CHA

Neuper & Klimesch (Eds.)

Progress in Brain Research, Vol. 159

ISSN 0079-6123

Copyright r 2006 Elsevier B.V. All rights reserved

PTER 4

Principles of oscillatory brain dynamics and a treatiseof recognition of faces and facial expressions

Erol Bas-ar1,�, Bahar Guntekin1,2 and Adile Oniz3

1Faculty of Science and Letters, Istanbul Kultur University, Istanbul, Turkey2TUBITAK BAYG, Ankara, Turkey

3Dokuz Eylul University, Faculty of Medicine, Department of Biophysics, Izmir, Turkey

Abstract: The research of event-related oscillations is one of fast-growing fields in neuroscience. In thisstudy, a theory of the ‘‘whole-brain-work,’’ which can be useful for functional interpretation of brainoscillations, is presented together with its application to recognition of faces and facial expressions. Fol-lowing results are summarized: (1) Mechanisms leading to the perception of the grandmother picture aremanifested with parallel activations of neural assemblies in different cortical locations and as superpositionof delta, theta, alpha, beta, and gamma oscillations. Known and anonymous faces can be differentiated bymeans of oscillatory brain dynamics. Percepts cannot be localized in a given specific region. The differ-entiation of facial expression induces significant change in alpha and theta oscillation. (2) While theimportance of fMRI in object recognition is clear, this method has low temporal resolution. Our resultsshows that multiple brain oscillations clearly differentiate the known and unknown faces with varieddegrees of selective-responsiveness in a short time window between 0 and 800 ms, thus completing andimplementing the analysis of percepts in the dynamic window and indicating a broader distribution at thecortex. (3) The presented evidence of selectively distributed multiple oscillations for differentiation of facialpercepts is in conceptual accordance with the ‘‘selectively distributed processing’’ in neurocognitive net-works of Goldman-Rakic, Fuster, and of Mesulam. The large-scale approach of several investigators is alsoconfirmed with the new results. On facial stimuli, a given location can show a considerable selectedactivation, but the formation of percepts is manifested by multiple oscillations with differentiated weight inlarge neural populations. (4) The most important feature of the comparison of percepts of grandmotherand anonymous faces is the existence of a variety of significant differences in delta, theta, alpha, beta, andgamma responses between the anonymous and grandmother faces in frontal, central, parietal, temporal,and occipital sites. (5) The brain response is a construct in a multi-dimensional state manifested by am-plitudes of oscillatory responses, topological coordinates, and changes in the time axis following presen-tation of the percepts including delays and prolongations, coherence between locations. Only a new metricsembracing all these parameters can be representative for dynamics of functionality in the brain. Theconceptual aspects of this new scope are explained in the presented theory.

Keywords: face recognition; facial expressions; brain oscillations; delta; theta; alpha; beta; gamma; phaselocking; event-related oscillations; memory; brain theory

�Corresponding author. E-mail: erol.basar@deu.edu.tr

DOI: 10.1016/S0079-6123(06)59004-1 43

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Introduction: aims of the study

According to Mountcastle (1992, 1998), the par-adigm change introduced by using brain oscilla-tions became one of the most importantconceptual and analytic tools for the understand-ing of cognitive processes. Mountcastle furtherstated that a major task for neuroscience is to de-vise ways to study and to analyze the activity ofdistributed systems in waking brains, includingparticularly human brains. According to Luria(1966) mental functions too are similar to vegeta-tive functions, a product of complex systems, anda component part, which may be distributedthrough the structures of the brain. The task ofneuroscience is, therefore. not to localize ‘‘cen-ters,’’ but rather, to identify the components of thevarious complex systems that interact to generatethe mental functions. Luria called this task ‘‘dy-namic localization.’’ A recent study tested the pos-sible interplay between the working and long-termmemory systems and indicated the relevance ofthis dynamic localization (Sauseng et al., 2002). Ina similar context, Lashley (1929) proposed thatmemories are in fact scattered across the entirebrain rather than being concentrated in specificregions.

As a consequence of this chain of reasoning, theanalytical and conceptual framework of thepresent study is premised on the methodologicaladvices of Mountcastle and the conceptual state-ments of Luria and Lashley.

Currently, the emphasis given to this branch ofneuroscience is growing fast, and publications re-lated to brain oscillations invade the neuroscienceliterature. However, there are several misleadingsand several unsatisfactory interpretations relatedto brain functions in this important new area ofneuroscience. Lord Kelvin (1880) indicated ‘‘All

science is measurement, but all measurement is not

science.’’ Missing of some common rules and gen-eral principles in understanding of brain oscilla-tions may lead to more erroneous interpretationsand may cause damages in this new emerging field.Therefore, ‘‘establishing principles and rules’’ tounderstand brain oscillations becomes an impor-tant issue. At the beginning of the present study,we will present and explain some general rules

derived from our empirical work of our laborato-ries in the last 35 years.

In the second part of this article, we will presentinitial findings related to face recognition, differ-entiation of semantic and episodic memory, anddifferentiation of facial expressions by using theapproach with the oscillatory dynamics by takinginto account the impact of the theory, which willbe described in the first part.

In the Sherringtonian view, ‘‘the grandmother

neuron’’ is defined as a neuron, which responds tonothing else but the face of one’s grandmother.According to Barlow’s (1995) concept, we wouldhave a specific neuron in the brain firing while see-ing the face of a particular grandmother. Followingthe relevant work of Eckhorn et al. (1988) andGray and Singer (1989) on gamma oscillations,Stryker (1989) raised the question ‘‘Is grandmother

an oscillation?’’ by commenting that neurons in thevisual cortex activated by the same object tend todischarge rhythmically and in unison.

In the analysis of the grandmother percept, theexperimenter is confronted with the process of faceprocessing, which comprises (i) perceptual andmemory processes required for the recognition ofcomplex stimulation as a face, (ii) the identificationof the particular face in view (here the grand-mother), (iii) the analysis of facial expression(McCarthy, 2000), and (iv) the concept of dynam-ics in integrative brain function. In addition to theprocesses pointed out, face recognition requiresintegration of attention, perception, learning, andmemory. Recent publications favor the idea thatattention, perception, learning, and memory areinseparable as described by Hayek (1952) (see alsoDamasio, 1994; Baddeley, 1996; Desimone, 1996;Fuster, 1997; Bas-ar, 2004) Therefore, face recog-nition can be considered as a prototype of process-ing complex signals by the brain.

The first results related to selectively distributedand selectively coherent multiple oscillatory re-sponses as large-scale approach were described byBas-ar et al. (1975) and Bas-ar (1980) in the cat andhuman brains (for reviews see Bas-ar, 1999;Bas-aret al., 2001a). In the last years, the large-scale hy-pothesis has became also a keyword with increas-ing number of publications Bressler and Kelso(2001), von Stein and Sarnthein (2000), Varela

45

et al. (2001), Makeig et al. (2002), Fell et al. (2001),and Mesulam (1994).

PART I: PRINCIPLES OF OSCILLATORY

BRAIN DYNAMICS

The theory of the whole-brain-work: an approach to

brain function by means of EEG-oscillations

Chronological evolution of our conceptual frame-work evolved in the last 20–25 years and is basedon empirical foundations from several laborato-ries.

The theory of the whole-brain-work proposesthat integrative brain function is based on the co-existence and cooperative action of many inter-woven and interacting sub-mechanisms. In itsextension, the theory includes mechanisms thatconsist of supersynergy, superbinding, and recipro-

cal interaction of attention, perception, learning,

and remembering (APLR-alliance).The mentioned mechanisms were grouped in the

following under four structural and/or functionallevels.

Level A: from single neurons to oscillatory dynamicsof neural populations

(1)

The neuron is the basic signaling element ofthe brain.

(2)

Since morphologically different neurons orneural networks are excitable on sensory-cognitive stimulation, the type of the neu-ronal assembly does not play a major rolein the frequency tuning of oscillatory net-works. Research has shown that neuralpopulations in the cerebral cortex,

hippocampus, and cerebellum are all tunedto the very same frequency ranges, al-though these structures have completelydifferent neural organizations (Eckhorn etal., 1988; Llinas, 1988; Singer, 1989;Steriade et al., 1992; Bas-ar, 1998; 1999). Itis therefore suggested that all brain net-works communicate by means of the sameset of frequency codes of EEG oscillations.

(3)

Intrinsic oscillatory activity of single neuronsforms the basis of the natural frequencies of

neural assemblies. Oscillatory activity of theneural assemblies or the brain consists of thedelta, theta, alpha, beta, and gamma frequen-cies. These frequencies are the natural fre-quencies and thus the real responses of thebrain (Bas-ar et al., 2001a,b).

(4)

Feature detectors (Sokolov, 2001), place

cells, and memory cells (Fuster, 1995) areempirically established neural elements.However, a crucial turning point occurredwith the so-called ‘‘grandmother’’ experi-ments showing that large groups of neuralpopulations were selectively activated uponcomplex semantic and episodic inputs tothe brain and that complex percepts cannotbe processed only by means of cardinalcells (Edelman, 1978; Bullock, 1992; Bas-ar,2004, and experiments described in‘‘Grandmother experiments’’). Theseexperiments and other similar studiesreplaced the functional role of the singleneurons with neural assemblies in attemptsto describe the integrative functions of thebrain (Bas-ar et al., 2001a). The emphasison neural assemblies is the major point,which differentiates our theory fromSherrington’s ‘‘neuron doctrine’’ and Bar-low’s ‘‘new perception doctrine’’ (Barlow,1995).

(5)

Sokolov (2001) has excellently describedand also constructively criticized the role offeature detectors. However, integrativefunctioning of the brain needs the selec-tively distributed and selectively coherentneural populations in concert with the fea-ture detectors.

(6)

The brain has response susceptibilities.These susceptibilities mostly originate fromits intrinsic rhythmic activity, i.e., its spon-taneous activity (Bas-ar,1980,1983a,b;Narici et al., 1990; Bas-ar et al., 1992). Abrain system responds to external or inter-nal stimuli with those rhythms or frequencycomponents that are among its intrinsic(natural) rhythms. Accordingly, if a givenfrequency range does not exist in its spon-taneous activity, it will also be absent in theevoked activity. Conversely, if activity in a

46

given frequency range does not exist in theevoked activity, it will also be absent in thespontaneous activity.

(7)

There is an inverse relation between EEGand event-related potentials. The amplitudeof the EEG thus serves as a control pa-rameter for responsiveness of the brain,which can be obtained in the form ofevoked potentials or event-related poten-tials (Rahn and Bas-ar, 1993; Bas-ar, 1998;Barry et al., 2003; Bas-ar et al., 2003;).

(8)

The EEG is a quasi-deterministic or achaotic signal and should not be consideredas simple background noise. This charac-teristic and the concept of response suscep-tibility lead to the conclusion that theoscillatory activity that form the EEGgoverns the most general transfer functionsin the brain (Bas-ar, 1990).

(9)

Oscillatory neural tissues that are selec-tively distributed in the whole brain areactivated on sensory-cognitive input. Theoscillatory activity of neural tissues may bedescribed through a number of responseparameters. Different tasks and the func-tions that they elicit are represented by dif-ferent configuration of parameters. Owingto this characteristic, the same frequencyrange is used in the brain to perform notjust one but multiple functions. The re-sponse parameters of the oscillatory activ-ity is as follows: enhancement (amplitude),delay (latency), blocking or desynchroniza-tion, prolongation (duration), degree of co-herence between different oscillations, anddegree of entropy (Pfurtscheller et al., 1997,2006; Neuper et al. 1998a,b; Bas-ar et al.,1999a, b; Miltner et al., 1999; Pfurtschellerand Lopes da Silva, 1999; Schurmannet al., 2000; Kocsis et al., 2001; Pfurtscheller,2001; Rosso et al., 2001, 2002; Bas-ar, 2004).

(10)

The number of oscillations and the ensem-ble of parameters that are obtained undera given condition increase as the complex-ity of the stimulus increases or the recog-nition of the stimulus becomes difficult(Bas-ar, 1980, 1999; Bas-ar et al., 2000,2001a).

Level B: supersynergy of neural assemblies

According to the theory of whole-brain-work, super-synergy consists of the following sub-mechanisms:

(11)

In simple binding, there is temporal coher-ence between cells in cortical columns. Thishas been demonstrated by several authors(Eckhorn et al., 1988; Gray and Singer,1989).

(12)

Each function is represented in the brain bythe superposition of the oscillations invarious frequency ranges. The values ofthe oscillations vary on a number of re-sponse parameters (Principle 9). The com-parative polarity and phase angle ofdifferent oscillations are decisive in pro-ducing function-specific configurations.Neuron assemblies do not obey the all or

none rule that the single neurons obey(Karakas- et al., 2000a, b; Klimesch et al.,2000a, b; Chen and Herrmann, 2001).

(13)

The superposition principle indicates syn-ergy between the alpha, beta, gamma,theta, and delta oscillations during per-formance of sensory-cognitive tasks. Thus,according to the superposition principle,integrative brain function is obtainedthrough the combined action of multipleoscillations (see also Sections ‘‘Grand-mother experiments’’ and ‘‘Analysis of fa-cial expressions’’).

(14)

The response susceptibility of the brain acti-vates resonant communications in the brainby facilitating electrical processing betweennetworks (Bas-ar et al., 1997a,b; Bas-ar, 2004).This could be also interpreted as a generaltuning process between neural populationsand feature detectors (Sokolov, 2001).

(15)

Parallel processing in the brain shows se-lectivity. The selectivity in parallel process-ing is produced by variations in the degreeof spatial coherences that occur over longdistances between brain structures/neuralassemblies (Bas-ar, 1980, 1983a, b; Bas-ar etal., 1999a; Miltner et al., 1999; Schurmannet al., 2000; Kocsis et al., 2001).

(16)

Temporal and spatial changes of entropy inthe brain demonstrate that the oscillatory

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activity is a controlling factor in the func-tions of the brain (Graben et al., 2000;Graben, 2001; Quiroga et al., 2001; Yorda-nova et al., 2002).

(17)

The superbinding can be denoted, accordingto the foregoing explanations, as an ensem-ble of mechanisms consisting of ‘‘superpo-sition, activation of selectively distributedoscillatory systems, and the existence of se-lectively distributed long-distance coher-ences.’’ The concept of supersynergy

includes superbinding and, additionally, en-tropy and the role of EEG-oscillations ascontrol parameter in brain’s responsiveness.

Level C: integration of attention, perception,learning, and remembering

Extension of the theory of whole-brain-work tocognitive processing is governed by the followingprinciples:

(18)

All brain functions are inseparable from

memory function (Hayek, 1952; Fuster,1995, 1997). Like in all integrative brainfunctions, memory is manifested as multipleand superimposed oscillations. A specificsuperposition of oscillations, each of whichis characterized with the response parame-ters in Item 9, represents the configurationthat is specific to the given type of memory.

(19)

‘‘Attention, perception, learning, and re-

membering’’ (APLR-alliance) are interre-lated. As the grandmother experimentsdemonstrated (Bas-ar et al., 2003; Bas-ar,2004) memory-related oscillations are se-lectively distributed in the brain. They havedynamic properties and evolve on ex-ogenous and endogenous inputs to brain.Memory states have no exact boundariesalong the time space. There is a hierarchicalorder that takes place on a continuum, butthe boundaries of memory states mergeinto each other. Memory functions fromthe simplest sensory memories to the mostcomplex semantic and episodic memoriesare manifested in distributed multiple os-cillations in the whole brain.

(20)

In our theoretical framework, we intro-duced the expression ‘‘evolving memory’’ or‘‘memory building.’’ The critical factor inmemory building is the APLR-alliance.This concept represents a constant recipro-cal activation within its sub-processes.Evolving memory has a controlling role inintegrative brain functions (Edelman, 1978;Tononi et al., 1992; Barry et al., 2003). Thehierarchy of memories is not manifestedwith separable states, since the memorymanifests rapid transitions. Therefore, wesuggest using the term ‘‘memory states’’rather than ‘‘memory stores,’’ a concept inwhich memory is considered to take placein successive stages. These explanations donot apply, however, to persistent memorywhich can be inborn or obtained throughover-learned engrams or habits.

Level D: causality in brain responsiveness

To discover the cause of an event is to discoversomething among its temporal antecedents suchthat, if it had not been present, the event wouldnot have occurred. In the introduction of thissection, causality was described as Newton, Gal-ileo, and Einstein conceptualized it. The presentsection considers causality as it pertains to spe-cifically the responsiveness of the brain. The the-ory of the whole-brain-work presently considersthree groups of factors as causes of the brain re-sponses.

Genetically fixed causal factors

There are in the brain, or in the CNS–ganglia, ge-netically coded networks. The phyletic memory-networks that are inborn play essential roles in theresponsiveness of neural populations. Accordingly(a) occipital networks in the mammalian brain re-spond to light stimulation with enhanced 12Hzoscillations (Bas-ar, 2004).In contrast, temporal au-ditory areas that do not react to light stimulationrespond to auditory stimuli with 10Hz enhancedoscillations. (b) The ray brain reacts with 10Hzoscillations to electric stimuli (electroception); thehuman brain, in contrast, does not have this ability(Bas-ar, 2004). (c) Like alpha networks, there are

48

selectively distributed gamma networks in thebrain. These networks show obligatory responsesto sensory stimuli (Karakas- and Bas-ar, 1998). (d)Reflexes are genetically coded. The so-called ‘‘pre-potent responses’’(Miller, 2000) in reflexive actionsalso partially represent this type of causality. (e)Results of Sokolov (1975) on the orienting re-sponse and the genetically fixed causal factors haveto be emphasized: there are expectation cells, whichfire on expected input; sensory-reporting cells,which fire in response to actual stimulus; and com-parator cells, which fire whenever there is a dis-crepancy between stimuli (Bas-ar, 2004).

The group of Begleiter and Porjesz launched re-cently a fundamental approach to examine the ge-netic underpinnings of the neural oscillations. It isproposed that the genetic underpinnings of theseoscillations are likely to stem from regulatorygenes, which control the neurochemical processesof the brain, and therefore influence neural func-tion (Porjezs et al., 2002). According to the pub-lications of this group, genetic analysis of humanbrain oscillations may identify genetic loci under-lying the functional organization of human neuro-electric activity and brain oscillations representimportant correlates of human informationprocessing and cognition.

The present behavior influences the immediatelyfollowing future behavior. The plasticity in thisadaptive behavior is demonstrated in the oscilla-tions, showing that oscillatory plasticity is an ad-ditional causal factor in brain responsiveness. Inauditory and visual memory task experiments, theEEG-oscillations manifest a high degree of plastic-ity: the reciprocal activation of the APLR-alliance(Bas-ar, 2004) also affects the future responsivenessof the brain, attesting for the presence of oscillatory

plasticity in the higher cognitive processes.

PART II: RECOGNITON OF FACES AND FA-

CIAL EXPRESSIONS

Grandmother experiments

Recognition of the ‘‘facial percept’’: a short survey

Physiological correlates of face processing havebeen studied by means of lesion studies, functional

neuroimaging, and conventional evoked potentials(Kanwisher et al., 1998; Kuskowski and Pardo,1999; McCarthy, 2000). The electro-physiologicalstudies have pointed out face-specific potentialswithin the range of 120–210 ms (Botzel et al., 1989;Endl et al., 1998; Taylor et al., 1999; Herrmannet al., 2002; Balconi and Pozzoli, 2003; Jemel et al.,2003). Depending on the study designs both MEGand EEG studies suggest that face-selectiveprocessing may start either in the range of100–130 ms (Linkenkaer-Hansen et al., 1998) oraround 150–170 ms (Sams et al., 1997). In addi-tion, the stages of face processing have been sug-gested to be separated into structural faceencoding that would take place at around 170 msand recognition that would take place around400–600 ms (Eimer, 2000).

Several research groups have reported distinctERP components during face presentation local-ized at temporal areas (Eimer, 2000; Zhang et al.,2001; Bentin and Golland, 2002). Face-selectivechanges in activation of the human fusiform gyruswere demonstrated by Klopp et al. (1999). Gruberet al. (2001) found significant interactions in thegamma range between electrode sites over moredistant cortical areas; however, this group did notanalyze slow frequency responses. Neurons inoccipito-temporal cortex have been observed bysingle-cell recordings in primates and in intracra-nial electrophysiological recordings in humans(McCarthy, 2000).

Begleiter et al. (1995) showed that for recogni-tion of familiar faces both the temporal and fron-tal regions are involved. Schweinberger et al.(2002) also reported that only for the familiarfaces, responses were recorded from parietal, cen-tral, and prefrontal areas.

Electrophysiological recording

The EEG was recorded from F3, F4, Cz, C3, C4,T3, T4, T5, T6, P3, P4, O1, and O2 locations ac-cording to the 10–20 system (Jasper, 1958). For therecordings, an EEG-CAP was used. Linked ear-lobe electrodes (A1+A2) served as reference.EOG from medial upper and lateral orbital rimof the right eye was also registered. For the ref-erence electrodes and EOG recordings, Ag/AgClelectrodes were used. The EEG was amplified by

49

means of a Nihon Kohden EEG-4421 G apparatuswith band limits 0.1–100Hz and 24 dB/octave. TheEEG was digitized on-line with a sampling rate of512Hz and a total recording time of 2000 ms, 1000ms of which served as the pre-stimulus baseline.

Computation of selectively averaged event-related

potentials (ERP)

Before the averaging procedure, the epochs con-taining artifacts were rejected by an off-line tech-nique. In the off-line procedure, single-sweep EOGrecordings were visually studied and trials witheye-movement or blink artifacts were rejected.Subject averages and grand averages were calcu-lated for each electrode site, experimental condi-tion. The data were digitally filtered according todetermined frequency bands of interest.

Amplitude frequency characteristics and digital

filtering

The numerical evaluation of the frequency char-acteristics was accomplished using a Fast Fouriertransform (FFT) of the following form: let Xn be adiscrete time series (Xn ¼ X (nDt), T ¼ ((N–1) Dt).Then the Fourier transform of Yk of Xn is:

Y k ¼ Y ðokÞ ¼XN�1

n¼0

X n expð�i2pN�1nkÞ;

ok � 2pkT�1

where Yk ¼ ak+ibk are the complex Fourier co-efficients whose geometric mean is the amplitudespectrum.

Digital filtering. Filtering produces visual dis-plays of the time courses of oscillatory compo-nents within the frequency limits of the utilizedfilters. The digital filters are advantageous becausethey do not produce the phase shifts that are acharacteristic of electronic filters. The digital fil-tering was employed in the present study for thedigital pass-band filtering of the ERPs and thus todemonstrate the event-related oscillations (EROs)in selected frequency bands.

The limits of the applied filters were chosen ac-cording to the cut-off amplitude frequency char-acteristics.

Analyze of phase locking of single sweeps in differ-

entiation of facial expressions

Correlation analysis was used for the statistical es-timation of the covariance of oscillations withinsingle sweeps, within a 500 ms time window (i.e.,between 0 and +500 ms). Each single sweep waspresented by a discrete time series of the amplitudes,At, t ¼ 1, 2, 3,y, 256, in this interval. Correlationcoefficients were computed for each pair-wisecombination of such time series. The obtainedcorrelation coefficients were converted into Fisher’sZ-values Z ¼ 1/2 ln (1+r)/(1–r) and then averaged.The arithmetic mean of Fisher’s Z-values was con-sidered to be a measure of similarity for the oscil-lations in the interval analysis. The mean Z-valuesincrease when the oscillations get phase-aligned andclose to ‘‘0’’ where sweeps have divergent behavior.Estimation of significance of the relationship be-tween sweeps was performed using a criterion fromcorrelation analysis. In this report, since 256 discretetime series were used, the corresponding Pearsoncorrelation coefficient for n ¼ 256�2 and p ¼ 0.01is r ¼ 0.164 and the corresponding Z-value isZ ¼ 0.165. This value was used as a criterion forthe estimation and the exceeding Z-values wereconsidered as having significant relation betweensweeps (Bas-ar et al., 1989; Maltseva et al., 2000).

In this analysis, single filtered responses werenot normalized. Accordingly, in our analysis, thecorrelation coefficients are also function ofchanges in the amplitude of single recordings.However, our empirical results confirm that singleoccipital theta responses are mostly phase lockedand depict similar amplitudes. On the contrary,frontal theta responses have a greater differentia-tion in the response amplitude and also distortedphase locking. According to this fact, we did notnormalize the data to have the advantage of dis-play with real response amplitudes.

Experimental strategy and procedure forrecognition of known and unknown faces

We used a strategy consisting of application ofthree different types of stimulations:

(1)

A simple light stimulation as control signal:its luminance was approximately at the same

50

level as for the pictures 2 and 3 described inthe following (�30 cd/m2).

(2)

The picture of an ‘‘unknown face’’: an anon-ymous elder lady.

(3)

The picture of a ‘‘known face’’: the subject’sown grandmother.

A total of 26 subjects in the age range of 15–36years (17 females and nine males) participated in thestudy. They had normal or corrected to normalbinocular visual acuity and were right handed.The pictures were presented black and white(17� 17 cm) and displayed on a screen at a distanceof 120 cm from the subjects. Stimulus duration wasset to 1000 ms with intervals varying between 3.5and 7.5 s. The subjects were instructed to minimizeblinking and eye movements, and they sat in asoundproof and dimly illuminated echo-free room.

Data recording set: the stimuli were randomlypresented in 75 trials, such that each type of stim-ulation was similarly distributed. The grand-mother (known face), unknown face, and lightresponses were analyzed separately in subsets. Allsubjects reported clearly recognizing and differen-tiating the face of their own grandmother.

Direct differentiations between grandmother and

anonymous faces

A large number of topologic and stimulus-relatedsignificant differences were described in the form ofhistograms representing comparative peak-to-peakresponse amplitudes of 26 subjects in five frequencyranges (Figs. 1–3). The illustrations with histogramscover results only with statistical significance.

Figure 1 illustrates the differentiation betweenanonymous face and grandmother’s face in threefrequency windows. At F3, there are no significantdifferences between responses of grandmother andanonymous, whereas at F4, the amplitude of thegrandmother fast theta (6–8Hz) response is sig-nificantly larger (20%) than that for the anony-mous face response (p ¼ 0.018). In T6, theamplitude of the anonymous face theta responseis significantly larger (46%) than that of thegrandmother response (p ¼ 0.035). We also analy-zed the differences between light responses andgrandmother face response at F4 and found nosignificant differences.

Beta responses reported in a recent study(Ozgoren et al., 2005) showed most clear differ-ences in distribution of the responses on the an-terior parts of the scalp (Fig. 1). Beta responses tothe unknown faces in central and frontal areaswere in the mean 40% higher than the grand-mother response; additionally, the grandmotherresponses showed prolonged beta responses. Thecomparison of P3 anonymous face/grandmotherface responses showed the greatest differenceamounting to 105%.

Gamma band (28–48Hz) also shows few directdifferentiations. At the Cz electrode, the amplitudeof anonymous face gamma response is signifi-cantly larger (22%) than that of grandmother re-sponse (p ¼ 0.02). At C3, the amplitude of facegamma response is significantly larger (38%) thanthat of the grandmother response (p ¼ 0.04). Thelight response is 23% larger than the grandmotherresponse (p ¼ 0.03).

Secondary (indirect) differentiations of grand-

mother response and anonymous face response

Figure 2A illustrates the comparisons of grand-mother responses between anterior and posteriorareas in the form of histograms. The amplitude ofthe delta response at O1 is significantly larger thanthat at F3 (p ¼ 0.001), and the amplitude of deltaresponse at O2 was significantly larger than that atF4 (p ¼ 0.000). The alpha response at O1 waslarger than that at F3 (p ¼ 0.000); the alpha re-sponse at O2 was significantly larger than that atF4 (p ¼ 0.000).

Laterality of the grandmother responses is illus-trated in Fig. 2B. On presentation of the grand-mother picture, the amplitude of delta response atT6 was 121% larger than that at T5 (p ¼ 0.008),the amplitude of 6–8Hz theta responses at T6 wassignificantly larger (114%) than T5 (p ¼ 0.008).On presentation of the grandmother picture, thealpha response at T6 was 96% larger than that atT5 (p ¼ 0.025). The amplitude of alpha response atP4 was significantly larger (22%) than that at P3

(p ¼ 0.004). The T6 gamma response was 46%larger than T5 response (p ¼ 0.021) (Fig. 2B).

Figure 3A illustrates the comparison of ampli-tudes for anonymous faces in anterior versus

Fig. 1. It illustrates the differentiation between anonymous face and grandmother face in three frequency windows in the form of

histograms that presented mean values of p–p measurements from 26 subjects in various frequency windows. The histograms cover

only results with statistical significance.

Fig. 2. (A) It illustrates the comparisons of grandmother responses between anterior and posterior areas; and (B) left/right locations in

the form of histograms that presented mean values of p–p measurements from 26 subjects in various frequency windows. The

histograms cover only results with statistical significance.

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posterior location. The delta response at O1 was32% higher than that at F3 (p ¼ 0.007); the deltaresponse at O2 was 55% higher than that at F4

(p ¼ 0.000). The alpha response at O1 was 64%higher than that at F3 (p ¼ 0.000). The alpha

response at O2 was 43% higher than the alpha thatat F4 (p ¼ 0.009).

Figure 3B illustrates differences in laterality toanonymous faces. The T6 delta response was106% higher than T5 delta response. Further

Fig. 3. (A) The comparisons of responses to anonymous face in anterior versus posterior areas. (B) The comparisons of responses to

anonymous face right versus left location in the form of histograms that presented mean values of p–p measurements from 26 subjects

in various frequency windows. The histograms cover only results with statistical significance.

52

large differences were observed in T6 location incomparison with T5 in alpha (86%), theta (194%),and gamma (59%).

These results indicate that besides direct differ-entiations between grandmother and anonymousfaces there are secondary differentiations betweenmultiple oscillatory responses to both types offaces in anterior–posterior differences. This means:in the topological space, the configurations of re-sponses in multiple frequency windows have ‘‘dif-

ferent oscillatory templates’’ to anonymous faceand grandmother faces.

Analysis of facial expressions

There are several differentiations in responses tofacial expressions. In this report, we will only givefew examples to show that by using techniques ofphase-locking analysis of single sweeps we cancollect important material for electrophysiologicalrecording of complex percepts. As we have seen inthe previous section, the differentiation of ‘‘knownand unknown’’ pictures is already an intriguingprocedure. Differentiation of facial expressions ofthe same subject is certainly a more difficult prob-lem. Accordingly, we did compare not only dif-ferentiation in the amplitude of theta responses

but also changes that took place in the single re-sponsiveness of single trails.

Experimental strategy and procedure for differen-tiation of facial expressions: ‘‘angry’’ and ‘‘happy’’

As stimulation, we have used the photographs ofeight persons, students of Department of Perform-ing Arts, who have been asked to mimic angry andhappy expressions. At the first group, we had 16photographs that include eight of each person’sfacial expressions mimicking two different facialexpressions. As stimulation, we have selected ran-domly 6 out of 16 photographs of 3 different per-sons in 2 measuring sequences. The pictures werepresented to the subjects under similar conditionsof the face recognition experiments Fig. 4.

The experimental procedure includes five re-cording sessions; the number of face and lightstimulation was 75 in every section.

1.

Spontaneous EEG of the subjects. 2. First person’s two different facial expressions

in the order as follows: (1) happy face and (2)angry face.

3.

Second person’s three different facial expres-sions in the order as follows: (1) angry faceand (2) happy face.

Fig. 4. Pictures of facial expressions: ‘‘angry and happy.’’

53

4.

Third person’s three different facial expres-sions in the order as follows: (1) happy faceand (2) angry face.

5.

As a control, for visual evoked potentials, wehave used light stimulation.

Twenty healthy subjects (11 male, 9 female) —most of them being medical school students —volunteered for the study. Subject’s age rangedbetween 16 and 39 (mean ¼ 23.27; SD ¼ 5.58).

At the end of the experiments, the subjectswere asked whether they had recognized the fa-cial expression or not. Eighteen out of 20 subjects(95%) recognized all the expressions correctly,while two of them could not differentiate the an-gry face stimulation well. Two of 20 subjects wereexcluded from the study due to the fact that theycould not recognize the face differentiation ofangry faces.

Methods of EEG recording and analysisof filtering and amplitude frequency characteris-tics and correlation analysis for this experimentwere described already in Sections ‘‘Electrophy-siological recording,’’ ‘‘Computation of selectivelyaveraged event-related potentials (ERP),’’‘‘Amplitude frequency characteristics and digitalfiltering,’’ and Analyze of phase-locking ofsingle sweeps in differentiation of facial expres-sions.’’

Results: differentiation of facial expressions

In our earlier studies, the comparison of theta re-sponse between known and unknown facesshowed that frontal theta responses were signifi-cantly high in comparison with occipital face re-sponses. F4 theta response provided one of therelevant differentiated responses (Figs. 5A, B). Inthe analysis related to differentiation of facial ex-pressions, a completely different feature was ob-served. Occipital theta responses have largeramplitudes in comparison with frontal responses.Accordingly, we evaluated this relevant changewith a more precise mathematical procedure. Wemade use of correlation coefficients and the sta-tistical approach with Z-scores.

In Fig. 5(A, right), frontal (F4) theta response(4–7Hz) of a typical subject is illustrated as an av-erage of 61 sweeps as response to angry face stim-ulation. The amplitude of the occipital thetaresponse was around 300% higher than that offrontal theta. We have to note that in experimentswith simple light stimulation or in experiments re-lated to recognition of the grandmother face, we didnot record such a crucial increase of theta responsein the occipital location. On the contrary, the frontaltheta response was much higher than the occipitaltheta response. Further, we analyzed single sweepsin the theta frequency range and observed that theamplitude increase in the occipital electrodes was

Fig. 5. (A) Superposition of theta oscillations in F4 locations in the frequency range 4–7Hz to angry face presentation. (B) Su-

perposition of theta oscillations in O2 locations in the frequency range 4–7Hz to angry face presentation.

Table 1. Z values of F4–O2 of single sweeps on ‘‘angry’’ face

stimulation

F4 O2

0.1561 0.1044

0.2869* 0.2682*

0.0110 0.1959*

0.0728 0.2463*

0.1912* 0.5982*

0.0520 0.1356

0.2905* 0.3118

0.1598 0.0951

0.0399 0.1892*

0.0836 0.3493*

�0.0164 0.0506

�0.0016 0.1884*

0.1231 0.5388*

0.1445 0.3120*

0.1266 0.1539

0.0438 0.2597*

0.0155 0.1938*

0.1137 0.2787*

54

mostly due to phase synchronization in the first 300ms following the stimulation. Fig. 5B shows the su-perposition of 61 sweeps in O2 locations. To showthe degree of phase locking, which is globally seen inFigs. 5A and B, we applied the method of correla-tion factors to describe the degree of congruence ofsingle sweeps by evaluation of correlation coeffi-cients. Further, the correlation coefficients weretransformed to Z-values for statistical analysis. Thisis one of the methods to analyze strength of phaselocking (Bas-ar et al., 1989; Maltseva et al., 2000).

Accordingly, the differentiation of phase-lock-ing processes in occipital and frontal locations canbe used as an indicator to differentiate angry facesfrom happy faces.

The Tables 1 and 2 show the Z-values of F4 andof O2 locations on ‘‘angry’’ and ‘‘happy’’ faces,respectively. The comparison of Z-scores in bothtables shows the following results:

(1)

Phase locking to angry faces was strong inoccipital locations: occipital theta Z-valuesshow that 12 out of 18 subject’s occipitaltheta Z-values were higher than the criticalZ-value (Z ¼ 0.165) (p ¼ 0.002; w2 ¼ 9.26).In contrast, the degree of phase locking isvery weak in frontal location. Only two sub-jects in frontal location had significantZ-scores, i.e., Z-value higher than 0.165.

(2)

Phase locking in theta responses to happy

faces is weak in occipital location and not

existent in frontal location. Only six subjectsin occipital and three subjects in frontal lo-cation had significant Z-scores, i.e., Z-valuehigher than 0.165 (see Section ‘‘Analyze ofphase-locking of single sweeps in differenti-ation of facial expressions’’).

The joint comparison of Tables 1 and 2 can besummarized as follows: the frontal locations donot depict significant phase locking to both types

Table 2. Z values of F4–O2 of single sweeps on ‘‘happy’’ face

stimulation

F4 O2

0.2459* �0.0096

0.0179 0.0941

0.0534 0.1188

0.0239 0.1790*

�0.0142 0.6985*

0.0081 �0.0075

0.0338 0.3228*

0.1469 0.0969

0.0656 0.2572*

0.0435 0.2393*

0.0179 �0.0928

0.1526 0.0667

0.0577 0.6967*

0.0425 0.1101

0.2728* 0.1059

0.1019 0.0708

0.0378 0.0715

0.0766 0.1386

55

of facial expressions. In occipital recordings, thedegree of phase locking is highly increased incomparison with frontal locations, again for bothtypes of facial presentations. However, only theO2 locations shows statistically significant strongphase locking to angry faces. This means that theresponsiveness of event-related theta oscillations isstrongest in O2 locations.

These findings describe a marked and shift ofstrength of theta response from frontal to occip-ital locations. The responses to simple light arestrong in frontal and weak in occipital locations.Also in experiments with facial presentation,during which subjects do not have the task ofdifferentiating facial expressions, the theta re-sponses are strong in F4 and weak in O2 loca-tions. This interesting shift is important from theviewpoint of psycho-physiological interpretation:it can be tentatively assumed that the selectiveattention task to differentiate facial expressionsdoes induce stronger and phase-locked occipitaltheta responses.

This type of phase-locking analysis will be ap-plied in future evaluations to several frequenciesand will indicate the importance of expressions instrong resonance phenomena and weak resonancephenomena described in very early publications

(Bas-ar et. al., 1975). The expressions ‘‘strongand weak resonances’’ were adapted to brainresearch from quantum physics, which will prob-ably gain importance again in the study of brainoscillations.

Interim conclusion for facial expression

(1)

After our initial analysis related to changesin face expression with the pictures of ourown setup, we expanded our analysis also tothe results withEkman pictures of facial af-fect. The first analysis shows, in general, verygood accordance with Ekman’s pictures;these analyses will be also completed andstatically checked.

(2)

The applied concept and tools of ‘‘OscillatoryBrain Dynamics’’ clearly show the possibilityof differentiation of changes between facialexpressions. The present report is limited onlyto comparison of frontal and occipital thetaresponses. The first survey of the alreadyanalyzed results shows, however, that othertopological differentiations related to oscilla-tory responses are also present. This was alsothe case by the analysis of face differentiationbetween ‘‘Grandmother’’ and ‘‘Unknownfaces.’’ The differentiation consisted in selec-tively distributed 25 components.

(3)

There are several other reports on evokedpotential analyses of facial affection (Satoet al., 2001; Bentin and Golland, 2002; Bal-coni and Pozzoli, 2003; Cicchetti and Curtis,2005) that do not include the oscillatoryanalysis.

(4)

An important additional finding should bementioned here without going into details.According to Guntekin and Bas-ar (in press),an important frequency shift was observed:the significant alpha response to angry faceswas in the frequency general of 9.5Hz at theoccipital recordings, whereas the happyfaces induce responses in a lower frequencyrange of 8.0Hz.This is also a relevant dif-ferentiation, which will be explained in moredetails in future publications.

56

General discussion

Survey of various approaches to the analysis offace recognition demonstrates that face recogni-tion and differentiation of facial expressions re-quire a profound analysis with an ensemble ofconcepts and strategies. A simple measurementcannot be absolute representative and may lead toerroneous statements. The analysis with EEG-os-cillations also requires special care by including (1)measurements of a baseline with simple light, (2)topologically distributed locations, and (3) theconsideration of multiple oscillatory components.Steps (2) and (3) are often neglected not only in theEEG but also in most of fMRI studies. (4) Thepresented data do not favor statements related toabsolute localizations and preferred frequencychannels; for the time being the presented resultsshould be only physiologically described andshortly commented.

Highlights of differentiation between grandmotherand anonymous faces

Direct differentiation between grandmother and

anonymous faces as indicated by varied degrees of

response amplitudes

A simple light stimulation evokes selectively dis-tributed multiple oscillations in the brain. Theanalysis of oscillatory responses to simple lightprovides a necessary control often neglected instrategies to understand brain function. Our re-sults once again demonstrate that even the per-ception and/or remembering of simple light evokecomplex processing in the brain.

Occipital delta responses on presentation ofanonymous faces and grandmother faces were veryhigh in comparison with light stimulation. Thismeans that the increase in occipital delta responseis a consequence of face processing in generalwithout differentiation between known and un-known faces. Earlier results demonstrated that theamplitude of the delta response is considerably in-creased during oddball experiments (Bas-ar-Erogluet al., 1992; Karakas- et al., 2000a, b). Therefore, itwas concluded that the delta response is related tosignal detection and decision-making. Further, the

delta responses to visual oddball targets have theirhighest response in parietal locations, whereas forauditory target stimuli the highest delta responsesare observed in central and frontal areas (Bas-ar-Eroglu et al., 1992; Schurmann et al., 1995). Ac-cordingly, the increase of delta in occipital areas inanonymous and grandmother pictures is a relevantfinding as a component of the recognition of thepresented picture as a face, but not a manifestationfor the differentiation between different faces.

The most important feature of our data is theexistence of a variety of significant differences indelta, theta, alpha, beta, and gamma responsesbetween the anonymous and grandmother faces infrontal, central, parietal, temporal, and occipitalsites.

Multiple oscillations in recognition of faces

The amplitude difference in theta response be-tween frontal (F4) grandmother and anonymousface responses indicate the differentiation betweenknown and unknown faces, accordingly betweenepisodic and semantic events.

Right temporal (T6) theta responses are signif-icantly higher to both types of faces in comparisonwith left temporal (T5) responses. Further, it is tonote that the temporal theta responses to bothtypes of face presentations are considerably high incomparison with light stimulation. This findingstrongly indicates that temporal theta processingand frontal theta processing have probably differ-ent functional correlates: whereas frontal thetaface differentiation has a role for differentiation ofsemantic and episodic memories, the temporo-posterior theta seems to be responsible for globalface detection, similar to posterior delta responses.Accordingly, it can be assumed that the T6 thetaresponses do not manifest a differentiation be-tween episodic and semantic memories, as it is thecase in F4 theta response, since no significant dif-ferences were noted between T6 responses to bothtype of picture presentation. Our results differfrom results presented by Haxby et al. (2001) andKanwisher et al. (1998), Grill-Spector et al. (1999)allocating absolute face recognition areas to tem-poral lobes.

Although the alpha response merits special at-tention, this will be subject of a separate and

57

detailed study by considering the fluctuations dueto higher and lower frequency bands and the jointalpha blocking process (Klimesch et al., 2000a; seealso preliminary results of Bas-ar, 2004).

The beta responses topologically highly differedbetween grandmother and anonymous face stimuli.The frontal beta response was higher to the anon-ymous face presentation, whereas the parietal betadifferentiation was the highest with 100% increaseof the anonymous face versus the known face.

Gamma responses showed differentiations in C3

and Cz locations, but not in posterior and rightlocations. As we stated, gamma responses wereone of the essential oscillatory components, buttheir contribution to all brain functions, and con-sequently to processing of face recognition, shouldbe described in parallel or in superposition withthe activation of other oscillatory components(Bas-ar, 1999, 2004). According to the presentedresults, the gamma response is more sensitive (withhigher activation) to the unknown face, in com-parison with the known face. It is also to be notedthat the beta response showed a higher sensitivityto differentiate both face presentations, in com-parison with the gamma response.

Secondary or indirect differentiations were alsoimportant: not only the differences between theface and grandmother responses but also thequantitative amplitude behavior of oscillatory re-sponses at right–left, and anterior–posterior loca-tion differences to both picture presentationscontributed to the shaping of percepts. As an ex-ample, we emphasized the theta responses at T5

and T6 locations. The grandmother T6 theta re-sponse showed a difference of 114% in compari-son with the left T5 theta response, whereas theanonymous face T6 theta response was 194%higher in comparison with the left. Thus, the T6

responses may indicate a higher sensitivity to se-mantic activation, in comparison with the episodicone. The present report cannot discuss in detail all18 indirect differentiations.

The delta response triggered by facial recogni-tion is much higher than the P300 delta responsewith simple light (Oniz and Bas-ar, in press). There-fore, it can be considered that the delta response tofacial stimulation is mostly due to recognitions offaces.

We have seen in Section ‘‘Results: differentia-tion of facial expressions’’ that the theta responseto facial expressions have stronger phase lockingin occipital location whereas to grandmother stim-ulation no significant occipital theta responseswere observed.

Dynamic localization

Our analysis for both types of faces shows a va-riety of significant differences. This suggests thatactivations of oscillations to each type of facialstimulus show significant selectively distributedactivation patterns. Therefore, our results supportand extend the concept that integrative brain func-tions are based on multiple oscillations. We wouldlike to emphasize that the analysis of conventionalERPs and single frequencies may lead to restrictedinterpretations (Bas-ar, 1980, 1999). This view findssupport by several recent publications (Gruzelier,1996; Bas-ar, 1999; Makeig et al, 2002; Klimeschet al., 2004). Different functions are often corre-lated with different oscillations at distinct locations.

According to the results, only ensembles orcombinations of delta, theta, alpha, beta, andgamma that act in parallel are tenable and essen-tial for the specific shaping of an individual per-cept, not only one recording area or a uniquefrequency.

The experimental studies of Klimesch groupshow the possibility of differentiating the role ofalpha and theta oscillatory activity during memorytasks (Klimesch et al., 1994; Sauseng et al., 2002).Our earlier and the present findings are in accord-ance with the scope of all these authors: the neuralrepresentations of different memory forms (e.g.,semantic and episodic memory) involve activationof neural populations firing in all common fre-quencies ranges (delta, theta, alpha, beta, andgamma). Thus, these findings are in accordancewith these fundamental physiological statementsstated above (Damasio, 1994; Fuster, 1997). Theresults suggest and emphasize the importance ofinvestigating multiple oscillations at different lo-cations as a tool for recognizing differences in ep-isodic and semantic events.

‘‘Is grandmother an oscillation? Question posedby Stryker (1989) can be answered by stating thatthe percept of the grandmother percept is

58

manifested by multiple oscillations selectively dis-tributed in the brain.

Comparison with fMRI and single-cell results

Courtney et al. (1997) presented subjects with pic-tures of human faces and asked them to recallwhether the picture being shown was the same as,or different from, from the one that had been pre-sented 8 s earlier. The authors found that activat-ions in the prefrontal areas correlated moststrongly with delay periods, compared with acti-vations in the visual areas, which were morestrongly correlated with sensory stimulation.

The results of Haxby et al. (2001) indicate thatthe representations of faces and objects in ventraltemporal cortex are widely distributed and over-lapping; Grill-Spector et al. (1999) describe thatthe fusiform face area is involved in both detectionand identification of faces. However, the interpre-tation of results that the percepts of several objectsare localized only in restricted given substructuresof temporal cortex is physiologically questionable,since such statements are not in accordance withphysiologically anchored theories by Mesulam,Fuster, Goldman-Rakic, and fMRI findings byCourtney et al. (1997). Although fMRI studiesmerit important consideration, a word of cautionshould be stated because of the very low-temporalresolution (Grill-Spector et al., 1999) and the miss-ing of frontal activations, although the co-activa-tion of frontal lobes is almost obligatory from thephysiological viewpoint (Fuster, 1997).

By using single-cell recordings, Quiroga et al.(2005) reported subsets of neurons that are selec-tively activated in the human medial temporallobe; these authors recorded under restrictive op-erative conditions and could not have the chanceto record in fusiform area, occipital or frontalcortices. Thus, results of Haxby, Grill Spector, andParrots are highly contradictory, possibly support-ing the concept of selective distribution, which wehave described in Section ‘‘Multiple oscillations inrecognition of faces.’’

Although our results show that the grandmotherpercept can be differentiated from the anonymousface by multiple and selectively distributed os-cillations, such an analysis cannot completely

exclude the existence of a group of face-sensitiveneurons somewhere in the brain. Moreover, ac-cording to Libet (1991) the brain needs at least300–500 ms for building percepts and all oscilla-tory responses take place in parallel at multiplelocations of the cortex �300–400 ms following thestimulation.

Concluding remarks

The application of the theory of the ‘‘whole-brain-work’’ is useful for interpretation of recognition offaces and facial expressions and provides the fol-lowing essential features:

(1)

Mechanisms leading to the perception of thegrandmother picture are manifested withparallel activations of neural assemblies indifferent cortical locations and as superpo-sition of delta, theta, alpha, beta, and gamma

oscillations. Grandmother (known andanonymous (unknown) faces can be differ-entiated by means of oscillatory braindynamics. Percepts cannot be localized in agiven specific region.

(2)

While the importance of fMRI in object rec-ognition is clear, this method has low tempo-ral resolution. Our results shows that multiplebrain oscillations clearly differentiate theknown and unknown faces with varied de-grees of selective-responsiveness in a shorttime window between 0 and 800 ms, thuscompleting and implementing the analysis ofpercepts in the dynamic window and indicat-ing a broader distribution at the cortex.

(3)

The presented evidence of selectively distrib-

uted multiple oscillations for differentiationof facial percepts is in conceptual accord-ance with the ‘‘selectively distributedprocessing’’ in neurocognitive networks ofGoldman-Rakic, Fuster, and Mesulam. Thelarge-scale approach of several investigatorsis also confirmed with the new results. Onfacial stimuli, a given location can show aconsiderable selected activation, but the for-mation of percepts is manifested by multipleoscillations with differentiated weight inlarge neural populations.

59

(4)

Not only direct differentiations between os-cillatory responses of different faces but alsosecondary differentiations related to selec-tive distribution in the whole cortex are nec-essary to describe various percepts.

(5)

The comparison of oscillatory responses forrecognition of different faces and differenti-ation of facial expressions show importantfeatures.(a) Amplitude of various oscillations is

different in both paradigms.(b) The degree of phase locking (e.g., in

the theta frequency range) is differentdepending on the topological side, theuse paradigm as ‘‘angry’’ or ‘‘happy’’presentation.

(c) Important frequency shifts dependingon the type of face presentations canbe observed.

(d) Some frequency responses show pro-longed oscillations (i.e., prolon-gation of beta oscillations to knownfaces).

(6)

All these results indicate that the oscillatoryresponses manifest a manifold of differenti-ation depending on the modality of the stim-uli, the used paradigm, and the side of therecording. Seemingly, the brain differenti-ates complex stimuli by using a number ofparameters that constitute the own strategyof the brain.

According to all the statements in the fifth pointmentioned above, the brain response is a constructin a multi-dimensional state incorporating ampli-tudes of oscillatory responses, topological coordi-nates, and changes in the time axis followingpresentation of the percepts including delays andprolongations, coherence between locations. Onlya new metrics embracing all these parameters canbe representative for dynamics of functionality inthe brain.

Acknowledgments

This study was supported by grants 446 TUR 112/14/01 and BAYG of The Scientific & Technolog-ical Research Council of Turkey (TUBITAK).

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