Cerebral Cortex May 2008;18:997--1009

doi:10.1093/cercor/bhm140

Advance Access publication August 22, 2007

Spatio temporal Dynamics of FaceRecognition

Emmanuel J. Barbeau1, Margot J. Taylor2, Jean Regis3,4,5,

Patrick Marquis3,4,5, Patrick Chauvel3,4,5 and

Catherine Liegeois-Chauvel3,4,5

1Centre de recherche Cerveau et Cognition, Universite Paul

Sabatier Toulouse 3, Centre National de la Recherche

Scientifique, Toulouse, France, 2Diagnostic Imaging, Research

Institute, Hospital for Sick Children, Toronto, Canada, 3Institut

National de la Sante et de la Recherche Medicale U751,

Marseille, France, 4Assistance Publique - Hopitaux de Marseille

Timone, Marseille, France and 5Universite Aix-Marseille,

Marseille, France

To better understand face recognition, it is necessary to identify notonly which brain structures are implicated but also the dynamics ofthe neuronal activity in these structures. Latencies can then becompared to unravel the temporal dynamics of informationprocessing at the distributed network level. To achieve high spatialand temporal resolution, we used intracerebral recordings inepileptic subjects while they performed a famous/unfamiliar facerecognition task. The first components peaked at 110 ms in thefusiform gyrus (FG) and simultaneously in the inferior frontal gyrus,suggesting the early establishment of a large-scale network. Thiswas followed by components peaking at 160 ms in 2 areas alongthe FG. Important stages of distributed parallel processes ensuedat 240 and 360 ms involving up to 6 regions along the ventralvisual pathway. The final components peaked at 480 ms in thehippocampus. These stages largely overlapped. Importantly, event-related potentials to famous faces differed from unfamiliar facesand control stimuli in all medial temporal lobe structures. Thenetwork was bilateral but more right sided. Thus, recognition offamous faces takes place through the establishment of a complexset of local and distributed processes that interact dynamically andmay be an emergent property of these interactions.

Keywords: electrophysiology, epileptic patients, intracerebral recordings,model of face recognition, neural network

Introduction

Face recognition is a fundamental skill that has received con-

siderable attention because of its importance for social inter-

actions. Furthermore, the ability to recognize a face can be

impaired in isolation suggesting that it relies on dedicated

neural circuits (Charcot 1883; Wilbrand 1892; Bodamer 1947).

Subsequent to the report of spatially and temporally local-

izable brain responses to faces (Allison, Ginter, et al., 1994),

there has been an enormous interest in revealing the char-

acteristics of these neural circuits via various functional

neuroimaging and electrophysiological methods as well as

neuropsychological case studies (De Renzi et al. 1994; Bentin

et al. 1999; Haxby et al. 1999; McCarthy et al. 1999; Halgren

et al. 2000; Itier and Taylor 2004a). Several brain areas have

been identified as being involved in face processing, such as the

inferior occipital gyrus, the posterior fusiform gyrus (FG), or

the temporal poles (Sergent et al. 1992; Evans et al. 1995; Puce

et al. 1995; Kanwisher et al. 1997). This has led to the proposal

that face recognition relies on a distributed neural network

(Haxby et al. 2000; Ishai et al. 2005). In the temporal domain, a

negative component peaking around 170 ms (N170) is re-

corded on the scalp surface and has been shown to be

consistently larger to face stimuli (Bentin et al. 1996; Itier and

Taylor 2004b). This N170 is preceded by an earlier component

(P1) that also shows some modulation by faces (Halgren et al.

2000; Taylor et al. 2001) and is followed by later responses

around 250 and 400 ms that are modulated by face familiarity

(Bentin and Deouell 2000; Schweinberger et al. 2002).

Despite progress in identification of the neural substrate and

timing underlying face recognition, it has been difficult to

relate both those dimensions because most investigation

methods currently available allow satisfactory precision in

either the spatial distribution (positron-emission tomography

[PET], functional magnetic resonance imaging [fMRI]) or the

temporal course of the information (scalp electroencephalog-

raphy [EEG], magnetoencephalography), but not the 2 simul-

taneously. However, precise knowledge of the temporal course

of the neural activation in each brain area involved in face

recognition is critical for any model of face recognition. It is

only with this knowledge that how the different brain areas

interact can be properly understood (Nowak and Bullier 1997;

Bullier 2001; Bullier et al. 2001; Bar 2003). The aim of the

present study was to analyze the temporal dynamics of the

different brain areas involved in face recognition.

Precise measures of where and when activity occurs in the

brain can be obtained with intracranial recordings. The classic

studies by Allison, Ginter, et al. (1994), Allison, McCarthy, et al.

(1994), Allison et al. (1999), McCarthy et al. (1999), and Puce

et al. (1999) using grids on the cortical surface identified

a series of components evoked by faces. A potential peaking at

200 ms specific to faces was found in the posterior FG as well

as the posterior middle temporal gyrus regions. Other

components such as a P350 were recorded in more widespread

regions in the posterior (VP350) and anterior (AP350) ventral

temporal surface as well as the posterior lateral temporal

surface (LP350). Using intracerebral electrodes implanted within

brain structures, Halgren, Baudena, Heit, Clarke, Marinkovic

(1994) and Halgren, Baudena, Heit, Clarke, Marinkovic, Chauvel

(1994) confirmed and extended these findings identifying

a series of components evoked by a face recognition task with

a first sequence of N130--P180--N240 followed by another of

N310--N430--P630. These potentials did not have the same

spatial distribution. Some were recorded in a limited set of

brain areas, such as the 180 component in the ventral pos-

terior temporal region, whereas others were recorded from

different areas, such as the 130 and 240 components in the

temporal, parietal, and frontal lobes. Using still a different

intracerebral approach, with an electrode passing through the

grand axis of the hippocampus, Trautner et al. (2004) and Dietl

et al. (2005) focused on a potential peaking around 400 ms

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(facial anterior medial temporal lobe-N400) recorded in the

depth of anterior subhippocampal structures and on a potential

recorded within the hippocampus peaking around 600 ms

(hippocampal P600).

These studies confirmed that face recognition is carried out

in different brain regions and helped reveal the underlying

distributed network. The temporal dynamics of face recogni-

tion are also highlighted, with components found as early as

110 ms, up to 600 ms. However, except in a figure of ours

(Halgren and Chauvel 1993), there has been little attempt to

specifically compare the latencies of the intracerebral compo-

nents recorded from different regions with one another. Such

comparison can help in meeting our objective of providing

a comprehensive framework of the spatiotemporal dynamics of

face recognition at the whole-brain level.

In order to elicit potentials related to the recognition of

faces, a famous face/unfamiliar face paradigm was used. To

achieve simultaneous high spatial and temporal resolution, this

paradigm was presented to subjects with drug-refractory

epilepsy who had 6 --10 depth electrodes implanted stereotax-

ically orthogonal to the interhemispheric plane. Each electrode

contained 10--15 contacts along its length. It was thus possible

to record from many different brain regions simultaneously,

including some that had seldom been studied. We first

investigated the spatiotemporal dynamics of the recognition

of famous faces. We then investigated whether the recognition

of faces differed from the recognition of another type of

stimuli. Lastly, we analyzed the lateralization of face processing.

Material and Methods

Stimuli and TasksAll subjects underwent a famous face/unfamiliar face recognition task

using Eprime v1.1 (Psychology Software Tools Inc., Pittsburgh, PA).

Accuracy of the delivery of the visual stimulus and the trigger recorded

simultaneously with the EEG was controlled using a photodiode on the

screen the subject was looking at. Famous faces (actors, singers, or

politicians) and unknown faces (Fig. 1) were presented on the screen

for 396 ms in a random order, and subjects had to indicate verbally

whether they knew them. Note that our procedure was not intended to

allow assessing how the patients recognized the faces (i.e., based on

a feeling of familiarity, on semantic retrieval, or on naming). All

photographs were gray scale and corresponded to an angular size of

about 6� 3 6�. Interstimulus interval (1992 ms) was filled by a fixation

cross. Mean luminance of famous and unknown face photographs was

equivalent. There were 48 pictures of each category of faces. Overall

performance was 83% correct responses (detailed in Table 1). After

correcting for errors (false alarms and omissions) and artifact rejection,

the number of epochs per condition (famous faces correctly

recognized and unfamiliar faces correctly rejected) was equated.

Following these procedures, there were on average 38.1 (standard

deviation = 7.8) epochs used to compute event-related potentials

(ERPs) in each condition per patient.

In order to differentiate processes related to the recognition of faces

from processes related to the recognition of other kinds of stimuli,

patients also completed a visual recognition memory task in which

trial-unique abstract patterns consisting of colorful clip arts (Fig. 1)

were used as stimuli. In this task, subjects first had to learn a set of 15

stimuli and, after an interfering task of 3 min, had to recognize them

among distracters in an old/new paradigm. Patients underwent several

blocks according to their availability.

Subjects and RecordingsEighteen patients who had drug-refractory epilepsy and were un-

dergoing evaluation of possible surgical intervention were studied.

Stereoelectroencephalographic (SEEG) recording was performed in

order to define the epileptogenic zone (Talairach and Bancaud 1973).

The choice of electrode location was based on pre-SEEG clinical and

video-EEG recordings and made independently of the present study.

This study did not add any invasive procedure to depth EEG recordings.

All subjects were fully informed about the aim of investigation before

giving consent. Subjects had from 6 to 10 intracerebral electrodes

implanted stereotaxically orthogonal to the midline vertical plane. Each

electrode was from 33.5- to 51-mm long, had a diameter of 0.8 mm, and

contained from 10 to 15 contacts 2-mm long separated by 1.5 mm

(Alcis, Besancxon, France). Seventy-two to 126 intracerebral contacts

were simultaneously recorded in each patient.

ERP recordings were part of the functional mapping procedure

(language, memory, and vision depending on the epilepsy) carried out

in each subject. Anticonvulsant therapy was reduced or withdrawn

Figure 1. Examples of stimuli used in this study. Top: famous and unknown faces. Bottom: trial-unique abstract stimuli.

998 Spatio temporal Dynamics of Face Recognition d Barbeau et al.

during the SEEG exploration in order to facilitate seizures. However, no

subject had seizures in the 12 h before ERP recordings. Signal was

acquired using SynAmps amplifiers and Neuroscan software (Compu-

medics, El Paso, TX). The sampling frequency of EEG depth recording

was 1000 Hz with an acquisition filter band-pass of 0.15--200 Hz.

Reference was a surface electrode located in Fz. Implantation of

electrodes, localization of epilepsy, and behavioral performance on the

famous/unknown face recognition task are reported in Table 1.

ERP ProcessingERPs were computed off-line using Brainvision v1.05 (Brain Products

GmbH, Munchen, Germany) between 0 and 1500 ms with a prestimulus

baseline of 150 ms. ERPs to famous faces and unknown faces as well as

old/new abstract figures were computed after the rejection of

omissions and false alarms (behavioral data were lost for 4 subjects

due to a hard disk failure. In this case, all responses were considered

correct, Table 1). Visual inspection of the EEG as well as artifact-

rejection procedures enabled the rejection of periods with interictal

activities. A filter with a high band pass of 0.53 Hz (time constant = 0.30,

12 dB/oct) and a low band pass of 40.00 Hz (12 dB/oct) was applied to

the resulting ERPs.

Statistical AnalysesThere were from 5 to 7 subjects’ data per brain area, so a nonparametric

matched-paired signed-rank Wilcoxon test on individual averaged ERPs

was used to assess significant differences. The Wilcoxon test was run

on each millisecond in the 100--600 time windows. Performing tests on

multiple time points increases the probability of a false positive. We

therefore only considered differences significant that were present for

10 consecutive time points (Molholm et al. 2006). In contrast, gaps of

less than 10 ms between adjacent periods were considered non-

physiological and the periods were combined.

Location of the Electrode ContactsContact location has been described in detail elsewhere (Barbeau,

Wendling, et al. 2005). Each electrode was inserted stereotaxically

under general anesthesia. A routine postoperative computerized

tomography (CT) scan without contrast was performed to check for

the absence of bleeding and for the precise location of each contact.

Magnetic resonance imaging (MRI) with 3-dimensional reconstruction

was performed after the removal of the electrodes in order to locate

the trace of each depth probe in the brain. The fusion of the

postoperative CT scan with this MRI allowed precise anatomical

localization of contacts. The distance from the midline vertical plane of

a given contact could be calculated on the axial CT scan. After the trace

of the electrode had been found on the postoperative axial MRI, the

distance from the midline vertical plane could be determined and the

position of the contact could then be viewed in the coronal or sagittal

plane. After this procedure was completed, the anatomical structures in

which contacts were located were identified using 3-dimensional

verification.

Results

More than 2000 sites were recorded in 18 subjects in total, an

average of 111 sites recorded simultaneously per subject. ERPs

to famous faces recorded from these contacts constitute the

basis of this study.

ERP Identification

A major aim of the present study was to identify the pattern of

ERPs underlying the temporal processing of face recognition.

We thus analyzed the ERPs region by region to identify those

that met the 2 criteria: 1) exhibiting a similar waveform and

latencies in the same region across and 2) being generated

focally. Criteria for local generation were polarity reversal

between adjacent contacts and/or steep voltage gradient and

high voltage fluctuations between adjacent contacts (Halgren,

Baudena, Heit, Clarke, Marinkovic 1994; Fernandez et al. 1999).

A supplementary criterion was to reach a threshold of 5 sub-

jects per region across our population of 18 patients in order to

be able to perform statistical comparisons at least 5 subjects.

ERPs corresponding to the criteria defined above were

identified in 7 regions: the posterior fusiform gyrus (FG), the

middle FG 1--2 cm anterior to the previous region, the posterior

parahippocampal gyrus, the perirhinal cortex within the

collateral sulcus, the medial temporal pole, the hippocampus

(posterior and anterior), and the inferior frontal gyrus. Figure 2

depicts all brain regions explored and shows in black the 7

regions from which the focal ERPs reproducible across subjects

were identified. Focal ERPs were recorded from other brain

regions (shown in grey), for example, from the amygdala, the

orbitofrontal cortex, the lateral temporal lobe (LTL) along the

Table 1Subjects, clinical details, and epileptic data

Subject Sex Age Handedness Languagelateralization

Epileptic focus Etiology Number ofelectrodes

Number ofcontacts

Analyzed in this study(r: right, l: left)

Taskperformance (%)

1 M 23 L L R-LTL CD 10 120 rpFG, rPG, rPC, rTP, rIFG 862 M 24 L L L-MTL OGD 6 78 — 673 M 32 R L L-MTL CD 8 90 lTP, lH 894 M 34 R R L-MTL HS 10 126 lPG, lPC, lTP, lH, lIFG 985 M 43 R L R-LTL Unknown 9 120 rPG, rPC, rTP, rH, rIFG n/a6 F 35 L B R-MTL CD 9 117 — n/a7 F 33 R L L-MTL Unknown 8 114 rPC 848 M 33 R L R-PF CD 10 120 — 909 M 36 R L R&L-MTL Unknown 6 72 lH 9510 M 29 R L R-LOE CD 10 109 lpFG n/a11 M 35 R L L-MTL HS 8 110 lmFG n/a12 M 42 L L R-MTL Unknown 9 117 rmFG, rPG, rTP, rH, rIFG 8913 M 25 R B R-MTL Unknown 9 114 rPG, lPC, lTP, rH, rIFG 6914 F 26 L L L-LOE Unknown 9 120 lpFG, lmFG 8115 F 24 R L L-LTL-LOE Unknown 10 121 rPC 7216 F 48 A L L&R-PA Unknown 9 123 rpFG, rmFG, lPG, rTP 7517 F 41 R L R-LOE CD 8 120 rpFG, rmFG 9618 M 56 R L R-MTL HS 8 115 rIFG 76

Note: R, right; L, left; A, ambidextrous; MTL, medial temporal lobe epilepsy; LTL, lateral temporal lobe epilepsy; PF, prefrontal lobe epilepsy; LOE, lateral occipital epilepsy; PA, parietal lobe epilepsy;

pFG, posterior fusiform gyrus; mFG, middle fusiform gyrus; PG, posterior parahippocampal gyrus; PC, perirhinal cortex; TP, temporal pole; H, hippocampus; IFG, inferior frontal gyrus; r, right; l, left; CD,

cortical dysplasia; OGD, oligodendroglioma; HS, hippocampal sclerosis; n/a, non available.

Cerebral Cortex May 2008, V 18 N 5 999

superior temporal sulcus, the anterior insula, or the dorsolat-

eral frontal lobes, but did not fulfill the criteria mentioned

above and so were not analyzed further.

Spatiotemporal Dynamics of Face Recognition

Figure 3 shows ERPs averaged across patients recorded from

the posterior to anterior regions mentioned above in response

to correctly recognized famous faces.

These results are also reported for a representative subject

(subject 1) in whom most brain areas of interest in this study

were recorded simultaneously, allowing an intrasubject com-

parison (Fig. 4). It demonstrates that the different components

reported in the group analysis are robust as they can also be

observed at the individual level.

The ERPs recorded from the posterior and middle FG were

characterized by a N110--P160--N240 with a mean onset at ~80ms. The N110, but not the P160, peaked slightly earlier in the

posterior than the middle region. The P160 could be recorded

from both the posterior and the middle FG simultaneously in

some subjects and sometimes from the lateral occipitotemporal

cortex, suggesting the involvement of several occipitotemporal

brain areas.

A delayed N240--P300--N360 triphasic complex was

recorded from mesial structures, notably from the posterior

parahippocampal gyrus, the perirhinal cortex, and the medial

temporal pole (Fig. 3). As shown in the intrasubject compar-

ison, this complex was also recorded from other brain areas

such as the lingual gyrus (Fig. 4), suggesting that it is a common

complex that may concern more regions than those detailed in

this study. Despite the similar morphology of this triphasic

complex, differences in onset and amplitude were observed.

The N240 was prominent in the perirhinal cortex and started

earlier in this region, whereas the N360 was prominent in the

temporal pole.

ERPs to faces recorded from the hippocampus had a quite

different waveform from the other structures, displaying a large

and slow positive component starting at ~160 ms, peaking at

480 ms, and finishing around 750--800 ms. This distinct pattern

suggests that the hippocampus plays a different role in face

processing.

Finally, a low-amplitude negative component (–19 mV)

peaking at 117 ms was recorded from the posterior inferior

frontal gyrus and displayed an interesting analogy with the

N110 recorded from the FG. They were recorded in both

regions in 2 subjects. The N110 from the inferior frontal gyrus

was slightly delayed compared with the N110 from the FG

(Fig. 5). Also, the onset of the N240 recorded in the perirhinal

cortex was found to coincide with the peak of the N110 in

both the group study and the intrasubject comparison (Figs 3

and 4).

In summary, we demonstrated early components peaking at

110 ms poststimulus not only in the FG but also in the inferior

frontal gyrus, a region not usually included in early visual

processing. The N110 is followed by 2 stages of widespread

parallel processing peaking at 240 and 360 ms. Given the

limited spatial sampling of intracerebral electrodes, it is

probable that still other regions participate in this network.

The 240- and 360-ms stages involved many aspects of the visual

ventral stream. In contrast, none of these components were

recorded in the hippocampus indicating that this region is

clearly involved in a different type of processing. These

components do not index independent serial stages but largely

overlap. The P160 is encompassed within the rise of the N240.

Likewise, the N240 and N360 components are encompassed

within the rise of the P480. These results suggest strong

interactions between these stages and, together with the

finding that an early N110 could be recorded in the inferior

frontal gyrus, argue against sequential processing stages.

Rather, they suggest that face recognition may be an emergent

property of these interactions.

Face Recognition

In this section, we compare ERPs to famous and unfamiliar

faces for each time point between 100- and 600-ms post-

stimulus onset using a nonparametric matched-pair signed-

rank Wilcoxon test on individual averaged ERPs (Fig. 6).

No difference between these conditions was found in the

posterior or middle FGs or in the inferior frontal gyrus.

However, significant differences (P < 0.05) were found in all

medial temporal lobe (MTL) regions that were investigated,

starting at 156 ms in the perirhinal cortex (156- to 216-ms

poststimulus onset) followed by the posterior parahippocampal

gyrus (220--248 and 268--326 ms), the medial temporal pole

(315--426 and 450--510 ms), and the hippocampus (322--541

ms). This effect was prominent in the temporal pole and the

hippocampus where 100% of the subjects showed a difference

during some periods (338--406 ms, n = 7, in the temporal pole

and 372--465 ms, n = 6, in the hippocampus). Famous faces

Figure 2. Schematic representation of all brain areas explored. Focal ERPs were recorded from different regions (in gray and black) but were found reliably with reproduciblemorphology at similar latencies only in the areas depicted in black. ERPs recorded in the areas in gray did not meet study criteria. No ERPs were recorded from the areas in white.

1000 Spatio temporal Dynamics of Face Recognition d Barbeau et al.

showed higher amplitudes in all these regions except in the

posterior parahippocampal gyrus where unfamiliar rather than

famous faces showed higher amplitude.

The difference between familiar and unfamiliar faces was

found in the mesial structures for the components N240, N360,

and P480. Together with the finding that the N240 and N360

reflect parallel processing in many different regions, this is

a further indication that recognition may be a property of

a network of activated brain regions rather than depend on a

single region. All these medial structures play a well-known

role in declarative memory, and the different responses in these

regions probably reflect the various aspects of recognition such

as familiarity detection and access to long-term semantic or

episodic memory.

Face Recognition versus Recognition of Other Stimuli

These data demonstrate that a network of brain areas is

involved in face recognition. The question we address in this

section is whether this neural system is a general system

involved in the recognition of any kind of stimulus. Thus, we

compared the ERPs to the famous/unfamiliar faces with the

ERPs to familiar and unfamiliar stimuli in a visual recognition

memory task, in the same series of patients. In this task, the

patients learned trial-unique abstract patterns (Fig. 1) during an

encoding phase, followed a 3-min interfering phase, and then

underwent a recognition phase. The patients had to recognize

the familiar stimuli (learned during the encoding phase) and

reject the unfamiliar stimuli (trial-unique distracters). We did

not try to match faces and the abstract stimuli on any low-level

visual characteristic, our goal in this section being to compare

the recognition processes involved in the 2 tasks.

ERPs to recognized abstract targets are shown in Figure 7.

Early components to familiar abstract patterns and famous faces

are not compared as visual characteristics of the 2 sets of

stimuli differ too much. However, note that the N110 and P160

are of strikingly similar amplitude in the middle FG indicating

a similar level of activity. In contrast, the amplitude to abstract

patterns was significantly lower in all MTL structures, whereas

the difference did not reach significance in the inferior frontal

gyrus.

Computing the difference between familiar and unfamiliar

abstract patterns yielded very different results than for faces as

Figure 3. Comparison of the averaged ERPs to famous faces across regions using a color-coding scheme. The vertical lines are drawn to facilitate comparison of the differentpeaks.

Cerebral Cortex May 2008, V 18 N 5 1001

differences were mainly found in both regions of the FG during

the N160 and N240 (matched-pair signed-rank Wilcoxon test,

P < 0.05). On the other hand, no differences were found in the

perirhinal cortex, hippocampus, and inferior frontal gyrus.

Differences during relatively late periods were seen in the

posterior parahippocampal gyrus (308--322 ms poststimulus)

and medial temporal pole (443--495 ms).

Abstract patterns elicited clearly formed ERPs in all brain

regions in which ERPs to faces were recorded (Fig. 7) with

similar morphologies. Amplitude between the categories of

stimuli was comparable in the middle FG and the inferior

frontal gyrus. In this sense, the network involved in the

recognition of faces is also involved in the recognition of

abstract patterns.

However, significant amplitude differences were found in

the posterior FG and all MTL structures, indicating a degree of

specificity for faces possibly related to additional processes. For

example, the posterior FG shows some early specificity to faces

as areas in this region appear to be preferentially involved in

the processing of faces over other kinds of stimuli (Puce et al.

1995; Kanwisher et al. 1997). Likewise, the amplitude differ-

ences found in MTL structures could be related to the fact that

famous faces carry strong semantic content, whereas abstract

Figure 4. Intrasubject comparison of ERPs to famous faces across regions (subject 1, right hemisphere). ERPs recorded in the lingual gyrus and lateral temporal pole arepresented to demonstrate that the N240--P300--N360 complex and N360 may be even more common than reported in the group study.

Figure 5. ERPs to famous faces recorded from the inferior frontal gyrus and the FG(posterior FG in subject 1, middle FG in subject 12). The N110 peaks slightly earlier inthe FG. Note the different amplitude scales.

1002 Spatio temporal Dynamics of Face Recognition d Barbeau et al.

patterns were newly learned stimuli and carry little semantic

content. In support of this hypothesis, a striking finding was

that a recognition effect for faces was found in all MTL

structures, whereas this effect was considerably smaller for

abstract patterns. In contrast, a recognition effect was seen

mainly in the FG for abstract patterns, whereas no such effect

was found for faces. These results further indicate that

recognition per se can rely on many brain regions and that

this may differ with stimulus category and processes involved.

Hemispheric Asymmetries

In our previous sections, we combined data from both left and

right hemispheres to focus on the general network underlying

face recognition and because data per region were obtained

from small samples (the fact that it was usually unilateral

implantation and the small number of patients per group

prevented direct right/left statistical comparisons). However,

numerous studies have found greater activity for faces in the

right hemisphere. In order to investigate this aspect, the same

data were analyzed for each hemisphere.

As can be observed from Figure 8, both hemispheres are

involved in face recognition. Further, the neuronal populations

involved in face recognition perform comparable processes as

ERPs with similar morphology can be found in both hemi-

spheres. There are 2 notable exceptions, however. First, a large

N240 was observed in the middle FG on the right, whereas this

component was virtually absent on the left. An N240 larger in

the right than in the left was also observed in the perirhinal

cortex. Second, the N110 was very small in the left inferior

frontal gyrus (in one patient, however) compared with the

N110 in the right. Some areas thus appear to be more specific

on the right than on the left. A notable exception was the

finding that the ERP to famous faces showed higher amplitudes

on the left than the on the right in the temporal pole.

Furthermore, 50% of the 18 patients included in this study

had right-hemisphere implantation (left: 39%, bilateral: 11%).

For all regions, we found more ERPs meeting our criteria (focal

and similar morphology across subjects) on the right hemi-

sphere than on the left, that is, the proportion of local ERPs

retained for further analysis was higher on the right than

expected by the number of right implantations (posterior and

middle FG: 60%, posterior parahippocampal gyrus and peri-

rhinal cortex: 67%, medial temporal pole: 57%, hippocampus:

67%, inferior frontal gyrus: 83%). This difference was maximal

in the inferior frontal gyrus with 5 ERPs found in the right

hemisphere for 1 ERP found in the left hemisphere.

Figure 6. ERPs to famous and unfamiliar faces. Asterisk indicates P\ 0.05. For clarity, peak labels are indicated for some brain areas only.

Cerebral Cortex May 2008, V 18 N 5 1003

Consistent with a large literature (Sergent et al. 1992; Allison,

Ginter et al. 1994; Allison et al. 1999; Puce et al. 1996;

Kanwisher et al. 1997; McCarthy et al. 1999; Haxby et al. 1999;

Rossion et al. 2000; Ishai et al. 2005), these data demonstrate

that face recognition relies on a bilateral network with a right

dominance. This was true not only for posterior, visuopercep-

tive regions but also for middle and anterior regions that may

be more involved in memory processing (e.g., posterior para-

hippocampal gyrus, perirhinal cortex, and inferior frontal gyrus).

Discussion

The time course of face recognition processes has been studied

extensively using surface electrophysiological methods. How-

ever, it has generally been difficult to relate these activities to

precise brain regions due to the poor spatial resolution of these

techniques. The brain areas involved in face recognition have

been extensively studied using fMRI and PET, but these

techniques have poor temporal resolution. Here, we meet

these limitations using intracerebral recordings, allowing the

assessment of the precise temporal course of the electrophys-

iological activity from ‘‘within’’ the brain regions involved in

face recognition. We recorded from a large sample of brain

regions across a group of 18 patients and report only the

electrophysiological activities that were similar in morphology

and latency across patients. The brain regions and electrophys-

iological activities identified using this approach are thus

hypothesized to be representative of the main regions and

activities involved in face recognition in the general population.

From these data, we propose a model of the electrophysiolog-

ical activities indexing face recognition, with the particularity

that they are detailed simultaneously in both time and space.

Figure 9 shows a schematic representation of this model,

which confirms and extends the current knowledge on face

recognition. 1) An early (110-ms poststimulus onset) electro-

physiological activity can be recorded not only from the

posterior regions but also from the inferior frontal gyrus. 2)

Around 160 ms, there is activation in both the middle and the

posterior FGs. 3) A stage of massive parallel processing occurs

around 240 ms poststimulus, involving different areas in the

visual ventral stream and closely followed by a second stage

of parallel processing around 360 ms. 4) Long-lasting electro-

physiological activities are recorded from the hippocampus

that appear unrelated to the activities recorded from the visual

ventral stream. 5) There are strong interactions between these

stages because some are encompassed in others, for example,

Figure 7. ERPs to (old) recognized abstract patterns. Same patients as in Figure 5. Left and right hemispheres combined.

1004 Spatio temporal Dynamics of Face Recognition d Barbeau et al.

the N160 on the rise of the N240. 6) Recognition effects

between famous and unfamiliar faces are found in all 4 MTL

regions investigated, suggesting that these effects are distrib-

uted. On the other hand, recognition of abstract patterns mainly

involved posterior regions where no such effect was found for

faces. Overall, these data support the view that face recognition

relies on a distributed network of brain areas, involving both

hemispheres but more prominent in the right, and illustrate the

dynamics of the information flow among these areas.

The finding that face recognition is mediated by a distrib-

uted network of brain structures is in accordance with several

electrophysiological and fMRI studies (Puce et al. 1999;

Leveroni et al. 2000; Paller et al. 2000; Ishai et al. 2002,

2004, 2005; Ishai and Yago 2006). Leveroni et al. (2000)

reported activations in parietal and frontal brain regions for

newly learned faces compared with trial-unique faces. These

authors also compared the recognition of famous faces with

the recognition of these newly learned faces. They found an

even larger network to the famous faces, including areas

in the LTL and MTL, the frontal, and parietal lobes as well as

in cingular and extrastriate structures. This network was

Figure 8. ERPs to famous faces recorded from the left and right hemispheres. For clarity, peak labels are indicated for some brain areas only.

Figure 9. Schematic model of the main electrophysiological activities underlying facerecognition in different brain regions. Components’ names are indicated on theabscissa. The rectangles indicate the main peaks of the components in each region.The horizontal lines provide an idea of the components’ onsets. The rectangles in darkblue indicate the peaks during which a recognition effect was found.

Cerebral Cortex May 2008, V 18 N 5 1005

hypothesized to reflect participation of these regions in long-

term memory.

We identified a distributed cortical network of at least 7

structures involved in face recognition. These included the

posterior and middle FGs, posterior parahippocampal gyrus,

perirhinal cortex, medial temporal pole, hippocampus, and

inferior frontal gyrus. Haxby et al. (2000) reported that face

processing is mediated by a distributed neural system, which

consists of a core and an extended system. The core system

includes the inferior occipital gyrus and FG and the superior

temporal sulcus. The FG is proposed to process invariant

aspects of faces, whereas the superior temporal sulcus would

process changeable aspects such as eye or mouth movements.

The extended system comprises several regions depending on

the information extracted from the face. Pertinent to the

present work, person identification would depend on anterior

temporal structures. The neural network identified in our study

largely supports the model of Haxby et al. as it includes both

the FG as well as anterior temporal lobe structures such as the

perirhinal cortex and temporal pole, expected when subjects

process famous faces. That the perirhinal cortex participates in

face recognition has previously been hypothesized by other

authors (Halgren, Baudena, Heit, Clarke, Marinkovic 1994;

Allison et al. 1999; Trautner et al. 2004). Our data confirm this

assumption as they are the first direct recordings from this area.

Components were also identified in the superior temporal

sulcus in our study, but not reliably across subjects. This

appears consistent with the fact that subjects did not have to

process changeable aspects of the face to perform the task. In

an fMRI study, Ishai et al. (2005) found that face perception is

mediated, independently of the nature of the task, by a cortical

network that includes the inferior occipital gyrus, FG, superior

temporal sulcus, hippocampus, amygdala, inferior frontal gyrus,

and orbitofrontal cortex. Interestingly, this study as well as

others (Leveroni et al. 2000; Ishai et al. 2002) report on 2 brain

structures, the hippocampus and the inferior frontal gyrus,

which we also identified but which were not reported in the

model of Haxby et al. On the other hand, anterior temporal lobe

structures are not reported in Ishai’s study, which is probably

related to the methodology used. These authors searched for

areas activated through various face tasks, rather than

exclusively tasks of face recognition. In contrast, our study

confirms that anterior lobe structures are part of a complemen-

tary system specifically involved in face recognition. Note that

Ishai et al. (2005) also reported on several brain areas that were

not identified in our study (inferior occipito gyrus, amygdala,

and orbitofrontal cortex). This discrepancy is probably related

to our study criteria to report only ERPs that were comparable

in at least 5 subjects and as such does not refute the possibility

that these other brain regions may also contribute to the

network involved in face recognition. It remains, however, to

characterize the temporal course of the neuronal activity in

these regions.

Although in most of the above studies activations were ob-

served in different brain regions, several pieces of information

critical to characterize a network were missing. These include

the notion of connectivity (is there a relation between the

different brain areas?), the direction or causality between these

areas, and the time when these relations occur. Fairhall and

Ishai (2006) recently attempted to meet these issues using

dynamic causal modeling on fMRI data while subjects

processed emotional and famous faces. They found that the

core system is mediated by a feed-forward architecture with

the inferior occipital gyrus exerting influence on the FG and

superior temporal sulcus. Furthermore, the FG was found to

exert a strong causal influence on the orbitofrontal cortex

when processing famous faces and on the amygdala and

inferior frontal gyrus when processing emotional faces.

However, the timing of the FG influence on other brain regions

was not determined and could occur either during the N110,

P160, or the N240, which can be recorded in this region.

The present study identified an N110 in the FG; an N110 was

also identified in the inferior frontal gyrus. However, it was

shown that this component was slightly delayed compared

with the N110 recorded in the posterior FG. It may be

compatible with the notion that rapid feed forward from the

FG occurs during this period, although an alternative is that

a third region (i.e., the lateral occipital gyrus) projects to both

simultaneously. The involvement of prefrontal cortex in face

processing has already been reported in primates (Wilson et al.

1993) and humans in intracranial and fMRI studies (Halgren,

Baudena, Heit, Clarke, Marinkovic, Chauvel, 1994; Allison et al.

1999; Vignal et al. 2000; Ishai et al. 2002, 2005). Marinkovic

et al. (2000) described large ERPs selective to faces in the right

anterior prefrontal region beginning 150 ms after stimulus

onset. However, although an N110 recorded from the FG

had already been reported (Halgren, Baudena, Heit, Clarke,

Marinkovic, Chauvel, 1994; Allison et al. 1999), this is the first

time that a component as early as 110 ms is reliably reported in

the inferior frontal gyrus.

Besides the N110, the causal influence of the FG on other

brain regions could also occur during the P160, which was

recorded in several posterior regions. This component prob-

ably corresponds to the P180 recorded using intracerebral

electrodes (Halgren, Baudena, Heit, Clarke, Marinkovic, 1994)

or to the N200 recorded on the cortical surface (Allison,

McCarthy, et al. 1994; Allison et al. 1999) as well as to the N170

recorded from the scalp surface. The polarity inversion

between intracerebral and surface recordings is related to the

fact that different sides of the generators are captured. The

N110--P160--N240 intracerebral complex thus probably corre-

sponds to the P1--N170--P2 components recorded from the

brain surface. (Note, however, that the N240--P300--N360 that

we also report is probably not polarity inverted and corre-

sponds, at least partly, to the N400 recorded from the surface.

In this case, it is the component (P350) reported by Allison

et al. (1999) which was polarity inverted because they

recorded from the temporal ventral surface and not from

above the source (dipole) as we did or as is done with surface

recordings.) The fact that the P160 was seen at several

occipitotemporal regions is in agreement with previous studies

which showed that this component could be recorded from

a large area covering the ventral occipital and posterior

temporal region (Allison, McCarthy, et al. 1994; Halgren,

Baudena, Heit, Clarke, Marinkovic 1994; Allison et al. 1999),

including simultaneously in different regions in the same

subject. Interestingly, Klopp et al. (2000) showed that during

160- to 230-ms poststimulus onset, a face-selective increase in

coherence was found between the FG and regions in the

temporal, parietal, and frontal lobes. This mechanism may

underlie the causal influence found by Fairhall and Ishai (2006).

The fact that the fusiform P160 was encompassed in the rise of

the N240 components found in different regions corroborates

this analysis.

1006 Spatio temporal Dynamics of Face Recognition d Barbeau et al.

On the other hand, the N110 was also found to be

synchronous with the onset of the N240 in the perirhinal

cortex. These data suggest that the N110 could be a signal

triggering further processing. Specifically, the frontal N110

could be a top-down signal on the visual ventral stream. Such

a mechanism has been postulated to be necessary to allow

more accurate and faster computation (Humphreys et al. 1997;

Tomita et al. 1999; Adolphs 2002; Bar 2003; Bar et al. 2006). Bar

(2003) proposed that low spatial frequencies of an image could

be projected rapidly through the magnocellular pathway from

occipital regions to the inferior frontal gyrus in order to trigger

expectations about the identity of the image itself. This top-

down modulation has been proposed to occur around 250 ms

(Ishai et al. 2006), although the current data suggest that this

can happen considerably earlier. This rapid activation could

then have projections on the visual ventral stream, possibly on

the perirhinal cortex, for early activations.

Furthermore, a N240--P300--N360 complex following the

P160 was observed in different brain regions. In the perirhinal

cortex and temporal pole, this complex may be equivalent to

the facial AMTL-N400 described by Trautner et al. (2004) and

Dietl et al. (2005). It has a comparable latency and polarity,

a similar topography in anterior subhippocampal structures,

elicits a component of higher amplitude to famous faces in the

300- to 600-ms time frame, and is composed of 2 subpeaks (see

Fig. 1a in Dietl et al. 2005 for example). However, the data

reported here indicate that the AMTL-N400 is not a single

component but is clearly divided into 2 subcomponents. A

further important finding of our study is that the N240 and the

N360 were observed in different regions (>5 or 6) where they

were generated locally, suggesting parallel processing. These

regions were located in the visual ventral pathway (Ungerleider

and Mishkin 1982; Ungerleider and Haxby 1994), although the

term pathway is improper here because simultaneous, rather

than serial, activation was observed. Therefore, along with feed-

forward activations, phases of possible top-down signal and

parallel processing are also identified in our study.

The recognition effects found in the present study were all

in MTL structures. This is in accordance with a study that

identified single neurons with invariant representations of

famous faces in several regions of the human MTL including the

amygdala, entorhinal cortex, parahippocampal gyrus, and

hippocampus (Quian Quiroga et al. 2005). It is important to

note that recognizing a face can mean different things: having

a sense of familiarity with a face, activating semantic

knowledge related to a person who has been recognized, or

retrieving the name of that person. In this study, a sense of

familiarity with the face was enough to perform the task, but

complementary processing related to identification may have

been performed. Along this line of thought, we hypothesize

that the activities recorded in MTL structures primarily reflect

access to different aspects of long-term memory. A putative

model could be that the earliest signal from the perirhinal

cortex during the N240 could signal that the stimulus is

familiar, based mainly on the visual characteristics of the

stimulus. This would be congruent with the role of this struc-

ture in visual processing and familiarity detection (Aggleton

and Brown 1999; Murray and Bussey 1999; Barbeau, Felician,

et al. 2005; Barense et al. 2005). The N360 amplitude to famous

faces was higher than to unfamiliar faces in the temporal pole

and, although seen bilaterally, was more prominent in the left

hemisphere. It may indicate access to a distributed semantic

system involving person identification and attempts to name

the faces (Joubert et al. 2006). On the other hand, because the

P480 recorded from the hippocampus showed a component

unrelated to those of the visual ventral pathway, a possibility is

that this component is related to a different system, for

example, episodic (personal) memory. We also suggest that the

MTL is where the major recognition effects are found as the

unique nature of famous faces can trigger memory retrieval. In

accordance with this hypothesis, we found that ERPs to famous

faces showed higher amplitude than to recently learned

abstract patterns. These control stimuli carry little semantic

content or relation to episodic memory, which may explain

why they elicit less neuronal activation in this brain region.

There is a continuing debate over the period during which

recognition effects (i.e., differences between familiar and

unfamiliar faces) can be found. The most robust effects are

seen 250- to 500-ms poststimulus onset when comparing

famous and unfamiliar faces in scalp recordings (Bentin and

Deouell 2000; Eimer 2000; Jemel et al. 2003). The N170, in

contrast, was not modulated, implying that posterior regions

are relatively insensitive to the ‘‘famous’’ factor. In accordance

with these studies, no difference between famous and

unknown faces was observed during the P160 in our study

in either the posterior or the middle FG. Indeed, the

intracranial face-specific P160 has been shown to reflect

a mandatory and invariant processing stage unaffected by

passive viewing of faces, face familiarity, face habituation, or

semantic priming (Puce et al. 1999). This led to the view that

the surface N170 or its intracerebral counterpart may reflect

operations carried out by a ‘‘structural encoding module’’

(Bruce and Young 1986) in charge of automatically processing

physiognomic aspects of the faces (Bentin et al. 1996).

However, recent findings using scalp recordings indicate that

other kinds of familiar faces such as personally known faces

(own face, family members’ faces, or friends’ faces) could

evoke larger N170s (Caharel et al. 2006). The N170 has also

been shown to be modulated by face repetition (Itier and

Taylor 2004a), and we further found in this study that the P160

was modulated by recently learned abstract patterns. These

results emphasize the fact that the mechanisms underlying

recognition could be different for famous faces than for other

types of faces or other types of visual stimuli. In particular,

recognition of repeated, newly learned, or personally known

faces can easily rely on visual clues because they have been

seen repeatedly and/or recently. On the other hand, the

photographs of famous faces are usually seen for the first time

during the experiments, implying that recognition may have

to rely more on person’s identification than on the visual

characteristics of the face.

Funding

Centre National de la Recherche Scientifique postdoctoral

fellowship to E.B.

Notes

EB was supported by a CNRS postdoctoral fellowship. The authors

wish to thank Dr Aileen McGonigal and Dr Fabrice Bartolomei for their

help with the manuscript as well as 2 anonymous reviewers for their

constructive remarks. Conflict of Interest : None declared.

Address correspondence to Emmanuel J. Barbeau, Centre de recherche

Cerveau et Cognition, UMR 5549, Centre National de la Recherche

Cerebral Cortex May 2008, V 18 N 5 1007

Scientifique, Universite Paul Sabatier Toulouse 3, Faculte de Medecine de

Rangueil, 31062 Toulouse Cedex 9, France. Email: emmanuel.barbeau@

cerco.ups-tlse.fr.

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