Functional connectivity of gamma EEG activity is modulated at low frequency during conscious...

International Journal of Psychophysiology 46(2002) 91–100

0167-8760/02/$ - see front matter� 2002 Elsevier Science B.V. All rights reserved.PII: S0167-8760Ž02.00108-3

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Functional connectivity of gamma EEG activity is modulated atlow frequency during conscious recollection

Adrian P. Burgess*, Lia Ali

Division of Neuroscience and Psychological Medicine, Imperial College School of Science, Technology and Medicine,St. Dunstan’s Road, London W6 8RP, UK

Received 2 September 2002; received in revised form 4 September 2002; accepted 5 September 2002

Abstract

We examined two subjectively distinct memory states that are elicited during recognition memory in humans andcompared them in terms of the gamma oscillations(20–60 Hz) in the electroencepahalogram(EEG) that theyinduced. These subjective states, ‘recollection’ and ‘familiarity’ both entail correct recognition but one involves aclear and conscious recollection of the event including memory for contextual detail whilst the other involves a senseof familiarity without clear recollection. Here we show that during a verbal recognition memory test, the subjectiveexperience of ‘recollection’ induced higher amplitude gamma oscillations than the subjective experience of ‘familiarity’in the time period 300–500 ms after stimulus presentation.Recollection, but notfamiliarity, was also associated withgreater functional connectivity in the gamma frequency range between frontal and parietal sites. Furthermore, themagnitude of the gamma functional connectivity varied over time and was modulated at 3 Hz. Previous studies inanimals have shown local theta frequency modulation(3–7 Hz) of gamma-oscillations but this is the first time thata similar effect has been reported in the human EEG.� 2002 Elsevier Science B.V. All rights reserved.

Keywords: Recollection; Gamma oscillations; Electroencepahalogram

1. Introduction

Functional connectivity mediated through syn-chronous oscillations in the gamma frequencyrange(20–60 Hz) has been proposed as a mech-anism that binds our disparate cognitive processesinto a unitary conscious experience(Freeman,

*Corresponding author. Division of Neuroscience and Psy-chological Medicine, Imperial College Faculty of Medicine,St. Dunstan’s Road, London W6 8RP, UK. Tel.:q44-20-8846-7343; fax:q44-20-8846-1670.

E-mail address: [email protected](A.P. Burgess).

1975; Damasio, 1990; Varela, 1995; Singer andGray, 1995). Most electroencepahalogram(EEG)studies of gamma oscillations in humans havefocused on visual perception(Sauve, 1999; Sum-merfield et al., 2002) but although some recentstudies have considered working memory(Tallon-Baudry et al., 1998, 1999; De Pascalis and Ray,1998; Sarnthein et al., 1998), to date, none haveinvestigated episodic memory. Furthermore, inmost cases these experiments have reported localchanges in gamma power rather than functionalconnectivity between scalp regions, although there

92 A.P. Burgess, L. Ali / International Journal of Psychophysiology 46 (2002) 91–100

have been some notable exceptions(Sarnthein etal., 1998; Rodriguez et al., 1999; Miltner et al.,1999). The aim of this study was to investigatechanges in both the amplitude of local gammaoscillations and the functional connectivitybetween electrode sites during an episodic memorytask.The suggestion that recognition memory can

give rise to two distinct conscious states, ‘recol-lection’ and ‘familiarity’ (Mandler, 1980), oftenoperationalised to ‘R’ and ‘K’ for ‘recognise’ and‘know’ (Tulving, 1985), has been one of consid-erable interest over recent years. The issue hasbeen studied extensively using behavioural(Gar-diner and Java, 1990), event-related potential(Smith, 1993) and functional imaging methods(Henson et al., 1999) but this is the first time thatdifferences in the functional connectivity betweenthe two conditions have been considered. Mostindividuals are able to make the distinctionbetweenrecollection and familiarity without diffi-culty and the validity of the subjective distinctionhas been shown by the superior recall of contextualdetail for recollection responses(Wilding andRugg, 1996). Furthermore, the recollection-famil-iarity distinction is not simply a measure of con-fidence and can be dissociated from guessing(Gardiner and Java, 1990). For recollection tooccur, the components of the perceptual experiencethat are to be remembered must be reconstructedand brought to mind in a way that is comparableto perceptual feature binding. Given the proposedrole of gamma oscillations in feature binding(Freeman, 1975; Damasio, 1990; Varela, 1995;Singer and Gray, 1995), we hypothesised(i) therewould be greater induced gamma amplitude in therecollection than thefamiliarity condition and(ii)greater functional connectivity in the gamma fre-quency range forrecollection than for familiarity.

2. Methods

2.1. Subjects

Ten right-handed participants(five men, fivewomen) aged 20–23 were tested. None had ahistory of neurological or psychiatric disease. Priorto commencing the study, ethical approval was

obtained and all participants provided informed,written consent.

2.2. Procedure

Subjects were seated in a sound-attenuated andelectrically shielded testing chamber and asked tocomplete a continuous recognition memory test forwords. Participants were shown a series of wordsand were required to indicate whether the wordwas one that was being shown for the first timeduring the test(‘New’) or was being repeated(‘Old’ ).The words were presented to the subject one at

a time following the sequence of events shown inFig. 1. The baseline period consisted of the pres-entation of a fixation point marked with a ‘q’ inthe centre of the screen for 500 ms before stimulusonset. The memory stimulus was presented cen-trally for 1500 ms and followed by the responsecue. No motor response was required during thetime the memory stimulus was presented. Theresponse cue had the words ‘New’ and ‘Old’placed on either the left or the right hand side ofthe screen. The participant was required to respondby pressing either the left or right hand button ofa control box to indicate whether the stimulus hadnot been seen before or was a repeat. The side of‘Old’ and ‘New’ changed randomly between trialsto prevent the participants anticipating the side ofmotor response. 1500 ms was allowed for theresponse and responses which occurred after thistime were scored as errors. If the subject indicatedthat the word was ‘NEW’ then there was an inter-trial interval of 3500 ms before the commencementof the next baseline period. If the subject indicatedthat the word was ‘OLD’ then ‘R?K’ appeared onthe screen and they were asked to make a recol-lectionyfamiliarity judgement. The subject wasrequired to press the left hand control button ifthey had experienced a recollection response andthe right hand button if the experienced a famil-iarity response. The R–K judgement was followedby an inter-trial interval of 1500 ms during whichtime the screen was blank.

2.3. Stimuli

Words were chosen from published lists(Paivioet al., 1968; Gilhooly and Logie, 1980; Friendly

93A.P. Burgess, L. Ali / International Journal of Psychophysiology 46 (2002) 91–100

Fig. 1. The procedure for the recognition memory test involved the presentation of(i) baseline period(ii) stimulus presentation(iii ) response cue. Presentation times are indicated on the figure and are timed from the start of the stimulus presentation period.For words identified as ‘Old’ the subjects were required to judge whether they had experienced a recollection(‘R’ ) or familiarity(‘K’ ) response. Inter-stimulus interval was the same for both ‘New’ and ‘Old’ trials.

et al., 1982) and were 5–7 letters of moderateimageability and concreteness. Mean imageabilitywas 4.0(S.D.s1.3) on a 7-point rating scale with7 indicating high imageability. Mean concretenesswas 4.3 (S.D.s1.3), again on a 7-point ratingscale where 7 indicated high concreteness. Meanfrequency on the Thorndike–Lorge count(Thorn-dike and Lorge, 1944) was 78 per million(range1–414).

2.4. Electroencepahalogram recording

EEG was recorded from 28 scalp sites using aECI electrode cap fitted to the subject’s head anda ground electrode was placed 1.5 cm anterior tothe vertex. Vertical and horizontal electrodes wereplaced above and below the right eye and laterallyfrom both eyes. A reference electrode was placedon the left ear but all EEG data were converted toaverage reference prior to analysis. Recording anddigitisation were carried out using a NeuroscanSynamps amplifier with signal bandpass 0.15–70Hz with a sampling rate of 500 Hz. The resultsreported below concern data from nine electrodesites: the F3, Fz, F4, C3, Cz, C4, P3, Pz and P4.

2.5. Time–frequency analysis

After exclusion of epochs contaminated withartifact the EEG was subjected to a wavelet anal-

ysis. This produced a time–frequency decomposi-tion giving estimates of signal amplitude(i.e.square root of power) across the gamma frequencyrange with high temporal resolution.The wavelet analysis involved the convolution

of the EEG by complex Morlet’s wavelets thathave a Gaussian shape in both the time andfrequency domain(Torrence and Compo, 1997).Wavelets were normalised so that their total energywas 1 and the internal frequency,v , was 5.336.0

The wavelet convolution provided a real and imag-inary amplitude and the modulus of these valueswas taken as the amplitude of induced gamma atthat time and frequency point. Amplitude was usedrather than power as it followed a near normaldistribution. The frequency range analysed was20–60 Hz with 8 wavelet convolutions per octave.The wavelet amplitude at each frequency wasbaseline corrected. This was done by calculatingthe mean amplitude in the period immediatelyprior to stimulus onset that was equivalent to onecomplete oscillation at that frequency. That is, at20 Hz, the baseline period wasy50 to 0 msperiod and at 60 Hz, it wasy16.7 to 0 msinterval.In order to improve the signal to noise ratio,

data were smoothed across time and frequencyusing a Gaussian filter with full-width half maxi-


mum of 0.25 octaves in the frequency dimensionand 0.25 times the period of an oscillation in thetime domain.For comparison of the mean gamma amplitude

in the recollection and familiarity conditions,amplitude was calculated before averaging acrosstrials. As this included both phase-locked and non-phase-locked components of the EEG signal, theresulting amplitude values are ‘induced’ rather than‘evoked’ gamma amplitudes.

2.6. Statistical analysis

The gamma amplitudes in therecollection andfamiliarity conditions were compared at each ofthe nine electrode sites using at-test. T-valueswere calculated at each time–frequency point. Foreach electrode site, 1914t-tests were required tocover the 20–60 Hz and 0–1000 ms time–fre-quency range and, uncorrected, this would havelead to an inflated Type-1 error. To overcome thisproblem, an adaptation of a method devised tocontrol the Type-1 error in Topographical Mappingof EEG was used was used(Burgess and Gruzelier,1999) that has recently been adapted for use withtime–frequency plots(Summerfield et al., 2002).This meant that the probability values reported inthe results have been corrected for multiple com-parisons are realistic estimates of the trueP-values.Statistical comparisons between the ‘R’ and ‘K’conditions were one-tailed.

2.7. Covariance analysis

Functional connectivity can be measured inseveral ways but in this study we used the covar-iance in gamma amplitude between electrode sites.Mean EEG amplitude in the 20–60 Hz range wascalculated in 50 ms windows from 0 to 1000 msafter the presentation of the word at each of the 9electrode sites. The data from each artifact-freeepoch were pooled across subjects giving a totalof 582 epochs for therecollection condition and201 for familiarity. The covariance structurebetween electrode sites was modelled separatelyfor each time period usingAMOS ver 3.6(Arbuck-le, 1997). This meant that there were 20 covariancemodels for each of therecollection and familiarity

conditions, each representing the covariances in a50 ms time period between 0 and 1000 ms.The aim of the modelling procedure was to find

the simplest model that could account for thepattern of covariances observed where simplestwas defined as the model with the fewest signifi-cant covariance pathways. The modelling involveda forward stepwise approach and proceeded asfollows. The initial model assumed that there wereno significant covariances between electrode sitesand this was tested against the data. If this modelcould be improved by including additional covar-iance pathways, then the one that resulted in thegreatest improvement of model fit was added. Theaddition of covariance pathways to the model wasguided by the use of a standard modification index(Soerbom, 1989) that showed the expectedimprovement in the fit of the model that could beobtained from the addition of each of the possiblecovariance paths that were not already included.This model in turn was tested and the procedurerepeated until no further improvements in themodel could be obtained. At this point, the stan-dard error of each covariance pathway was esti-mated using a bootstrap method with 1000replications. Bootstrapping provided a robust esti-mate of the reliability of the covariances obtainedand any pathways that were not statistically sig-nificant under this procedure(P-0.05) were omit-ted from the final model. The modelling procedurewas performed for each time point and for bothrecollection and familiarity conditions separately.The covariance models were compared across

all time points and the frequency with which eachcovariance pathway was found to be a necessarycomponent of the models was compared betweenthe recollection and familiarity conditions usingx .2

3. Results

Performance on the memory tests was good and78% of repeated stimuli were correctly identified.Of these, 74% were rated as beingrecollectionexperiences. The results reported concern onlythose responses where the subject correctly iden-tified a word as one that had been seen previously


Fig. 2. Shaded regions indicate the time–frequency areas where there were statistically reliable differences(P-0.05, one-tailed)between the ‘R’ and ‘K’ conditions were seen at each electrode site. The time–frequency coverage includes the 20–60 Hz frequencyregion during the first 1000 ms of stimulus presentation. Type-1 error was controlled for multiple comparisons using a randomizationprocedure.

(i.e. ‘True Old’) and also made a recollection or afamiliarity judgement.

3.1. Topographical analysis

The statistical comparison of the recollectionand familiarity conditions at each of the nineelectrode sites are shown in Fig. 2. Statisticallysignificant differences between the conditions wereseen at F3, Fz, F4, P3, Pz and P4. In each casethe differences occurred from approximately 250to 500 ms with greater gamma amplitude occurringin the recollection condition. The differences werein the 30–50 Hz range although the largest regionof difference was at F3 between 20 and 30 Hz.

3.2. Covariance analysis

The covariance modelling procedure found thatsome covariance pathways between electrode sites

were significant in nearly all models examined butas these were exclusively between adjacent elec-trode sites, the most parsimonious explanation isthat they were due to volume conduction. Othercovariance pathways were significantly more com-mon for recollection than for familiarity and theseare shown in Fig. 3. No covariance pathways weresignificantly more common in thefamiliaritycondition.The main difference between therecollection

and familiarity covariance models was the appear-ance of long-distance fronto-parietal covariancesin the recollection condition. This we interpretedas indicating a greater transfer of information inthe gamma frequency range between frontal andparietal sites during therecollection condition.This is exactly what would be expected if featurebinding were mediated via functional connectivity


Fig. 3. Covariance pathways that were significantly more oftenrequired to produce a good fit to the covariance structure inthe ‘recollection’ condition than in the ‘familiarity’ models.The frequency of significant and non-significant pathways inthe 20 time periods from 0 to 1000 ms for the ‘recollection’and ‘familiarity’ conditions was compared byx . Only path-2

ways that were significantly more common in the recollectioncondition(P-0.01) are shown.

Fig. 4. Upper panel(a) shows the sum of the fronto-parietalcovariances over time from 0 to 1000 ms. Clear peaks in thedistribution of the time series can be seen at 150 ms, 500 and850 ms suggesting a frequency of 2.5–3.3 Hz. The lower panel(b) shows a spectrogram of the time series showing a peak at3 Hz. The spectrogram was calculated using the FFT. Reshuf-fling the data and recalculating the FFT 10 000 times indicatedthat the magnitude of the observed peak was unlikely to haveoccurred by chance(P-0.013).

in the gamma frequency range. What was notexpected was that there was clear variation overtime in the magnitude of the fronto-parietal covar-iances(Fig. 4a). For thefamiliarity condition, thecovariances were consistently low and showed noobvious pattern of variation. In contrast, the covar-iances for therecollection condition showed aclear oscillation with peaks at 150, 500 and 850ms indicating a frequency of approximately 3 Hz(2.5–3.3 Hz). This was confirmed when we sub-jected the time series in Fig. 4a to a fast Fouriertransform(FFT) and this showed a clear peak at3 Hz (Fig. 4b). To determine whether the magni-tude of the 3 Hz peak was significantly greaterthan might be expected by chance, we randomlyshuffled the time series and recomputed the FFT10 000 times. Of the 10 000 re-samplings, themagnitude of the observed 3 Hz peak was exceed-

ed only 134 times at any frequency indicating thatthe probability of finding a FFT coefficient bychance as great as that observed was-0.013.

4. Discussion

Our results show that memory conditions thatdiffer only in the richness of their subjectiveexperience show distinct patterns of functionalconnectivity in the gamma frequency range. Thefact that the richer memory experience was asso-


ciated with both greater gamma amplitude andgreater functional connectivity supports thehypothesis that gamma oscillations are associatedwith feature binding(Freeman, 1975; Damasio,1990; Varela, 1995; Singer and Gray, 1995).The suggestion that synchronous gamma oscil-

lations have a role in feature binding is supportedby a growing body of evidence but there is astronger version of the hypothesis that is morecontroversial and this proposes that synchronousgamma oscillations are a neural correlate of con-sciousness(Crick and Koch, 1990; Llinas et al.,1998). As the only difference between recollectionand familiarity in this study was the subjectiveexperience of the participants involved, the resultsreported here are directly relevant to this conjecturetoo. Unfortunately, in this case, the hypotheses thatcan be derived from the feature binding and theneural correlate of consciousness models are iden-tical and, consequently, our data do not provideany method of discriminating between the two.The largest region of difference between the

recollection and familiarity conditions was at aleft-frontal site(F3). The timing of this difference(300–600 ms) is consistent with previous studiesof gamma activity during complex cognitive tasks(Pulvermuller et al., 1997) and most of the otherregions of difference also occurred during this timeperiod. The frequency of the difference at F3(20–30 Hz) is lower than most previous reports butthere is a strong tendency for complex cognitivetasks to elicit changes in the lower part of thegamma frequency range(Pulvermuller et al.,1997). The reason for this remains uncertain butit may reflect the size and distribution of the cellassemblies involved. Complex cognitive tasksinvolving larger, more dispersed neural assemblieswill require longer conduction times and henceslower oscillations.Although inferences about localisation using

EEG should be made with caution, it is noteworthythat the left-frontal region was one of the fewbrain regions that showed greater regional cerebralblood flow during recollection compared withfamiliarity in an fMRI experiment using a similarexperimental design(Henson et al., 1999).Although much recent work on cortical oscilla-

tions has emphasised the role of gamma oscilla-

tions Basar et al. have long argued that oscillatoryactivity in the delta, theta and alpha frequencyranges is also critically important(Basar, 1976,¸1980; Basar et al., 2000, 2001). The relationship¸between oscillatory networks at these differentfrequency ranges has been relatively neglected,however. The main exception has been the rela-tionship between gamma and theta oscillationswhich has attracted considerable interest, particu-larly with regard to memory functioning as bothfrequencies are generated in the hippocampus(Green and Arduini, 1954; Stumpf, 1965), a struc-ture strongly implicated in episodic memory(Squire and Zola-Morgan, 1991). Computationalmodels of hippocampal function have been pro-posed in which gamma oscillations occur in burstsnested within the slower theta rhythm(Lismanand Idiart, 1995; Jensen and Lisman, 1996a,b) andthis prediction has been confirmed by electrophy-siological recordings from the hippocampus, entor-hinal cortex and neocortex of rats(Bragin et al.,1995; Chrobak and Buzsaki, 1998; Fisahn et al.,1998; Penttonen et al., 1998). As far as we areaware, however, the present study provides thefirst evidence that functional connectivity in thegamma frequency range in humans has been shownto display a similar pattern of modulation.Although previous studies in humans investigat-

ing functional connectivity in the gamma-frequen-cy range have not found a similar relationship(Sarnthein et al., 1998; Rodriguez et al., 1999;Miltner et al., 1999) it is likely that this is due todifferences in the measures of functional connec-tivity used, the time windows considered or totask-specific factors. There is evidence, however,that there is a relationship between gamma andlow frequency oscillations in humans recorded atthe same electrode site. Schack et al.(2002) usinga modified Sternberg paradigm found increasedbicoherence between the gamma and theta fre-quency ranges at frontal electrode sites.Our findings also relate to von Stein and Sarnth-

ein’s (2000) proposal that there is a relationshipbetween the extent of functional synchronisationand the frequency at which that synchronisationoccurs. Specifically, they argue that local synchron-isation occurs in the gamma frequency range whilstlonger distance synchronisation occurs at lower


frequencies and is predominantly associated with‘top–down’ processes. Given that von Stein andSarnthein’s(2000) evidence was based on coher-ence during epochs of several seconds duration,any low-frequency modulation of gamma in theirstudies would not have been detected. Our results,together with Schack et al.’s(2002) are not incom-patible with von Stein and Sarnthein’s(2000)proposal but they do suggest that the relationshipbetween gamma and lower frequency oscillationsmay be more complex than they initially proposed.Whilst we do not claim that scalp recorded EEG

in humans directly reflects activity in the hippo-campus we do speculate that the oscillatory natureof the functional connectivity seen here is ulti-mately derived from hippocampal–neocorticalinteractions. As the output from the hippocampalformation through layer IV of the entorhinal cortexdoes not display theta oscillations in rats(Buzsaki,1996) we suggest that critical hippocampal–neo-cortical interaction may be from the hippocampusto the anterior cingulate via the anterior thalamus.The suggestion is supported by the fact that themidline frontal theta rhythm(Fmu) seen inhumans during many cognitive tasks(Ishihara andYoshi, 1972; Mizuki et al., 1980; Sasaki et al.,1994; Lang et al., 1987; Laukka et al., 1995)including recognition memory(Klimesch et al.,1996; Burgess and Gruzelier, 1997, 2000) is nowbelieved to originate in the anterior cingulatecortex (Gevins et al., 1997; Ishii et al., 1999;Asada et al., 1999). Unfortunately, our data do notdirectly address this issue and it is one that canonly be answered by simultaneous electrophysiol-ogical recordings from hippocampus and cortex.Nevertheless, our results show that low frequencymodulation of gamma-oscillations is a more gen-eral phenomenon than previously thought andapplies not only to single cells but also to longdistance interactions between cortical regions inhumans as well as animals.

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Functional connectivity of gamma EEG activity is modulated at low frequency during conscious...

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