NeuroImage 59 (2012) 1491–1500

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Grouping of MEG gamma oscillations by EEG sleep spindles

Amr Ayoub a,b, Matthias Mölle a,c, Hubert Preissl c, Jan Born a,c,d,⁎a Department of Neuroendocrinology, University of Lübeck, Haus 50, 2. OG, Ratzeburger Allee 160, 23538 Lübeck, Germanyb Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germanyc MEG Center, University of Tübingen, Otfried Müller Strasse 47, 72076 Tübingen, Germanyd Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Gartenstrasse 29, 72074 Tübingen, Germany

⁎ Corresponding author at: Institute ofMedical PsycholoUniversity of Tübingen, Gartenstrasse 29, 72074Tübingen, G

E-mail address: jan.born@uni-tuebingen.de (J. Born)

1053-8119/$ – see front matter © 2011 Published by Eldoi:10.1016/j.neuroimage.2011.08.023

a b s t r a c t

a r t i c l e i n f o

Article history:Received 14 June 2011Revised 8 August 2011Accepted 10 August 2011Available online 27 August 2011

Keywords:Neocortical networksThalamusMemory processingPhase-coupling of rhythmsHuman

Studies have revealed an association between EEG sleep spindles and processing ofmemories during sleep. Herewe investigated whether there is a temporal relation between sleep spindles and MEG oscillatory activity in thegamma frequency band (N30 Hz) which is considered to reflect local cortical processing of memory representa-tions. MEG and simultaneous EEG (at Cz) were obtained in subjects during sleep together with standard poly-somnography. As expected EEG spindles were correlated with power increases in MEG spindle (12.5–15.5 Hz)powermainly over prefrontal and occipital cortical areas. During EEG spindles we revealed both transient signif-icant increases and decreases in MEG power, with decreases occurring significantly more often than increases.Themodulations in gammapower occurredmainly at sites of increasedMEG spindle power, andmore often dur-ing peaks than troughs within the EEG spindle cycle. Cross-frequency coherence analyses confirmed a strongphase-coupling of gamma band activity with the spindle rhythm. The findings are consistent with the ideathat spindles provide a fine-tuned temporal frame for integrated cortical memory processing during sleep.

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Introduction

Classical sleep spindles represent rhythmic bursts of electroenceph-alographic (EEG) activity oscillating at frequencies between about 12.5–15.5 Hz. In human sleep EEG, spindles are most readily detected duringNon-Rapid eye movement (NonREM) sleep stage 2 as discrete spindleevents with waxing andwaning flanks of oscillatory activity and a dura-tion of about 1 s (DeGennaro and Ferrara, 2003). However, spindle ac-tivity occurs also during slow wave sleep (SWS) where it forms lesswell-recognizable discrete events (Marshall et al., 2003; Werth et al.,1997). Spindles are generated in thalamic networks and the correspond-ing EEG reflects activity conveyed viawidespread cortico-thalamic affer-ent projections (Contreras and Steriade, 1995; Steriade and Timofeev,2003). In humans, spindles show maximum amplitudes over centraland parietal neocortical areas (DeGennaro and Ferrara, 2003).

Recent research has provided compelling evidence that spindles areinvolved inmemory processing during sleep. Increases in spindle densi-ty andactivity are consistently observed in humans and rats duringNon-REM sleep following a learning period, and in several studies the spindleincreases were correlated with the overnight improvement in memory(e.g., Clemens et al., 2005, 2006; Eschenko et al., 2006; Fogel andSmith, 2006; Gais et al., 2002; Nishida and Walker, 2007; Schabus

et al., 2004; Tamaki et al., 2008). In both species, spindles are also asso-ciated with signs of reactivation of newly encoded memories in hippo-campal as well as specific neocortical regions (Bergmann et al.,unpublished results; Johnson et al., 2010; Peyrache et al., 2009). Basedon this and further evidence it was proposed that spindles are involvedin the transfer of memory information from hippocampal to neocorticalsites for long-term storage (Clemens et al., 2011; Diekelmann and Born,2010; Marshall and Born, 2007; Sirota et al., 2003). Specifically, it wassuggested that during spindles reactivated hippocampal memory infor-mation becomes nested into the excitable phases of the spindle cyclesuch that an integration of this information into neocortical networksis facilitated. Consistent with this view several studies indicated thatboth spindle and gamma activity are jointly increased during the upstate (as compared to the down state) of slow oscillations (Csercsaet al., 2010; Steriade, 2006; Steriade et al., 1996) although these studiesdid not address the issue of a direct gamma-spindle coupling.

Gammaactivity, i.e., rhythmic synchronization of neuronal activity inthe 30–100 Hz frequency band, has indeed been established as an ele-mentary operation of local information processing, underlying theencoding as well as reactivation of information in neocortical networks(Fries, 2009; Fries et al., 2007). Moreover, gamma oscillations representtemporal frames optimally suited for establishing spike-timing depen-dent synaptic plasticity assumed to underlie the formation of memory(Bikbaev and Manahan-Vaughan, 2008; Buzsáki, 2006). Based on thisbackground we hypothesized that spindles should be accompanied byincreases in or a distinct regulation of gamma frequency band if they

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serve the integration of memory-related information into neocorticalcircuitries. To test this hypothesis, we examined human gamma bandactivity recorded with magnetencephalography (MEG) during spindlesdetected in the EEG during NonREM sleep.We concentrated on the clas-sical 12.5–15.5 Hz-spindles ignoring themore recently dissociated slow(b 12 Hz) frontal spindles (Mölle et al., 2011). MEG rather than EEGwasused to determine gamma band activity becauseMEG does not rely on areference that can be confounded by electromuscular activity, and be-cause of its greater spatial resolution.

Material and methods

Subjects and polysomnographic recordings

Seven volunteers (age 23–32 years, six men) participated in thestudy. All were non-smokers, free of medication and had no history ofneurological, psychiatric or endocrine disorders, and had followed anormal sleep–wake rhythm for at least 2 weeks before the experiments.All subjects reported very robust sleeping habits and the ability to fallasleep in challenging environmental conditions. To increase sleep pro-pensity at the time ofMEG recordings, subjectswere asked to get up be-fore 5.00 am in the morning of experimental day, and not to take anynaps during the day. Also, they were not allowed to consume caffeineor alcohol on this day. The study was approved by the ethics committeeof the University of Luebeck and all participants gave written informedconsent prior to participation.

Sleep recordings were conducted between 11.00 pm (lights off) and2.00 am while the subject was lying in the MEG scanner (CTF SystemsInc. Port Coquitlam, Canada) in a supine position. For standard polysomo-graphical recordings and the detection of EEG spindles, EEG, vertical andhorizontal electrooculographic, and electromyographic signals wererecorded continuously. The EEG was recorded from electrodes at Cz andFz (according to the International 10–20 System) referenced to linkedelectrodes attached to the mastoids. Electrode impedance was alwaysbelow 5 KΩ. EEG signals were filtered between 0.1 and 120 Hz. EEGdata (2 channels) and MEG data (151 channels — first order gradiome-ters) were acquired simultaneously. The MEG system is installed in amagnetically shielded room (Vakuumschmelze, Hanau, Germany) to at-tenuate the influence of external magnetic fields. All signals were digi-tized at 250 Hz.

Sleep stages (1, 2, 3, 4, and REM sleep), awake time, and movementartifacts were scored offline for 30 s epochs according to standard cri-teria (Rechtschaffen and Kales, 1968). Stage 2 (S2) sleep correspondsto light NonREM sleep and stages 3 and 4 correspond to slow wavesleep (SWS).

Detection of EEG spindles

EEG analyses were carried out using an inhouse program running inMatlab R2008a (MathWorks, Natick, MA). Spindles were identified inNonREM sleep stage 2 and SWS in the EEG recorded from Cz. Detectionwas based on a standard algorithm (Mölle et al., 2002). In short, the EEGat Czwasfirst filtered by applying zero-phase digital band pass FIRfilterwith a 3 Hz bandwidth centered around the peak of fast spindles seen inthe power spectrum ~14 Hz. Then the standard deviation of the filteredsignal in all S2 and SWS epochs of the respective subject was calculated.Subsequently, the averaged root mean square (rms) of the signal wascomputed in a movingwindow of 0.2 s at every sample point. A spindlewas identified if the rms signal was above the threshold which was de-fined by 1.75±0.38 of the standard deviation of the filtered signal for atime period between 0.5 and 3.0 s. MEG data epochs were excluded asoutliers if they exhibited an increase of greater 1.5 times themean stan-dard deviation of all spindle epochs identified in an individual record-ing. Averaging of spindles was computed time-locked to the spindlepeak defined by the largest trough of the spindles. In addition, MEGspindles were analyzed which were basically detected in the same

manner as in the EEG. Individual MEG spindle thresholds were setsuch that spindle density was similar to that for detected EEG spindles.Spindle density refers to the number of identified spindles per 30 sepoch of scored NonREM sleep.

Analysis of MEG signals during detected EEG spindles

MEG activity in the low (30–40 Hz) and high (40–100 Hz) gammafrequency bands during detected spindles was quantified by (i) time-frequency analysis providing the distribution of gamma band power inthe MEG signal in a time interval ±0.9 s around the spindle maximum,and (ii) cross-frequency coherence as measure of phase coupling be-tween the MEG gamma band activity and the EEG spindle frequency,during the±0.9 s epochs around the spindlemaximum. Time-frequencyanalysis was additionally performed for the low 30–40 Hz gamma bandactivity in the EEG signal. For the analysis the FieldTrip open source tool-box (http://www.ru.nl/fcdonders/fieldtrip) was used.

Time-frequency analysis of EEG andMEG signal powerwas computedfor epochs of ±1.4 s around spindle peaks and for 8–100 Hz in steps of1 Hz using Morlet wavelets of 7 cycles (4 in the case of high gamma)and a step size of 0.01 s. (A larger±1.4 s epoch, instead of±0.9 s, aroundthe spindle peak was chosen for the time-frequency analysis to preventthat boundary effects affected power values for the ±0.9 s interval of in-terest.) The power was normalized separately for each frequency by sub-tracting absolute values by the average power during the ±0.9 s intervalaround the spindle peak.

Cross-frequency coherence between MEG signal and EEG/MEG spindle

To examine more closely a phase-frequency coupling of the MEGsignal to EEG spindles we calculated the cross-frequency coherencebetween both signals during periods of detected EEG spindles. Cal-culation of cross-frequency coherence was performed between theindividual EEG signal in the wider 10–16 Hz frequency range (com-prising the 12.5–15.5 Hz spindle frequency band of interest) and thetime course of the MEG power in the gamma frequency band. Forsimplification, this analysis was calculated for the total 30–100 Hzfrequency band. Power was evaluated using a single-hanning taper inthe time-frequency transformation at every sample for frequencies 30–100 Hzwith a resolution of 2 Hz. The number of cycles per timewindowwas set at 2 cycles. Then the spindle and non-spindle epochs were z-transformed. Cross spectral density was computed between the EEGspindle signal and MEG signal power (30–100 Hz). Coherence valueswere calculated from the cross spectral density carrying a value of0 for random phase and of 1 for constant phase difference (Osipovaet al., 2008).

In supplementary analyses, we calculated the cross-frequency co-herence between MEG signal and MEG spindles which were identifiedlocally in the MEG (rather than in the EEG at Cz). The cross-frequencycoherence method was applied in the same way as mentioned abovefor EEG spindles.

Statistical analyses

For a statistical evaluation, time-frequency and cross-frequency re-sults during EEG spindles were compared with respective non-spindlecontrol epochs. Non-spindle control epochs (of 2.8 s duration) wererandomly selected from the same time intervals of NonREM sleepused for spindle detection, and subjected to identical processing as spin-dle epochs. (Epochs containing movement arousals or movement timewere excluded). As time-frequency data did not exhibit normal distri-bution they were log transformed (decibel) prior to applying unpairedStudent's t-tests (two-sided). Resulting p-values were corrected formultiple comparisons using Bonferroni correction.

Table 1Sleep parameters for the four subjects entering stable sleep during MEG recordings.Total sleep time (after first sleep onset) as well as time spent in sleep stages S2, S3,and S4 (S3 and S4 representing slow wave sleep) is indicated. None of the subjects en-tered REM sleep. The two bottom rows indicate the total number of spindles detectedin each individual and the spindle density (number/per 30-sec epoch in NonREM sleepstage 2, 3 and 4), respectively.

Subject # # 1 # 2 # 4 # 6

Total (min) 30 85 43 25S2 (min) 10.5 43.5 13.5 13S3 (min) 1 24 3.5 0S4 (min) 1 11.5 8.5 0Spindle count 50 41 79 36Spindle density 2.0 0.3 1.6 1.4

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Results

Sleep recordings and EEG spindles

Polysomnographic data showed that 4 of the 7 subjects were able tosleep during MEG recordings. Average (± SEM) sleep duration in thesesubjects was 45.8±13.6 min (see Table 1 for a summary of polysomno-graphic data in these subjects). All of these subjects entered NonREMsleep stage 2 and three reached slow wave sleep (SWS). The numberof EEG spindles identified during NonREM sleep averaged 51.5±9.6(range 36–79), average spindle density amounted to 1.3±0.4 spindles/30 s (Table 1).

Fig. 1. (A) Grand average across four subjects (n=206) of (left) spindles identified in the EEof DC drift in the EEG signal). Middle — time-frequency distribution of EEG power during despindles; Y-axis indicates EEG frequency (8–40 Hz); color coding refers to EEG power (in dBpower in a ±0.9 s interval around the spindle peak, separately for each frequency band. Ridistribution during spindle periods with random non-spindle EEG periods. Areas with absotours denote areas of Bonferroni corrected (for time axis) significance, with absolute t-valurespectively).

Fig. 1 illustrates the grand average of spindles, as identified in the EEGsignal obtained from Cz, as well as the averaged EEG spindle from repre-sentative subjects (#1 and #4). In a first step, we analyzed the time-frequency distribution of the EEG signal during these spindles. Asexpected, there was a strong increase in power in the 12.5–15.5 Hz fre-quency band ±0.35 s around the spindle peak. In addition, recordingsin individual subjects indicated both significant increases and decreasesin power in the lowgammaand beta band frequencies (25–40 Hz) occur-ring during spindles which, however, varied considerably in timing andexact frequency among subjects. (Some of these increases and decreases,as seen in Fig. 1 B and C, even appeared to occur before or after the actualaverage spindlewhich is difficult to explain, but could point to a networkmechanism imposing the spindle rhythmon lowgamma activitywithoutexpressing in the synchronizedmembrane potential oscillations that un-derlie the recorded spindle.) In the grand average, significant increases inlower gamma power between 30 and 40 Hz were revealed that occurredshortly after the spindle peak (Fig. 1).

MEG topography of EEG spindles — 12.5–15.5 Hz frequency band

Fig. 2 shows the time-frequency distribution of t-values for MEGpower for the frequencies between 8 and 40 Hz during identified EEGspindles, including the 12.5–15.5 Hz spindle frequency band of interest,for the grand average across all subjects. The t-valueswere derived froma comparisonwith non-spindle epochs randomly selected from the EEG

G at Cz. Averaging was performed time-locked to the spindle maximum (after removaltected EEG spindles. X-axis indicates ±0.9 s interval around the maximum of detected) for respective frequency. Power values are normalized with reference to the averageght — map of t-values (color coded) resulting from comparison of EEG time-frequencylute t-valuesb1.96 (corresponding to uncorrected pN0.05) are masked out. Black con-es≥3.67, pb0.00028. B and C show the same for representative subjects (#1 and #4,

Fig. 2. Time-frequency distribution of t-values for MEG signal power during EEG spindles averaged across subjects and spindles (n=206) at a reduced resolution. X-axes indicate ±0.9 sinterval around the maximum of detected EEG spindles; y-axes indicate MEG frequencies (8–40 Hz); color coding refers to t-values (−8≤t≤+8) resulting from the comparison of thetime-frequency distribution ofMEG power during EEG spindle periods with random non-spindle EEG periods. Areas with absolute t-valuesb1.96 (corresponding to uncorrected pN0.05)are masked out. Black contours denote areas of Bonferroni corrected (for time axis) significance, absolute t≥3.67, pb0.00028. Insert (upper left corner) magnifies data from example re-cording site (MZP02). Note transient significant increases as well as decreases in gamma band activity over the occipital, right parietal, and mid-prefrontal cortex.

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recording. The analysis revealed a clear increase in signal power in the12.5–15.5 Hz frequency band which corresponded to the power of thereference EEG spindles and, in the grand average showed maximaover the mid-prefrontal cortex and the occipital cortex (pb0.0001, foreffects of topography), although inspection of individual recordings in-dicated substantial topographical variability in the actual peaks, withlacking occipital activity in one subject (#2). More fine grained analysesof the temporal evolution of t-values for MEG signal power in the 12.5–15.5 Hz frequency band during EEG spindles confirmed, in the averageacross all subjects, a gradual increase in MEG power spreading fromprefrontal cortical sites during the waxing portion of the EEG spindle,and a parallel increase originating from occipital regions, with bothmaxima showing somewhat greater persistence during the waningthan waxing portion of the EEG spindle (Fig. 3A). Cross-correlation an-alyses between individual EEG spindles and the corresponding MEGsignal confirmed high synchrony between both signals either in phaseor at a 180° phase lag (i.e., maximum positive or negative coefficientsat a zero-time lag), although therewere also caseswhere theMEG spin-dle signal appeared to be local and showed little correlation with theEEG signal (Fig. 3B).

MEG power in the low and high gamma frequency bands during EEGspindles

The distribution of MEG power in the low gamma frequency band(30–40 Hz) during spindles identified in the EEG is illustrated as partof the time-frequency plots in Fig. 2. Similar to EEG power in the lowgamma frequency band (see above), also MEG power in this bandshowed both transient increases and decreases during EEG spindles

which in all subjects reached clear significance (at a p-level of0.00028, Bonferroni-corrected for multiple comparisons in time) whencompared with non-spindle control intervals. Looking at individualEEG spindles, the modulations of gamma power often appeared asbroadband phenomena covering both low and high gamma frequencybands (Fig. 3C). To compare the distribution of significant increasesand decreases in low gamma activity during EEG spindles, we summedpixel-wise (i.e., per 1 Hz and 0.01 s) significant increases and decreasesacross all MEG channels and subjects ±0.5 s around a spindle peak. Infact, this analysis revealed a preponderance of decreases (i.e., suppres-sion) over increases of gamma band activity during EEG spindles (255vs 186, chi2=5.44, pb0.02). Increases in gamma power occurredmore often during thewaning portion of the spindle (i.e., after the spin-dle peak) than during the waxing portion (120 vs 66, chi2=8.01,pb0.005), whereas decreases occurred more often during the waxingportion of the spindle (156 vs 99, chi2=6.46, pb0.011). Topographical-ly suchmodulation (i.e., increases and decreases) occurredmostly at lo-cations showing also quite robust MEG spindle power in the 12.5–15.5 Hz frequency band (pb0.00028, for a comparison between sitessplit-half into sites of low vs. high MEG spindle activity). While signifi-cant in the individual recordings, thesemodulations showed some jitterin frequency and localization as well as in the exact timing during thespindle so that, in the grand average across subjects, only the most ro-bust modulation remained significant, mainly located over the occipital,right parietal, and mid prefrontal cortex (Fig. 2).

In all subjects, we also observed significant (at pb0.0005, Bonferronicorrected) transient increases and decreases in MEG power in the highgamma frequency band (40–100 Hz) during identified EEG spindles,when compared with the distribution of high frequency gamma

Fig. 3. (A) Temporal evolution of MEG signal power in the 12.5–15.5 Hz frequency band during EEG spindles averaged across subsequent 100-ms intervals in the ±0.5 s intervalaround the maximum (0.0 s) of detected EEG spindles averaged across subjects and spindles (n=206, upper panel) and for subject #4 (n=79, lower panel). Maps of t-values areshown resulting from comparison of MEG signal power during EEG spindle periods with random non-spindle EEG periods. A t-value b1.96 (corresponding to uncorrected pN0.05)was masked out. The black contours denote areas of Bonferroni corrected (for time and number of sensors) significance, t≥3.75 (upper panel) and t≥3.81 (lower panel), pb0.0002.(B) Left — superimposed individual EEG (blue) spindle and MEG (red) signal (filtered between 12.5 and 15.5 Hz, lower panel) and corresponding cross-correlation function be-tween both signals (for ±0.1 s lag, upper panel). Location of MEG recording is indicated at the top. Rightmost map shows the grand average (across subjects and EEG spindles)of absolute correlation coefficients at a zero lag (i.e. maximum coefficient ±0.02 s around zero) between EEG spindles and MEG signal, indicating high phase synchrony betweenboth signals in particular over right frontal and occipital areas. (C) Time-frequency distribution of power in the low (30–40 Hz) and high (40–100 Hz) gamma frequency band dur-ing individual EEG spindles (an interval ±0.5 s around the maximum of the spindle is shown; spindles are the same as in panel B). Power values are corrected per frequency bandwith reference to the average power in a ±0.9-sec interval around the spindle peak. Note, synchronization of gamma power modulation to individual spindle oscillations acrosswide frequency ranges.

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power during non-spindle epochs (Fig. 4). Again these significantevents represented mainly suppression of gamma activity rather thanenhancement (22,321 vs 940, chi2=12457.9, pb0.0001) and were un-evenly distributed between thewaxing andwaning parts of the spindle,both occurring slightly more over the waning than waxing portion ofthe spindle (decreases: 11,772 vs 10549, chi2=33.53, pb0.0001; in-creases 544 vs 396, chi2=11.72, pb0.001). Topographically, thesemodulations also occurred preferentially at sites with high 12.5–

15.5 Hz MEG spindle activity (pb0.00028, for a comparison with sitessplit-half into sites of low vs. high 12.5–15.5 MEG spindle activity). Inthe grand average, most robust transient modulations in high gammaband activity were observed in the 40–60 Hz portion, mainly over themid-frontal and mid-parietal cortex (Fig. 4).

We finally explored the distribution of significant (pb0.0005) in-creases and decreases in gamma band activity between troughs andpeaks of EEG spindles. For this analysis we summed significant

Fig. 4. Time-frequency distribution of MEG signal power (in the 40–100 Hz band) during EEG spindles averaged across all subjects. X-axes indicate ±0.5 s interval around the max-imum of detected EEG spindles; y-axes indicate MEG frequencies; color coding refers to t-values (−8≤ t≤+8) resulting from comparison of MEG time-frequency distribution dur-ing EEG spindle periods with random non-spindle EEG periods. Areas with absolute t-valuesb1.96 (corresponding to uncorrected pN0.05) are masked out. Black contours denoteareas of Bonferroni corrected (for time axis) significance, absolute t-values≥3.51, pb0.0005. Insert magnifies data from example recording site (MZF03). Note preponderance ofsignificant decreases in gamma power.

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increases vs. decreases pixel-wise (per 1 Hz and 0.01 s) across frequen-cies, MEG channels and subjects in the interval ±10 ms around spindlepeaks and troughs. Because gamma power between 30 and 40 Hz couldnot be estimated at a sufficiently high temporal resolution this analysisconcentrated on the high 40–100 Hz frequency band. (Please note thateven for the lower 40–50 Hz portion of the high gamma band this anal-ysis is imprecise). Both types of events, i.e., significant increases and de-creases occurred slightly but significantly more often±10ms around aspindle peak than ±10 ms around a trough (increases: 249 vs 156events, chi2=10.83, pb0.001; decreases: 4992 vs 4543, chi2=10.58,pb0.001; preliminary analyses in one subject which allowed to com-pare spindles that occurred in the presence vs. absence of a K-complex,suggested that K-complexes maymodulate this relationship in asmuchas here gamma band suppression occurred more often around spindlepeaks).

Cross-frequency coherence between MEG signal and EEG spindle

The cross-frequency plots as depicted in Fig. 5 indicate whether at agiven (EEG) frequency a phase-coupled increase in amplitude occursfor a certain MEG frequency. While a wider 10–16 Hz EEG frequencyrange was included, the analysis revealed a highly significant couplingofMEG gamma band frequencies selectively to the 12.5–15.5 Hz EEG fre-quency band indicative of EEG spindle activity. This coupling, in the grandaverage, was most robustly observed over the posterior (occipital, rightparietal) cortex and over central cortical regions. Comparing low and

high MEG gamma band frequencies, it appeared to be stronger in thelow 30–40 Hz frequency band. In the high MEG gamma band significantcoupling occurred mainly for frequenciesb80 Hz, and was topographi-cally more restricted to recordings from posterior and central corticalareas.

In supplementary analyses, spindles in the MEG were detected basi-cally in the same way as in the EEG separately at the different recordingsites. Fig. 6 compares cross-frequency coupling of gamma band activity(30–100 Hz) during spindles detected in the local MEG signal versusspindles detected in the EEG signal (at Cz). Cross-frequency coherencefor local MEG spindles revealed strong coupling of gamma band activityat some locations. In comparisons within single subjects, the local phasecoupling of gamma band activity to MEG spindles appear to be more ro-bust than to the EEG spindles (Fig. 6). Also, this comparison revealed thatsignificant phase-coupling of gamma activity toMEG spindles occurred atadditional sites, however, again with some topographical heterogeneityacross subjects.

Discussion

The major findings of the present study were: (i) that EEG spindleswere associatedwith increasedMEG power in the 12.5–15.5 Hz spindlefrequency band predominantly over prefrontal and occipital regions.(ii) EEG spindles were associated with both transient increases and de-creases in MEG power in the low (30–40 Hz) and high (40–100 Hz)gamma frequency with clear preponderance of decreases in gamma

Fig. 5. Cross-frequency coherence between EEG spindles (at Cz) and MEG gamma band activity averaged across subjects and spindles (n=206). The EEG signal between 10 and16 Hz (x-axes) was related to MEG activity in the 30–100 Hz frequency band. The color code represents (EEG-MEG) coherence values between 0 and 0.6, indicating phase-coupledincreases in amplitude for a certain MEG frequency at a given EEG frequency. Cross-frequency coherence during EEG spindles was statistically compared with random non-spindleEEG periods. Areas with t-valuesb1.96 (uncorrected pN0.05) were masked out. Black contours denote areas of Bonferroni corrected (for EEG frequencies) significance, pb0.00122.Insert magnifies data from example recording site (MRO12).

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power. (iii) Modulations of power especially in the high gamma band,occurred slightly more often during the waning than waxing part ofthe spindle and, importantly, more often around spindle peaks thantroughs. (iv) Cross-frequency coherence analyses indicated strongphase coupling ofMEG gamma activitywith the spindle rhythm,mainlyfor the b80 Hz portion of the gamma band. In combination these find-ings are in linewith the assumption that the spindle oscillation providesa temporal frame for the coordinate processing of local cortical memoryrepresentations.

A number of previous studies recorded MEG during sleep spindlesmainly in order to identify the sources of spindle activity. Overall, thesestudies revealed generators situated bilaterally in the deep parieto-central and fronto-central cortex with the parieto-central spindles typi-cally showing faster average frequencies than the fronto-central spindles(Anderer et al., 2001; Dehghani et al., 2010a; Gumenyuk et al., 2009; Ishiiet al., 2003;Manshanden et al., 2002; Shih et al., 2000; Simon et al., 2000;Urakami, 2008). Concentrating on fast spindles detected in EEG record-ings from central regions, our findings of associated MEG activity in thissame frequency range dominating over frontal and occipital areas arewell in line with these previous studies, overall supporting the viewthat most of the activity picked up in the MEG during fast centro-parietalspindles results from synchronous activation in widely distributed tha-lamo-cortical modules (Anderer et al., 2001; Urakami, 2008). However,considering the substantial variability in the topography of MEG spindleactivity during EEG spindles that was observable within and across sub-jects, activity within more local thalamo-cortical loops has probably

contributed to the averaged signal (Dehghani et al., 2011). These observa-tions are in accordance with recent findings from intracranial recordingsin neurosurgical patients which revealed that spindles quite often repre-sent local events (Nir et al., 2011). Whereas distributed spindles arisefrom non-specific intralaminar thalamic projections reaching mainlylayer I in wide spread cortical areas, focal spindles are assumed to arisefrom specific thalamic projections reaching layer IV of a restricted num-ber of cortical modules (Jones, 2001). However, given that only epochsof substantial EEG spindle activity at Cz entered themain analyses, contri-butions of distributed non-specific spindles probably prevailed in mostcases.

Rather than locating sources of EEG spindles, this study aimed at ex-amining the association of spindles with gamma activity. The synchro-nized neuronal activity in the gamma band is considered a sign ofspecific cortical information processing enabling the potentiation of syn-aptic transmission in local circuits, i.e., processes assumed to underlie theformation of cortical memory representations (Bikbaev and Manahan-Vaughan, 2008; Fries, 2009). Indeed previous observations have pointedtowards a close coexistence of spindle and gamma rhythms during slowoscillations (Cash et al., 2009; Le Van Quyen et al., 2010; Puig et al.,2008; Steriade, 2006). Extending these findings, the present study, tothe best of our knowledge, is the first to directly demonstrate a distinctregulation of gamma band activity during spindles, expressing in a clearmodulation of gamma power as well as a phase coupling of gammaband activity to the spindle oscillation. Phase-coupling covered thewhole range of frequencies between 30 and 80 Hz excluding a primary

Fig. 6. Cross-frequency coherence between MEG gamma band activity (30–100 Hz) and (top) EEG spindles at Cz (n=79) and (bottom) local MEG spindles (n=114 to 147) in anindividual subject (#4). X-axes denote 10–16 Hz frequency band in which spindles occur; y-axis indicates 30–100 Hz MEG gamma band. The color code represents coherencevalues between 0 and 0.6; with areas masked out for which the comparison with coherence during random non-spindle periods resulted in t-valuesb1.96 (uncorrectedpN0.05). Black contours denote areas of Bonferroni corrected (for EEG/MEG spindle frequencies) significance, pb0.00122. Inserts magnify data from example recording site(MRO22).

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origin fromharmonics of the spindle activity 12.5–15.5 Hz (see Fig. 5, e.g.,MEG spindle sensor—MRO12). Increases and decreases in gamma bandactivity during detected EEG spindles were seen in the MEG but also inthe EEG where the effects appeared to be clearer in the lower gammaband frequency which possibly reflects the lower sensitivity of the EEGto faster brain potential oscillations. (A deeper comparison of gammaband findings in our MEG and EEG recordings is hampered by the factthat the EEG was recorded only from 2 channels.) Modulations ingammapower overall occurred preferentially at sites of highMEG spindleactivity, further supporting a functional link between both rhythms.However, highMEG spindle activity was not predictive for a strongmod-ulation of gamma amplitude as it also occurred in the absence of de-creases or increases in gamma activity. In addition the topography ofsignificant phase coupling of gamma activity to EEG spindles (withmost robust coupling over occipital, right parietal and central cortical re-gions) slightly differed from that of spindle-associated increases in MEGgamma power (overall extending more to frontal cortical regions). To-gether with a preponderance of significant gammamodulation occurringduring the falling flanks of the spindle, these observations argue againstany immediate influence of spindle oscillations on gamma generatingmechanisms, but are rather indicative for distinct neuronal circuitries un-derlying both spindles and gammaoscillations. However, spindles appearto provide time-framing conditions for the occurrence of gammaband ac-tivity. Given that decreases clearly outweighed increases in gammapower during a spindle, this regulatory influence is obviously mediatedprimarily via inhibitory actions on networks generating gamma activity.

The present analyses basically do not allow for any inferences as tothe directionality of the interaction between spindles and gammaband activity. However, the global nature of the EEG spindle signal,used here as reference, contrasting with the local nature of the in-creases in gamma band activity, supports the assumption that spin-dles represent a permissive condition for the temporal regulation ofgamma band activity in neocortical networks. This view of a coordi-nating role of spindles for the generation of gamma activity is furthersupported by our cross-frequency coherence analyses. These analysesrevealed that, compared with the EEG derived central spindle, phasecoupling of gamma activity was overall more robust to local MEGspindle oscillations which probably reflect to a larger extent specificthalamo-cortical projections (Dehghani et al., 2010b; Jones, 2001).

The surface positive peaks of the spindle correspond to extracellularnegativity in deeper cortical layers near the soma of pyramidal cells, andthus may represent the excitable phase of the spindle cycle (Contreraset al., 1997). The fact that decreases and increases in gamma powermore often occurred around peaks than troughs of the spindle suggestsa gamma-spindle phase coupling such that these gamma modulationspreferentially fall into the periods of increased pyramidal excitability,i.e., periods that might also be associated with increased Ca2+ entryinto these cells, thusmaking them susceptible to succeeding plastic syn-aptic changes (Yuste and Tank, 1996). However, the high-frequencyspike bursts of thalamo-cortical cells underlying the (surface positive)peaks of the spindle are associated with only a surprisingly low dis-charge rate of these pyramidal cells. This could be explained by thefact that thalamo-cortical cells during spindle peaks do not only directlyinduce excitatory post synaptic potentials in pyramidal cells, but simul-taneously recruit inhibitory cells providing local inhibition to the somaand proximal dendrites of pyramidal cells (Contreras et al., 1997).There is evidence that this inhibition is mediated by fast spiking inter-neurons which, during the slow oscillation cycle, were revealed to con-sistently fire in the beginning of the up-state when spindle probability ishighest (Puig et al., 2008). It might be the thalamo-cortical input to thisnetwork of inhibitory fast spiking neurons that eventually promotes thephase-locked generation of local gamma oscillations (Bartos et al., 2007;Gibson et al., 1999; Puig et al., 2008). Such scenario would also be con-sistent with our at first glance perhaps surprising finding that controlof gamma activity during the spindle is established preponderantly viaa gamma suppressing mechanism. The preponderance of gamma

suppression may also reflect that contributions from ascending path-ways to gamma band activity are blocked during spindles at the thalam-ic level (Castelo-Branco et al., 1998; Rosanova and Timofeev, 2005;Steriade, 1991; Steriade et al., 1996). It can be speculated that this sup-pressive control of local gamma activity associated with spindles repre-sents a mechanism serving to increase the signal-to-noise ratio withregard to the spatial representation of information in neocortical net-works. However, this view needs to be experimentally explored.

Conclusion

In conclusion, although suffering from obvious limitation such as thesmall sample of subjects examined and the poor quality of experimentalsleep in theMEG scanner, our study demonstrates a clear temporal asso-ciation between EEG sleep spindles and local gamma activity. The obser-vation of significant phase-coupling of gamma activity to spindleoscillations is consistent with the view that the surface positive (peak)periods of the spindle oscillation provide windows for increasedmemo-ry processing during sleep that may eventually lead to an enhanced cor-tical representation of these memories (Marshall and Born, 2007;Rosanova and Ulrich, 2005; Steriade and Timofeev, 1997). Phase cou-pling of gamma activity to the theta rhythm during wakefulness hasbeen proposed to serve the temporal coordinate integration of informa-tion residing in spatiallywidespread neocortical assemblies (Sirota et al.,2008; Varela et al., 2001). Because spindles occur synchronized overwidespread cortical areas, phase coupling of gamma to spindle oscilla-tions may likewise serve to particularly facilitate the integration ofmemory information over distributed cortical areas.

Acknowledgments

We thank Anja Otterbein for technical assistance and Lisa Marshallfor helpful discussions and comments on previous versions of themanu-script. This study was supported by a grant from the DFG SFB 654 ‘Plas-ticity and Sleep’. A.A. was supported by the Graduate School forComputing in Medicine and Life Sciences which is funded by Germany'sExcellence Initiative [DFG GSC 235/1].

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