NeuroImage 66 (2013) 642–647

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Successful memory encoding is associated with increased cross-frequency couplingbetween frontal theta and posterior gamma oscillations in humanscalp-recorded EEG

Uwe Friese a,b,⁎,1, Moritz Köster b,c,1, Uwe Hassler b, Ulla Martens b,Nelson Trujillo-Barreto d, Thomas Gruber b

a University Medical Center Hamburg-Eppendorf, Department of Neurophysiology and Pathophysiology, Hamburg, Germanyb Institute of Psychology, University of Osnabrueck, Germanyc Institute of Cognitive Science, University of Osnabrueck, Germanyd Cuban Neuroscience Center, Havana, Cuba

⁎ Corresponding author at: University Medical Centerment of Neurophysiology and Pathophysiology, MartinGermany. Fax: +49 40 7410 57752.

E-mail address: u.friese@uke.de (U. Friese).1 These authors contributed equally to this work.

1053-8119/$ – see front matter © 2012 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.neuroimage.2012.11.002

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 1 November 2012Available online 8 November 2012

Keywords:EEGOscillationsCross-frequency-couplingGamma-bandTheta-bandMemory encoding

Although previous studies have established that successful memory encoding is associated with increasedsynchronization of theta-band and gamma-band oscillations, it is unclear if there is a functional relationshipbetween oscillations in these frequency bands. Using scalp-recorded EEG in healthy human participants, wedemonstrate that cross-frequency coupling between frontal theta phase and posterior gamma power is en-hanced during the encoding of visual stimuli which participants later on remember versus items which par-ticipants subsequently forget (“subsequent memory effect,” SME). Conventional wavelet analyses and sourcelocalizations revealed SMEs in spectral power of theta-, alpha-, and gamma-band. Successful compared to un-successful encoding was reflected in increased theta-band activity in right frontal cortex as well as increasedgamma-band activity in parietal-occipital regions. Moreover, decreased alpha-band activity in prefrontal andoccipital cortex was also related to successful encoding. Overall, these findings support the idea that duringthe formation of new memories frontal cortex regions interact with cortical representations in posteriorareas.

© 2012 Elsevier Inc. All rights reserved.

Introduction

Impressive evidence has been accumulated that synchronized neu-ronal activity serves the integration of spatially distributed processingin the brain (Engel et al., 2001; Varela et al., 2001). In particular, oscilla-tions in the gamma band range (>30 Hz) are thought to reflect pro-cesses related to the activation and maintenance of neuronal objectrepresentations (Jensen et al., 2007). Low frequency oscillations inthe theta band,~5 Hz, and in the alpha band, 8–12 Hz, have been re-lated to top-down control modulating the processing of neuronal ob-ject representations (Palva et al., 2005). Mostly based on animalresearch or studies with invasive recordings in epilepsy patients,several authors have suggested that the coordinated interplay oflow- and high-frequency oscillations is likely to be crucial for variousmemory processes (Canolty and Knight, 2010; Fell and Axmacher,2011; Freunberger et al., 2011; Nyhus and Curran, 2010; Palva and

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Palva, 2007). So far, only a few studies using electroencephalography(EEG) or magnetencephalography (MEG) in humans have demon-strated cross-frequency coupling between low- and high-frequencyoscillations in working memory and attention tasks (Demiralp etal., 2007; Palva et al., 2005; Sauseng et al., 2008, 2009).

In the present study, we employed a subsequent memory para-digm to investigate whether low- and high-frequency oscillationsin scalp-recorded EEG data interact during successful encoding ofnewmemories. In a subsequent memory paradigm, neuronal activityduring the encoding of stimuli which are remembered in a subsequentmemory test, is contrasted versus activity during the encoding ofstimuli which are later forgotten (Paller and Wagner, 2002). PreviousEEG/MEG studies on oscillatory brain activity have established “subse-quent memory effects” (SME) in various frequency bands. Successfulmemory encoding has been found to be accompanied by increased syn-chrony in the theta and gamma band aswell as by decreased synchronyin the alpha and beta band (Gruber et al., 2004; Hanslmayr et al., 2009,2011; Klimesch et al., 1996a, 1996b; Osipova et al., 2006). Most impor-tantly, Osipova et al. (2006) found that increased frontal theta powercoincided with enhanced gamma power in posterior cortex regionsfor later remembered versus later forgotten stimuli. The authors specu-lated that frontal theta oscillations might reflect top-down processes

643U. Friese et al. / NeuroImage 66 (2013) 642–647

modulating gamma band activity related to neuronal representations inposterior regions. However, this hypothesis was not tested directly. Onthe basis of Osipova et al. (2006), themain objectives of our study were(a) to confirm subsequent memory effects in theta, alpha, and gammafrequency bands, (b) to identify the cortical generators of these effects,and (c) to explore if posterior gamma power is coupled to the phases offrontal theta oscillations during the encoding of new information. Thistype of cross-frequency coupling would provide strong evidence forthe notion that theta-oscillations play a pivotal role in long-rangecommunication between brain regions involved in memory encodingprocesses.

Materials and methods

Participants

A total of 26 healthy university students (23 female, M=21.3 years, SD=3.7) received either monetary compensation orcourse credits for participation. None of the participants reported anyhistory of neurological or psychiatrical disorder. All participants hadnormal or corrected-to-normal vision. Informed consent was obtainedfrom all participants according to the Declaration of Helsinki.

Stimuli and procedure

The stimuli consisted of 150 pictures of living (e.g. plants, animals)and 150 pictures of nonliving objects (e.g. clothes, tools). Pictures cov-ered a visual angle of 6.2° 6.2° and were presented at the center of a21 inch computer screen. In the encoding session, 200 pictures (100of each category) were presented in random order and had to be classi-fied as living or nonliving objects. The presentation sequencewas blankscreen (1 s), fixation point (1.0–1.5 s), target stimulus (2 s), questionmark until response. Participants used response keys with different fin-gers of their right hand. The encoding sessionwas followed by a 15 minfiller task in which participants solved mathematical equations. After-wards 200 pictures from the encoding session were randomlyintermixed with 100 new stimuli, and participants were to indicatewhether they “remember” the object, they “know” the object, or if theobject was not shown in the first part of the experiment and therefore“new”. This so-called remember/know procedure allows to distinguishbetween familiarity-based recognition and controlled recollection(Tulving, 1985). “Know”-responses are assumed to rely more on famil-iarity while “remember”-responses are rather associated with recollec-tion. Given our main interest in successful memory encoding processes,we focused on remember-responses to obtain “purer”measures of rec-ollection memory and to enhance SMEs (Paller and Wagner, 2002).Hence, we discarded trials with know-responses and limited EEG dataanalyses to comparisons of subsequently remembered (SR) and subse-quently forgotten (SF) items.

Electrophysiological recordings

A BioSemi Active-Two amplifier system with 128 active electrodeswas used at 512 Hz sampling rate. Additionally, horizontal and verticalelectro-oculograms were recorded. Common mode sense (CMS) anddriven right leg (DRL) electrodes served as recording reference andground. EEG data were re-referenced to the average of all electrodesand segmented into epochs from−500 to 2000 mswith regard to stim-ulus onset. Eye blink artifacts were removed using an independentcomponent based procedure (Delorme andMakeig, 2004). Further arti-factswere removedwith SCADS (Junghoefer et al., 2000). For the analysisof gamma-band oscillations, we applied a correction of saccade-relatedtransient potentials (COSTRAP; Hassler et al., 2011). This procedure hasbeen shown to remove possible artifacts caused by miniature eye move-ments (Yuval-Greenberg et al., 2008). For all analyses, the number oftrials per condition was matched for each subject according to the

condition with the lowest number of trials. Data of three participantswith less than 15 trials per conditionwere discarded. For the remaining23 participants on average 26.1 trials (SD=7.7 trials) per conditionwere available.

Data analysis: Spectral changes in electrode space

Total spectral changes of oscillatory activitywere calculated for eachtrial using Morlet wavelets (Bertrand and Pantev, 1994) with seven cy-cles. Further analyses were based on a time window of 400–1300 msafter stimulus onset. This segment was chosen after visual inspectionof time-by-frequency plots. Moreover, we wanted to exclude possibleinterference from early visual responses (Osipova et al., 2008). Forbroadband gamma oscillations, single-trial power spectra were aver-agedwithin 50–80 Hz. As theta and alpha signals vary largely across in-dividuals (Klimesch, 1999),we selected frequencies for each participantindividually on the basis of all trials across all conditions. The meantheta frequencywas 5.1 Hz (SD=1.2 Hz) and themean alpha frequen-cy was 10.2 Hz (SD=1.7 Hz). To evaluate SMEs (SR minus SF) in elec-trode space, we averaged data for each subject and electrode within therespective frequency band and time frame and subjected individualmeans to paired sample t-tests.

Data analysis: Spectral changes in scource space

To localize the generators of oscillatory EEG activity, single-trialVARETA analyses were conducted (for details see Gruber et al., 2006).This method is based on a discrete spline distributed inverse modeland reveals the estimates of the primary current densities (PCDs) insource space which are the spatially smoothest solutions compatiblewith the observed scalp topographies. PCD estimates for SR and SFwere subjected to voxel-wise paired sample t-tests.

Data analysis: Phase-amplitude coupling

For the assessment of phase-amplitude coupling between low-frequency theta/alpha and high-frequency gamma oscillations, weused the modulation index (MI) described in detail by Tort et al.(2010). The time series data for this analysis were created by averagingdata epochs (400–1300 ms) across selected electrodes for SR and SF.Electrode selection was based on significant SMEs in the different fre-quency bands (as described above). In brief, coupling betweenlow-frequency phase signals and high-frequency amplitude signalswas computed as follows. The signals in questionwerefiltered at the re-spective frequencies, and the Hilbert transform was used to derivephase information of the low-frequency signal as well as the amplitudeenvelope of the high-frequency signal. Next, a composite time serieswas constructed which represents the amplitude of the gamma oscilla-tion at each phase of the low-frequency signal. Low frequency phaseswere binned into twelve 30° intervals (0–360°) and the mean ampli-tude of the gamma oscillation in each bin was derived. These meanvalues were normalized by dividing each bin value by the sum over allbins. The resulting “amplitude distributions” are uniform if nophase-amplitude coupling is evident. Deviations from uniformity canthen be quantified by means of a discrete and normalized (between0 and 1) version of the Kullback-Leibler divergence between the em-pirical phase-to-amplitude distribution and the uniform distribution(i.e. the modulation index, MI). Tort et al. (2010) have shown thatthe MI does not depend on the absolute level of the signal, and it asalso relatively insensitive to changes of noise levels. These propertiesmake the MI especially suited for evaluating the intensity of phase-amplitude coupling. For all subjects, we calculatedMI-values separatelyfor SR and SF trials, and tested for differences between conditions usingnon-parametric Wilcoxon signed rank tests.

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Results

Behavioral data

On average, 48.5% (SEM=3.6%) of the encoding session items wereremembered and 22.1% (SEM=3.6%) were reported new. Counting re-member responses to old items as hits and remember responses to newitems as false alarms (M=2.8, SEM=1.0%), mean discrimination per-formance for remembered items was significantly larger than chance(d′=1.99; t22=17.2, pb .001).

SMEs in theta-, alpha-, and gamma-band power

Time-by-frequency representations of oscillatory activity aredepicted in Fig. 1 as power changes relative to the 200 mspre-stimulus baseline activity. After stimulus-onset, the averagetheta power clearly increased, whereas spectral power decreasedin the alpha-band. Average gamma power showed a maximum ap-proximately between 50 and 65 Hz throughout the measurementinterval.

Subsequent memory effects in oscillatory power were evident intheta-, alpha-, and gamma-band. The left hand side of Fig. 2 showsgrand mean spherical spline interpolated topographical maps ofspectral power (400–1300 ms) separately for SR and SF conditions.At marked electrodes the subsequent memory effect, i.e. the differ-ence SR–SF, was significant with pb .01 (corresponding to a false dis-covery rate of qb .1; Benjamini and Hochberg, 1995). The right handside of Fig. 2 illustrates the estimated VARETA source localizations ofthe SMEs. Statistical maps are thresholded at t-values associatedwith pb .01 and projected onto the smoothed surface of an MRI tem-plate brain. In the theta-band, SMEs were evident at right frontalelectrodes with corresponding source localization in the right frontalcortex. Alpha-band suppression was larger for remembered versusforgotten items at two large electrode clusters in bilateral anteriorand posterior scalp regions. The sources of these SMEs were foundin bilateral middle frontal gyri and in primary visual cortex areas.Gamma-band SMEs occurred at central parietal electrodes withsources located in left parietal and occipital cortex as well as inright inferior parietal cortex.

1.06

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0 400 800 1200 1600

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0 400 800 1200 1600

10

6

2

14

18

60

50

40

70

80

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z)F

requency (Hz)

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Fig. 1. Grand mean time-by-frequency plots of spectral power for frequencies up to 20 Hzand for the gamma range (40–100 Hz). Low frequency activity was averaged across all elec-trodes, gamma-band activity was averaged across parietal electrodes. Amplitude values areexpressed as signal changes relative to the 200 ms pre-stimulus baseline.

Cross-frequency coupling

To test the hypothesis if parietal gamma-band activity was mod-ulated by frontal theta-band activity (Osipova et al., 2006), we firstaveraged raw signals across electrodes with significant SMEs (seeFig. 2), i.e. right frontal electrodes for the theta phase signals andcentral parietal electrodes for the gamma amplitude signals. Thetheta phase signals were band-pass filtered from 5 to 8 Hz, andgamma amplitude signals were band-pass filtered from 50 to 80 Hzusing default EEGlab two-way least-squares FIR filtering procedures.Individual modulation indices were calculated for each participantsand condition. Fig. 3, left panel, illustrates phase-amplitude relationsof two exemplary participants as amplitude distributions for twocomplete phase cycles (0–720°). For participants P20, the amplitudedistributions for forgotten items (left) are more uniform than forremembered items (right), whereas for participant P17 both distri-butions appear to be largely comparable. More formally, the modula-tion indices for SR and SF conditions of all participants are plottedin Fig. 3, right panel. Data from the two exemplary participantshave been highlighted. For participants located above the diagonal(e.g., P20), the MI was greater in the remember condition than inthe forgotten condition. Confirming the visual inspection of theamplitude distribution, the MIs of participant P17 fall close to thediagonal. Overall, MIs in the remembered conditions were significantlylarger than in the forgotten condition (z=-2.37, pb0.02) indicatingenhanced cross-frequency phase-amplitude coupling between frontaltheta and parietal gamma oscillations during successful memoryencoding.

It is important to note that the differences in cross-frequencycoupling between conditions could be biased by power changes ofthe phase-modulating frequency. Since theta power was higher inthe SR condition, estimations of theta phase might be improved incomparison to the SF condition. Consequently, estimations ofcross-frequency coupling could be biased. To rule out this possibility,we assessed the relationship between MIs, theta power, and alsogamma power independently from the conditions as follows.Single-trial data of each participant were sorted regardless of condi-tion by mean theta power averaged across the selected electrodes.We divided the sorted data into quartiles such that the first quartile(Q1) contained 25% of all trials with lowest theta power, and theforth quartile (Q4) contained 25% of all trials with highest thetapower. For each quartile, we derived MIs, mean theta power aver-aged across frontal electrodes, and mean gamma power averagedacross parietal electrodes. Fig. 4A depicts the corresponding meansacross participants in quartiles of theta power (Q1 to Q4). Naturally,there is a noticeable increase of mean theta power (gray bars) butalso an increase of parietal gamma power (white bars) across quar-tiles. In contrast, MIs do not differ across quartiles (black squares).Corresponding 1-way analyses of variance across quartiles resultedin significant effects for theta power (F3,93=88.0, pb .01, lineartrend: F1,31=93.7, pb .01) and gamma power (F3,93=13.4, pb .01,linear trend: F1,31=19.1, pb .01), whereas no significant differencesacross quartiles were found for MIs (F3,93b1). Hence, it is unlikelythat our finding of increased cross-frequency coupling in the remem-bered versus the forgotten condition is due to theta power differ-ences between the conditions per se.

Lastly, to explore the cross-frequency coupling pattern at thelevel of individual electrodes, we chose the frontal electrodes forwhich theta power was larger for SR versus SF as seeds, and calculat-ed MIs between each seed electrode and all other scalp electrodes. InFig. 4B, electrode pairs associated with significant differences MISR−MISF (pb .01, two-tailed t-tests) are indicated by solid lines. (Nosignificant results were found for the opposite direction.) Theseresults show that with respect to the selected frontal region, thepattern of increased theta-gamma phase-amplitude coupling is in-deed confined to primarily parietal and occipital brain areas.

Forgotten Remembered

+ signs: electrodes with significant difference

Theta +/- 3, Alpha +/- 6, Gamma +/- 0.4 (µV2)

Remembered – Forgotten

Alpha-2.5 -4.0Theta/Gamma2.5 4.0T

Theta

Alpha

Gamma

Fig. 2. The left panel depicts subsequent memory effects in electrode space. Topographical maps of average oscillatory power are shown for theta-, alpha-, and gamma-band (400–1300 ms) in the forgotten and remember conditions. Amplitude values are expressed as signal change to the 200 ms pre-stimulus baseline. SMEs are associated with pb .01 atmarked electrodes. In the right panel, source space SMEs are illustrated as t-values (thresholded at pb .01) projected onto the surface of an average MRI template brain.

645U. Friese et al. / NeuroImage 66 (2013) 642–647

Discussion

This study provides first strong evidence for a functional linkbetween low- and high-frequency oscillations during the encoding ofnew memories. Phase-amplitude coupling between frontal theta oscil-lations and parietal gamma oscillations was found to be larger for sub-sequently remembered versus subsequently forgotten items. Thiscoupling might reflect a mechanism by which theta-mediated controlprocesses modulate gamma-band related processes implicated in the

Forgotten

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embered

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SR > SF

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Fig. 3. The cartoon head illustrates phase-amplitude coupling with frontal theta phase moduand forgotten conditions of all participants. Above the diagonal line, MIs are larger for remversus remember (SF>SR). The right panel shows phase-amplitude distributions for two cand remembered conditions. The extent of cross-frequency coupling can be inferred from d

reactivation and maintenance of neuronal object representations(Osipova et al., 2006; Palva et al., 2005).

Overall, our findings regarding SMEs in theta, alpha, and gammapower show considerable concordance with other EEG/MEG studies.Osipova et al. (2006), using a similar design in an MEG experiment,observed a gamma-band SME at posterior parietal and occipital sen-sors, as well as a theta-band SME at right temporal sensor sites. Inthat study, the source of the gamma-band SME could be located in vi-sual cortex whereas no reliable source was found for the theta SME.

parietalgamma

frontaltheta

Remembered

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plitude

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lating parietal gamma power. The left panel displays modulation indices for rememberember versus forgotten (SR>SF), below the diagonal line, MIs are larger for forgottenomplete phase cycles (0–720°) of the two participants (P17, and P20) in the forgotteneviations of the uniform distribution.

Frontal seed electrodes

Significant CFC:Remembered > Forgotten

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B

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er (µV2)

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I)

Q2 Q3 Q4

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Q1

Fig. 4. (A) Mean theta-gamma modulation indices (MI) and mean theta/gammapower in quartiles of theta power. Calculations were based on all trials across condi-tions sorted by theta power at frontal electrodes. Bars illustrate increase of power intheta band (gray) and gamma band (white) over quartiles (Q1-Q4). Black squares in-dicate that corresponding cross-frequency modulation indices (MI) do not increasewith theta power. (B) Theta-gamma phase-amplitude coupling was calculated be-tween frontal seed electrodes and all other scalp electrodes. Solid lines indicate sig-nificant cross-frequency coupling differences (SR>SF) between electrode pairs(pb0.01, uncorrected).

646 U. Friese et al. / NeuroImage 66 (2013) 642–647

We identified sources in posterior parietal cortex regions for thegamma-band SME and right frontal cortex for the theta-band SME.Previous studies on EEG gamma-band activity are difficult to inter-pret because of potential contamination with miniature eye move-ment artifacts which are not accounted for by traditional artifactremoval procedures (Yuval-Greenberg et al., 2008). Here, we usedthe COSTRAP-algorithm (Hassler et al., 2011) to detect and removethese artifacts. Furthermore, we matched the number of trials eachparticipant contributed to the analyses of SR and SF conditions, andwe found individual modulation indices to be uncorrelated to thesetrial numbers. Hence, different signal-to-noise ratios between condi-tions cannot explain the results. Although it is in principle possiblethat the phase-amplitude cross-frequency coupling found herecould also be caused by phase-phase frontal-posterior theta couplingin combination with phase-amplitude posterior theta-gamma cou-pling, inspections of the inverse solutionmaps (Fig. 2) do not suggestsuch a relationship (i.e., no significant theta activity in posteriorareas and no significant gamma activity in frontal areas). Moreover,differences between cross-frequency coupling were not biased bydifferences in theta power per se (Fig. 4A). Our findings are compat-ible with increased gamma-band activity in posterior cortex regionsthat have been observed in MEG studies during incidental learning(Friese et al., 2012; Kaiser et al., 2004; Osipova et al., 2006). Evidencefor theta-band SME in frontal areas has also been reported bySederberg et al. (2003) using intracranial EEG in epileptic patientsand by Hanslmayr et al. (2009) in an EEG study. Alpha-band SMEshave also been demonstrated in former investigations (Hanslmayret al., 2009; Klimesch et al., 1996b), as well as the co-occurrence ofEEG theta synchronization and alpha desynchronization duringmemory encoding (Klimesch et al., 1997; Molle et al., 2002).

The consistent findings of SMEs in different frequency rangesraise the question which different functional aspects might bereflected by each frequency band during memory encoding. Further-more, if certain cognitive processes are preferentially associatedwith oscillatory activity in a specific frequency band, what is the

functional significance of interactions between frequency bands? Astriking model of the role of cross-frequency interactions in workingmemory has been put forward by Lisman and Idiart (1995). Theypropose that objects are cortically represented by consecutivegamma cycles which are nested within a given theta cycle. Capacitylimitations of short-term memory then arise as a consequence ofthe number of gamma cycles that fit into one theta cycle. Supportingevidence for this model has been generated with invasive recordings inhumans (Axmacher et al., 2010) but also by some EEG/MEG studies.Gamma power was found to be correlated with theta phase in a work-ing memory task (Demiralp et al., 2007), and theta-gamma synchroni-zation was modulated by attention (Sauseng et al., 2008, 2009).Furthermore, a relationship between individual short-termmemory ca-pacity and individual theta/gamma cycle length ratios has been demon-strated (Kaminski et al., 2011). Beyond the working-memory domain,cross-frequency coupling between theta phase and gamma amplitudein various brain regions has also been found during word recognition(Mormann et al., 2005) and in a variety of cognitive tasks (Canolty etal., 2006). Cross-frequency coupling has also been detected in variousbrain regions of rodents (e.g., Tort et al., 2008, 2009) and nonhumanprimates (Lakatos et al., 2005, 2008; Tsunada et al., 2011). The diversityof tasks, brain areas, and species in which cross-frequency couplingshave been observed suggests that the interactions between oscillatoryactivity in different frequency bands serve universal mechanisms ofneuronal communication. Lakatos et al. (2005), e.g., propose thatcross-frequency couplings reflect a general mechanism by whichlower frequency oscillations modulate the exitability of local neuronalassemblies. Based on theoretical considerations and empirical findings,low frequency oscillations are likely to play an important role inlarge-scale communication between brain areas (Canolty and Knight,2010; Jensen and Colgin, 2007; Palva and Palva, 2007). Our results indi-cate that successful memory encoding is accompanied by increased in-teraction of frontal and parietal brain regions via theta-phase togamma-amplitude coupling. This finding is compatible with the ideathat during encoding frontal areas exert top-down influence on posteri-or cortex areas. Frontal cortex regions are thought to subserve encodingprocesses by the selection of goal-relevant information and by theintegration of different pieces of information in working memory(Blumenfeld and Ranganath, 2007). Gamma-band activity in posteriorcortex regions has been associatedwith processes related to cortical ob-ject representations (Tallon-Baudry et al., 1997). Within the network ofbrain areas that act in concert to facilitate memory encoding, these rep-resentations in posterior regions might reflect the informational coun-terpart of the frontal control system. Although in agreement with thelines of evidence stated above, these interpretations should neverthe-less be considered with caution. Measures of cross-frequency couplingare descriptive and do not necessarily reflect causal relations betweenoscillations in different frequency bands. Future research will be neces-sary to establish the causal links and underlying mechanisms ofcross-frequency phase-amplitude coupling.

Conclusion

Our study is the first to demonstrate increased cross-frequency cou-pling between EEG-recorded frontal theta and parietal/occipital gammaoscillations during the formation of newmemories. Presumably, this cou-pling reflects interactions between a frontal control system and corticalrepresentations activated and maintained in posterior brain regions.

Acknowledgments

This study was supported by grants to T.G. from the German Re-search Foundation (DFG). We are grateful to three anonymous re-viewers for their constructive criticism.

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