals after artifact rejection. SWA was evaluated for each minute as EEG power within the delta band (0.5–4 Hz). SWA power was normalized within each recording to the overnight geometric mean of the total power. The first three NREM–REM cycles of each sleep recording were then divided into an equal number of intervals (20 steps for each NREM phase and 5 steps for each REM) (Moroni et al., 2007): this enabled between subject averaging in order to estimate a mean SWA profile for each sleep cycle and brain region. In order to compare SWA between regions and cycles, the normalized delta power was averaged for each recording, region and NREM cycle. Time–frequency analysis In order to evaluate EEG patterns characterizing the different derivations in association with Mc LAs, we analyzed the time–frequency distribution of EEG signals within a 40 s time window centered on the Mc LA onset. Time–frequency analysis of bipolar EEG signals was performed by means of the discrete wavelet transform (Jobert et al., 1994; De Carli et al., 1999), particularly suitable for the analysis of nonstationary signals in the time–frequency domain. The discrete wavelet transform was used in this study by means of the recursive application of a pair of half-band mirror filters, generating wavelet-coefficient time series for each band in a multi-resolution scheme. High frequencies were estimated with high time resolution and large bandwidth, while time resolution decreased and frequency resolution increased by halving the bandwidth at each step toward lower frequencies (Rioul and Vetterli, 1991). This process produced a sequence of frequency bands with constant relative bandwidth (bandwidth/central frequency) well suited for EEG analysis. The 8– 16 Hz band was further split into the conventional alpha (8–12 Hz) and sigma (12–16 Hz) EEG bands. Based on this decomposition, signal power was computed and arranged in a grid as function of time (0.125 s resolution) for the following frequency bands: 0.5–1 Hz, 1–2 Hz, 2–4 Hz, 4–8 Hz, 8–12 Hz, 12–16 Hz, 16–32 Hz. In order to get a better frequency discrimination, the wavelet filters were drawn from the application of the Remez exchange algorithm for orthonormal wavelets proposed by

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Fig. 1. Example of a Magnetic Resonance Imaging (MRI) scan showing intracerebral electrodes implanted into the motor cortex (a) and the dorso-lateral prefrontal cortex (b) in a single subject. From the left: axial, coronal and sagittal views. White circles indicate the location of the two electrode contacts on which sleep EEG analysis was performed. Superimposition of the ten bipolar derivations (yellow spheres) of the 5 subjects on the Montreal Neurological Institute (MNI) brain template (c: mesial projection; d: lateral projection).

Rioul and Duhamel (1994) for the optimization of frequency selectivity (32 coefficients). Grids of power data were then averaged for each recording and normalized to the geometric mean of the signal power within the 40-second time window.

Statistics In order to evaluate the dynamics of SWA across the night, Analysis of Variance (ANOVA) with the factors Brain Region (Mc, dlPFc, Scalp) and Cycle (1st, 2nd, 3rd) was carried out on normalized EEG power in the delta band (0.5–4.0 Hz). Local Activation Index (AI) has been compared across Brain Region (Mc, dlPFc, Scalp) by means of a one-way ANOVA.

To analyze the time course of Mc LA across subsequent sleep periods, each sleep cycle was divided into four time intervals. The values of Mc LA index (events/hours) were then submitted to an ANOVA with the factors Cycle (1st, 2nd, 3rd) and Segment (1st, 2nd, 3rd, 4th). Finally, a repeated measure ANOVA with the factors Brain Region (Mc, dlPFc, Scalp), Frequency Band (0.5–1 Hz, 1–2 Hz, 2–4 Hz, 4–8 Hz, 8–12 Hz, 12–16 Hz, 16–32 Hz) and Time Interval (40 consecutive 1sec intervals within the Mc LA-associated time window) was carried out on log-transformed signal power. In order to limit the number of factor levels, time resolution for this analysis was reduced to 1 s. The Huynh and Feldt adjustment was applied to the estimation of significance levels in order to take into account time-dependent deviation from sphericity assumption.

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Table 1 Demographic, MRI findings and clinical information for each patient. Patient

Gender

Age (years)

Medications (mg/day)

MRI findings

Lamotrigine 400 Phenytoin 300 Carbamazepine 800 Levetiracetam 2000 Valproic acid 1200 Topiramate 200 Lamotrigine 400 Topiramate 400

1 2 3

F M F

33 21 32

4

M

19

5

M

36

SEEG Side

Sample lobes

dlPFca

Mca

Epileptogenic zoneb

Unrevealing Unrevealing Parietal–temporal ischemic lesion Unrevealing

L R L

FC FCT FCTP

F3 F3 F1–F2 sulcus

Paracentral lobule Paracentral lobule Precentral gyrus

Anterior cingulate gyrus Anterior cingulate gyrus Superior temporal gyrus

R

FCT

F3

Paracentral lobule

Orbito-basal region

Unrevealing

R

FC

F3

Paracentral lobule

Superior frontal gyrus

C = central; F = frontal; P = parietal; T = temporal. F1: superior frontal gyrus; F2: middle frontal gyrus; F3: inferior frontal gyrus. dlPFc = dorso-lateral prefrontal cortex; Mc = Motor cortex. a Indicates the position of the bipolar SEEG derivations submitted to sleep EEG analysis. b Indicate the site of origin of the seizure.

For all the ANOVAs carried out in the present study, the normality assumption was checked by Lilliefors test, a 2-sided goodness-of-fit test, using the Kolgomorov–Smirnov statistic and suitable for normality testing in small samples (Lilliefors, 1967). The level of significance was set at p b 0.01. Post-hoc comparisons were conducted on confidence intervals (CI) based on ANOVA result and evaluated at 95% level with the application of the Dunn-Sidák adjustment for multiple comparisons. For the post-hoc analysis of time–frequency distribution within the Mc LA-associated time window, the following procedure was adopted: the first 10 s of the time window were considered as background; for each frequency band, the sample of values relevant to each point in time (one value for each recording, 0.125 s resolution) was compared by a two sample t-test with the background values and a probability level was associated to each point in the time–frequency plane; a probability threshold was then set according to the False Discovery Rate (FDR) method (Benjamini and Hochberg, 1995); all points in the time–frequency plane, with a probability level lower than selected threshold were considered significant. ANOVAs, the associated multiple comparisons and Lilliefors test to control for normality of residuals were performed by the Statistics Toolbox of Matlab software (Mathworks Inc, Natick, MA, USA).

In order to quantify the intranight-distribution of LAs, we visually scored all the activations during NREM sleep independently in each derivation, as specified in the Methods. Mean local Activation Index (AI), measured as events/hour of NREM sleep, was 18.7 for Mc, 4.1 for dlPFc and 6.7 for scalp. One-way ANOVA on local AI showed a significant difference for Brain Region (F(2,8) = 77.0, p b 0.0001). Post-hoc comparisons showed that mean AI for dlPFc and scalp do not significantly differ, but they are both significantly lower compared to Mc mean value (see Fig. 4a). Eighty-eight percent of Mc activations were strictly local in nature (being discernible only on this derivation), while LAs account for only 6% of dlPFc activations and 10% of scalp EEG activations. Specifically,

Results Dynamics of slow wave activity and distribution of local activations The dynamics of SWA across the night was analyzed by means of EEG spectral analysis applied to the first three NREM-REM cycles. The presence of SWA during NREM sleep and its progressive, physiological decay across sleep cycles, typically present in the scalp recordings, was also clearly visible both in the dlPFc and in the Mc (see Fig. 2). The Brain Region x Cycle ANOVA showed a significant main effect for Brain Region (F(2,16)= 8.58, p=0.003). Post-hoc comparisons indicated that Mc have lower SWA levels compared to both dlPFc and scalp derivations, that do not differ between them (means±CI: Mc= 0.78±0.11, dlPFc = 1.14 ±0.11, scalp= 1.05±0.11). The main effect for Cycle (F(2,16)= 13.58, p=0.0004) indicated that the first two sleep cycles show higher SWA compared to the third cycle (Means ± CI: 1st = 1.21 ± 0.11, 2nd =1.01 ±0.11, 3rd =0.75±0.11). The Brain Region ×Cycle interaction was not significant (F(4,16) =1.63, p= 0.21). In spite of the similar global dynamics of SWA recognized in both cortical structures, the Mc showed frequent local activations characterized by an abrupt interruption of the sleep EEG slow waves pattern and by the appearance of a wake-like EEG high frequency pattern (including alpha and/or beta rhythm). These LAs, differently from those occurring simultaneously on the three derivations, were paralleled by a deepening of sleep in other cortical regions, as expressed by the concomitant increase of slow waves in the dlPFc and scalp EEG recordings (see Fig. 3).

Fig. 2. Overnight distribution of slow wave activity (SWA) and motor cortex Local Activations (LAs). The vertical gray bars indicate REM sleep periods. Upper graph: SWA, marker of NREM sleep depth, has been calculated as the power in the 0.5–4.0 Hz band of the EEG signal recorded from the motor cortex (Mc) the dorso-lateral prefrontal cortex (dlPFc) and the scalp (Fz-Cz). Signal power has been normalized within each recording to the geometric mean overnight value and averaged between recordings. In order to obtain an average SWA profile of each derivation, the first three NREM–REM cycles of each recording were divided into an equal number of intervals (20 steps for each NREM phase and 5 steps for each REM) and then averaged between patients. The light gray area represents the confidence intervals for the three curves, calculated by assuming a chi-square distribution of the band. The intervals overlap indicating no significant difference at single time-step level. All three curves exhibit the typical cyclic NREM– REM pattern and a progressive decrease from cycle to cycle. Lower graph: Distribution of the mean motor cortex LA index (# of events/hour of NREM sleep). LAs were detected from motor cortex EEG as an abrupt increase of signal frequency interrupting sleep EEG pattern. LA index increases from cycle to cycle and at the end of each cycle. Error bars show the confidence intervals associated to each column (assuming a chi-square distribution of the event count) based on False Discovery Rate probability threshold.

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Fig. 3. Sample patterns of intracerebral EEG. The first three traces show EEG recordings from the motor cortex (Mc), dorso-lateral prefrontal cortex (dlPFc) and scalp (Fz–Cz), and are followed by the electrooculogram (EOG) and electromiogram (EMG, chin). a) Quiet wakefulness (eyes closed): fast rhythms prevail in all the three EEG tracings. b) NREM sleep: slow waves prevail in all the three EEG tracings. c) A local activation (LA, grey shadowed area), characterized by fast EEG activity, appears in the motor cortex derivation and continue for tens of seconds while sleep EEG with slow waves prevails in the other tracings. d) When a short LA appears in the motor cortex, a burst of slow waves characterizes the other two EEG derivations. e) A diffuse short burst of delta waves followed by a complete awakening with fast rhythms in all the three EEG recordings.

we observed that 89.2% of all Mc LAs occurred during Stage 2, 6% during Stage 3 and 4.8% during Stage 4. Mc LAs generally lasted less than 30 s (mean: 15.63, standard error, SE: 1.9); however, such contrasting behavior (fast EEG activities in the Mc and SWA in the dlPFc and scalp recordings) sometimes continued for a substantial period of time (30–120 s; Fig. 4b). The rate of these long lasting (N30 s) Mc LAs was 2.6 events/h of NREM sleep (SE: 0.3) with a cumulative duration of 132.8 s/h of NREM sleep (SE: 41.2 s). DlPFc and scalp EEG did not show long lasting LAs. As far as the evaluation of the coincidence of Mc activations and peripheral muscle activity changes is concerned, we observed that 45 ± 4.3% of all Mc LAs were accompanied by an EMG activation. Considering only these coupled activations, the percentage of time of chin muscle tone increase during the Mc LA was 33 ± 3.2% of the entire LAs duration. The overnight distribution of Mc LAs is depicted in Fig. 2 (lower panel). The Cycle x Segment ANOVA showed a main effect for Cycle (F(2,24) = 8.83, p = 0.0013). The mean LA index (LAs/h) increased across NREM sleep cycles (Means ± CI: 10.5 ± 4.42, 18.3 ± 4.42 and 27.2 ± 4.42). Post-hoc comparisons showed that each cycle was significantly different from each other (see Fig. 4c). The increase from the first to the third cycle was significantly fitted by a linear trend (r 2 = 0.34, p b 0.0001).

The main effect for Segment was also significant (F(3,24) = 6.40, p b 0.0024). Mc LAs increased in the last part of each NREM period (Means ± CI: 13.3 ± 5.65, 15.3 ± 5.65, 15.1 ± 5.65 and 30.9 ± 5.65). Post-hoc comparisons indicated that the last segment differed significantly from the others (Fig. 4d). The Cycle × Segment interaction was not significant (F(6,24) = 0.53, p = 0.78). Time–frequency analysis of local cortical activations For each artifact-free Mc LA, the time–frequency distribution was evaluated by discrete wavelet transform. The mean time–frequency distribution of signal power (after square root transformation) is reported in Fig. 5 for the three derivations. Repeated measure ANOVA did not show significant differences among mean log-transformed power values as function of Region (F(1,13) = 2.68, p = 0.13), Frequency (F(6,78) = 2.00, p = 0.16) and Time (F(39,507)= 2.47, p = 0.08). Significant first order interactions were found for Frequency× Region (F(6,78) = 18.36, p b 0.0001) and Frequency × Time (F(234,3042)= 13.3, p b 0.0001), while the Time× Region interaction was close to significance at the selected threshold (F(39,507) = 3.88, p = 0.018). Moreover, the crucial Frequency × Time × Region interaction was also significant (F(234,3042) = 9.20, p b 0.0001). Therefore, post-hoc

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Fig. 4. Panel a: Means and confidence intervals (CI) of the Activation index (AI) values as a function of brain region. The AI for Motor cortex (Mc) was significantly higher compared to the dorso-lateral prefrontal cortex (dlPFc) and scalp values (Fz–Cz). Panel b: Distribution of Mc LA Index as a function of the activation duration. Panel c: Means and confidence intervals (CI) of the Mc LA index (events/hour) as a function of the sleep cycle. The LA index increased from the first to the third cycle: the first and last cycle were significantly different (their confidence intervals did not overlap) while the second one had an intermediate value. The global increase was fitted by a significant linear trend (p b 0.0001). Panel d: Means and confidence intervals (CI) of the Mc LA index as function of within-cycle segments. The CI for the first 3 segments largely overlapped, indicating that mean values were not significantly different. The last segment of the cycle showed a significantly higher mean LA index.

Fig. 5. Time frequency distribution of EEG patterns recorded in Motor cortex (Mc, upper part), dorso-lateral prefrontal cortex (dlPFc, middle part) and scalp (Fz–Cz, bottom part) in association with motor cortex Local Activations (LAs). A 40-sec time window was set around the start (0 time) of each Mc LA and signal power was calculated by wavelet transform for each time unit within a time frequency grid (0.125 s time resolution and frequency-dependent bandwidth). Time–frequency distribution was averaged within each recording and then among recordings after within-subject normalization. The distribution of averaged signal amplitude (square root of the power) is presented for each brain region by a color scale. The points, in the time–frequency plane, which didn't significantly differ from the background, are marked by the black symbol ‘X’ (ANOVA followed by FDR-corrected post-hoc comparison—see the text for details). The abrupt shift towards higher frequencies (4–32 Hz) in the motor cortex is accompanied by an increase of EEG activity in the low frequencies (0.5–2.0 Hz) in the other regions, in association with the onset of the Mc local activation.

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analysis focused on band power changes as a function of time in each region. In Fig. 5, the points in the time–frequency plane which did not significantly differ from background were marked by the black symbol ‘X’. A significant shift of power from low to higher frequencies associated to LA onset is evident for Mc (top diagram), characterized by a significant reduction of SWA (0.5–4.0 Hz) and by an increase in the upper frequency bands (4.0–32.0 Hz) particularly evident for the alpha band (8.0–12.0 Hz). On the other hand, dlPFc (central diagram) and scalp (bottom diagram) were characterized by a transient and significant increase of SWA (0.5–4.0 Hz) around the Mc LA, actually beginning just before the Mc LA. An increase of low-frequency activities was also detected in Mc slightly preceding the increase of fast activities. Discussion Here we showed, by means of intracerebral stereo-EEG recordings, that human sleep can be characterized by the coexistence of wake-like and sleep-like EEG patterns in different cortical areas, as indicated by the high number of local activations within the motor cortex that were accompanied by deep-sleep EEG patterns in the prefrontal cortex and scalp. In particular, we observed that: i) The physiological and progressive decay of SWA across NREM sleep cycles is comparable between the derivations (dlPFc, Mc, scalp) investigated; ii) In spite of this similarity, Mc shows a higher rate of local activation during NREM sleep compared to dlPFc and scalp, that increases across sleep cycles and within each cycle towards the end of each NREM period, being possibly associated with the decrease of SWA and the approach of REM sleep; iii) Mc LAs generally have a duration of 5–10 s, but they could sometimes last up to 120 s; iv) More than 50% of all Mc LAs were not accompanied by an EMG activation and only one third of the Mc LA cumulative duration is accompanied by an increase of chin muscle tone; v) The time–frequency analysis of the EEG around each local activation indicates that Mc LAs were characterized by an increase of high frequency EEG activity of Mc at the onset of the LA, paralleled by the increase of low-frequency EEG activities (peaking at 0.5–2 Hz) at dlPFc and scalp sites. A number of scalp EEG studies (Finelli et al., 2001; Ferrara et al., 2002; Marzano et al., 2010) have already highlighted the presence of regional differences in the topographic distribution of slow waves; our results go beyond these findings, indicating for the first time in humans, by means of intracerebral recordings, that the sleep process is not necessarily present simultaneously in the entire brain. The appearance of localized simultaneous sleep-like and wake-like EEG activity during NREM sleep can explain poorly understood sleep events, such as sleepwalking and confusional arousal. In particular, during such clinical phenomena, considered from the first pioneering description as arousal disorders (Broughton, 1968), the coexistence of activated and deactivated brain regions have been shown by Single Photon Emission Computed Tomography (Bassetti et al., 2000) and intracerebral electrophysiological studies (Terzaghi et al., 2009). Our results demonstrate that the occurrence of local dissociated states is actually an intrinsic feature of physiological NREM sleep, thus supporting the hypothesis that sleep and wakefulness could be not mutually exclusive and that unusual phenomena might result from an imbalance of these two states (Mahowald and Schenck, 2005). Genetic factors or external triggers such as sleep deprivation (Zadra et al., 2008), inducing a modification of the arousal threshold, may favor the persistence of local dissociated activity, and this may cause the appearance of motor phenomena such as those observed in NREM parasomnias. Moreover, if local awakenings can appear during sleep, we cannot exclude that such a dissociation may also occur during wakefulness (in this case with cortical areas showing local sleep features) and this could explain other phenomena such as sleep inertia (Ferrara et al., 2006; Marzano et al., 2011), that subjective

feeling of grogginess accompanied by decreased levels of performance which typically follows awakening. The occurrence of LAs is consistent with experimental studies showing that sleep and wakefulness can be restricted to small groups of neurons (Pigarev et al., 1997), individual cortical columns (Rector et al., 2005) or to larger brain regions, as in some birds and marine mammals that in order to continue flying, swimming or scanning the surrounding environment can simultaneously exhibit sleep in one cerebral hemisphere and wakefulness in the other one (Mukhametov, 1984; Lyamin et al., 2008). Moreover, our data are in accordance with recent studies which, applying experimental manipulations of sensory and learning systems before sleep, provided evidence of a local regulation of sleep (Vyazovskiy et al., 2000; Cantero et al., 2002; Huber et al., 2004; Huber et al., 2006; Nelini et al., 2010). More generally, the coexistence of sleep-like and wake-like patterns is in agreement with the predictions of the neuronal group theory of sleep function (Krueger et al., 1999, 2008), which posits that sleep is local in nature, being a fundamental property of small neuronal groups. In an evolutionary perspective, we can speculate that a lower arousal threshold and a higher level of activation in the motor cortex during the least activated physiological state (NREM sleep) may have been selected, because they increase the probability of survival, facilitating motor behaviors in case of sudden awakenings. In the same vein, the significant enhancement of slow frequencies in the dlPFc immediately before and during the Mc LAs could be interpreted as a behavior that allows the global sleep process to easily proceed, even when a local activation appears. This finding also suggests that sleep (and sleep intensity) is not a spatially global state. In fact, a form of local homeostasis seems to occur, characterized by the enhancement of delta activity in one cortical area to compensate for a decrease of slow activity in another area. Our results may also shed light on the interpretation of certain sleep EEG features, such as K complexes and transient bursts of delta waves, which have alternatively been considered a hallmark of deep sleep (Amzica and Steriade, 1997; De Gennaro et al., 2000) or as arousal reactions (Terzano et al., 1990; Wauquier et al., 1995; De Carli et al., 2004; Halász et al., 2004). In particular, since transient K complexes and bursts of delta waves can be elicited by external or internal stimuli (Terzano et al., 1990; Nobili et al., 2006; Terzaghi et al., 2008), they have been interpreted as a kind of ‘anti-arousal’ response (Wauquier et al., 1995; Halász et al., 2004). However, the transient increase of delta band in the dlPFc observed in our study seems not to represent an anti-arousal response to the Mc LA. In fact, the increase of delta power is present and precedes the motor cortex activation (see Fig. 5). Furthermore, cross-correlations between prefrontal delta activity and alpha activity over Mc (data not shown) indicate that the phasic increase of prefrontal delta activity is significantly related to the increase in motor cortex activation (alpha activity, 8.0–12.0 Hz), and precedes this activation with a mean lag of −1.5 s. Nevertheless, these phenomena could be interpreted as indicating that, during NREM sleep, different cortical areas respond to activation (due to internal or external stimuli) with different electrophysiological features and timing. Further studies including the analysis of more cortical regions and the assessment of vegetative functions, which have been shown to be linked to reactive slow waves over the frontal regions (Church et al., 1978; Ferini-Strambi et al., 2000) might be helpful to evaluate possible hierarchical regulations of local activations. Although our findings derive from SEEG investigations in epileptic patients, we are confident that they can be extended to the general population, as we investigated patients without any sleep complaint. Moreover, the EEG channels analyzed here were free of inter-ictal epileptic discharges and were not involved in the ictal discharge during seizures. Therefore, we can reasonably rule out a possible epileptic origin of the described local activations.

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In conclusion, our data show that the boundaries between sleep and wakefulness are less clearly defined than expected and support the new, local interpretation of the electrophysiology of sleep and vigilance. Although these states have been usually considered as whole-brain phenomena, our findings suggest that their electroencephalographic features can coexist in different brain areas. In the future, it would be interesting to specifically assess the relations between motor cortex activations and muscular activity recorded from the muscular districts directly corresponding to the cortical regions explored by the intracerebral electrodes. Moreover, it would be fascinating to evaluate whether motor cortex EEG activations are part of normal dreaming in NREM sleep, associated with fictive movement mentation. Acknowledgments This work has been in part supported by grants from Compagnia di San Paolo, Programma Neuroscienze 2008/09 (3896 SD/sd, 2008.2130), the University of L'Aquila (Ricerche di Ateneo ex 60%) and MIUR, Italy, (PRIN: n. 2007BNRWLP-002) to Michele Ferrara, and by the ESRS SanofiAventis Research Grant 2008-10 to Fabio Moroni. References American Sleep Disorders Association (ASDA), 1992. EEG arousals: scoring rules and examples: a preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorders Association. Sleep 15, 173–184. Amzica, F., Steriade, M., 1997. The K-complex: its slow (b 1-Hz) rhythmicity and relation to delta waves. Neurology 49, 952–959. Bassetti, C., Vella, S., Donati, F., Wielepp, P., Weder, B., 2000. SPECT during sleepwalking. Lancet 356, 484–485. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300. Broughton, R.J., 1968. Sleep disorders: disorders of arousal? Science 159, 1070–1078. Cantero, J.L., Atienza, M., Salas, R.M., Dominguez-Marin, E., 2002. Effects of prolonged waking-auditory stimulation on electroencephalogram synchronization and cortical coherence during subsequent slow-wave sleep. J. Neurosci. 22, 4702–4708. Church, M.W., Johnson, L.C., Seales, D.M., 1978. Evoked K-complexes and cardiovascular responses to spindle-synchronous and spindle-asynchronous stimulus clicks during NREM sleep. Electroencephalogr. Clin. Neurophysiol. 45, 443–453. Cossu, M., Cardinale, F., Castana, L., Citterio, A., Francione, S., Tassi, L., Benabid, A.L., Lo Russo, G., 2005. Stereoelectroencephalography in the presurgical evaluation of focal epilepsy: a retrospective analysis of 215 procedures. Neurosurgery 57, 706–718. De Carli, F., Nobili, L., Gelcich, P., Ferrillo, F., 1999. A method for the automatic detection of arousals during sleep. Sleep 22, 561–572. De Carli, F., Nobili, L., Beelke, M., Watanabe, T., Smerieri, A., Parrino, L., Terzano, M.G., Ferrillo, F., 2004. Quantitative analysis of sleep EEG microstructure in the time– frequency domain. Brain Res. Bull. 63, 399–405. De Gennaro, L., Ferrara, M., Bertini, M., 2000. The spontaneous K-complex during stage 2 sleep: is it the "forerunner" of delta waves? Neurosci. Lett. 291, 41–43. De Gennaro, L., Ferrara, M., Curcio, G., Cristiani, R., 2001. Antero-posterior EEG changes during the wakefulness-sleep transition. Clin. Neurophysiol. 112, 1901–1911. Ferini-Strambi, L., Bianchi, A., Zucconi, M., Oldani, A., Castronovo, C., Smirne, S., 2000. The impact of cyclic alternating pattern on heart rate variability during sleep in healthy young adults. Clin. Neurophysiol. 111, 99–101. Ferrara, M., De Gennaro, L., Curcio, G., Cristiani, R., Corvasce, C., Bertini, M., 2002. Regional differences of the human sleep electroencephalogram in response to selective slow-wave sleep deprivation. Cereb. Cortex 12, 737–748.

619

Ferrara, M., Curcio, G., Fratello, F., Moroni, F., Marzano, C., Pellicciari, M.C., De Gennaro, L., 2006. The electroencephalographic substratum of the awakening. Behav. Brain Res. 167, 237–244. Finelli, L.A., Borbely, A.A., Achermann, P., 2001. Functional topography of the human nonREM sleep electroencephalogram. Eur. J. Neurosci. 13, 2282–2290. Halász, P., Terzano, M., Parrino, L., Bódizs, R., 2004. The nature of arousal in sleep. J. Sleep Res. 13, 1–23. Huber, R., Ghilardi, M.F., Massimini, M., Tononi, G., 2004. Local sleep and learning. Nature 430, 78–81. Huber, R., Ghilardi, M.F., Massimini, M., Ferrarelli, F., Riedner, B.A., Peterson, M.J., Tononi, G., 2006. Arm immobilization causes cortical plastic changes and locally decreases sleep slow wave activity. Nat. Neurosci. 9, 1169–1176. Jobert, M., Tismer, C., Poiseau, E., Schulz, H., 1994. Wavelets—a new tool in sleep biosignal analysis. J. Sleep Res. 3, 223–232. Krueger, J.M., Obal, F., Fang, J., 1999. Why we sleep: a theoretical view of sleep function. Sleep Med. Rev. 3, 119–129. Krueger, J.M., Rector, D.M., Roy, S., van Dongen, H.P., Belenky, G., Panksepp, J., 2008. Sleep as a fundamental property of neuronal assemblies. Nat. Rev. Neurosci. 9, 910–919. Lilliefors, H.W., 1967. On the Kolmogorov–Smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc. 62, 399–402. Lyamin, O.I., Manger, P.R., Ridgway, S.H., Mukhametov, L.M., Siegel, J.M., 2008. Cetacean sleep: an unusual form of mammalian sleep. Neurosci. Biobehav. Rev. 32, 1451–1484. Mahowald, M.W., Schenck, C.H., 2005. Insights from studying human sleep disorders. Nature 437, 1279–1285. Marzano, C., Ferrara, M., Curcio, G., De Gennaro, L., 2010. The effects of sleep deprivation in humans: topographical electroencephalogram changes in non-rapid eye movement (NREM) sleep versus REM sleep. J. Sleep Res. 19, 260–268. Marzano, C., Ferrara, M., Moroni, F., De Gennaro, L., 2011. Electroencephalographic sleep inertia of the awakening brain. Neuroscience 10, 308–317. Moroni, F., Nobili, L., Curcio, G., De Carli, F., Fratello, F., Marzano, C., De Gennaro, L., Ferrillo, F., Cossu, M., Francione, S., Lo Russo, G., Bertini, M., Ferrara, M., 2007. Sleep in the human hippocampus: a stereo-EEG study. PLoS ONE 2, e867. Mukhametov, L.M., 1984. Sleep in marine mammals. Exp. Brain Res. 8, 227–238. Nelini, C., Bobbo, D., Mascetti, G.G., 2010. Local sleep: a spatial learning task enhances sleep in the right hemisphere of domestic chicks (Gallus gallus). Exp. Brain Res. 205, 195–204. Nobili, L., Sartori, I., Terzaghi, M., Stefano, F., Mai, R., Tassi, L., Parrino, L., Cossu, M., Lo, Russo G., 2006. Relationship of epileptic discharges to arousal instability and periodic leg movements in a case of nocturnal frontal lobe epilepsy: a stereo-EEG study. Sleep 29, 701–704. Pigarev, I.N., Nothdurft, H.C., Kastner, S., 1997. Evidence for asynchronous development of sleep in cortical areas. NeuroReport 8, 2557–2560. Rechtschaffen, A., Kales, A., 1968. A manual of standardized terminology. Techniques and Scoring System for Sleep Stages of Human Subjects. Public Health Service, Washington DC. NIH Publication No. 204, U S Government Printing Office. Rector, D.M., Topchiy, I.A., Carter, K.M., Rojas, M.J., 2005. Local functional state differences between rat cortical columns. Brain Res. 1047, 45–55. Rioul, O., Duhamel, P., 1994. A Remez exchange algorithm for orthonormal wavelets. IEEE Trans. Circuits Syst. 41, 550–560. Rioul, O., Vetterli, M., 1991. Wavelets and signal processing. IEEE Signal Process. 8, 14–38. Terzaghi, M., Sartori, I., Mai, R., Tassi, L., Francione, S., Cardinale, F., Castana, L., Cossu, M., LoRusso, G., Manni, R., Nobili, L., 2008. Coupling of minor motor events and epileptiform discharges with arousal fluctuations in NFLE. Epilepsia 49, 670–676. Terzaghi, M., Sartori, I., Tassi, L., Didato, G., Rustioni, V., Lo Russo, G., Manni, R., Nobili, L., 2009. Evidence of dissociated arousal states during NREM parasomnia from an intracerebral neurophysiological study. Sleep 32, 409–412. Terzano, M.G., Parrino, L., Fioriti, G., Orofiamma, B., Depoortere, H., 1990. Modifications of sleep structure induced by increasing levels of acoustic perturbation in normal subjects. Electroencephalogr. Clin. Neurophysiol. 76, 29–38. Vyazovskiy, V., Borbély, A.A., Tobler, I., 2000. Unilateral vibrissae stimulation during waking induces interhemispheric EEG asymmetry during subsequent sleep in the rat. J. Sleep Res. 9, 367–371. Wauquier, A., Aloe, L., Declerck, A., 1995. K-complexes: are they signs of arousal or sleep protective? J. Sleep Res. 4, 138–143. Zadra, A., Pilon, M., Montplaisir, J., 2008. Polysomnographic diagnosis of sleepwalking: effects of sleep deprivation. Ann. Neurol. 63, 513–519.