Perception of emotional expressions is independent
of face selectivity in monkey inferior temporal cortex
Fadila Hadj-Bouziane*†, Andrew H. Bell*, Tamara A. Knusten‡, Leslie G. Ungerleider*†, and Roger B. H. Tootell*‡
*Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892; and‡Athinoula A. Martinos
Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
Contributed by Leslie G. Ungerleider, January 31, 2008 (sent for review December 26, 2007)
The ability to perceive and differentiate facial expressions is vital
for social communication. Numerous functional MRI (fMRI) studies
in humans have shown enhanced responses to faces with different
emotional valence, in both the amygdala and the visual cortex.
However, relatively few studies have examined how valence
influences neural responses in monkeys, thereby limiting the
underlying neural mechanisms. Here we tested the effects of
macaque facial expressions on neural activation within these two
regions using fMRI in three awake, behaving monkeys. Monkeys
maintained central fixation while blocks of different monkey facial
tested: (i) neutral, (ii) aggressive (open-mouthed threat), (iii) fear-
that both the amygdala and the inferior temporal cortex in mon-
keys are modulated by facial expressions. As in human fMRI,
fearful expressions evoked the greatest response in monkeys—
even though fearful expressions are physically dissimilar in hu-
mans and macaques. Furthermore, we found that valence effects
were not uniformly distributed over the inferior temporal cortex.
Surprisingly, these valence maps were independent of two related
functional maps: (i) the map of “face-selective” regions (faces
versus non-face objects) and (ii) the map of “face-responsive”
regions (faces versus scrambled images). Thus, the neural mecha-
amygdala ? emotion ? valence ? fMRI
emotional state. For instance, in monkeys a fearful expression
could indicate a nearby predator, whereas a lip smack expression
could reflect social submission. Thus, the interpretation of facial
expressions is crucial for both individual and group survival.
However, the neural bases underlying the recognition of facial
expression remain unclear.
Lesion studies in both humans and monkeys suggest that the
amygdala plays a key role in both the recognition of facial
expressions (1) and the production of behavioral responses to
emotional stimuli (e.g., refs. 2–4). Numerous functional MRI
(fMRI) studies in humans have shown enhanced responses to
faces with emotional valence (e.g., greater responses to fearful
relative to neutral faces) in both the amygdala and visual cortex
(e.g., refs. 5–9; for a review see refs. 10 and 11). As in the human
fMRI studies, several single-unit studies in monkeys have re-
ported modulation of amygdala responses by viewing facial
expressions (e.g., refs. 12–15). A few single-unit studies also
examined the effects of facial expressions in the inferior tem-
poral (IT) cortex, including the superior temporal sulcus (STS)
(16–18), and found that some neurons were also modulated by
Surprisingly, a recent fMRI study in monkeys showed virtually
no effect of facial expression within the ventral visual pathway,
even though valence effects were found in the amygdala (19).
Conceivably, this lack of effect in IT cortex could indicate that
aces are complex stimuli that convey information not only
about an individual’s identity, but also about the individual’s
few neurons are modulated by facial expressions in monkey IT
cortex, relative to its human counterpart. Alternatively, neurons
showing valence effects in monkey IT cortex may be widely
distributed and therefore not easily detectable using fMRI.
Thus, it was important to reexamine whether it is possible to
use fMRI to reveal valence modulation in monkeys. A positive
finding would resolve the currently discrepant fMRI results
between humans and monkeys, thus allowing subsequent clari-
fication of fMRI results using classical invasive techniques (e.g.,
ref. 20). A specific goal here was to reexamine valence effects
within monkey IT cortex using fMRI, comparing regions selec-
tive for faces (relative to non-face objects) with those responsive
for faces (relative to scrambled images). In both humans and
monkeys, the former contrast is the standard measure for face
selectivity (e.g., refs. 21–23) whereas the latter isolates only
regions that are activated by objects versus non-objects. Of
course, the object category includes faces, but, because most
objects are non-faces, this comparison is much less functionally
specific as compared with face selectivity (e.g., refs. 24–26). A
further goal was to compare valence effects in monkey visual
cortex with those observed in the amygdala.
We scanned three awake, fixating monkeys while they viewed
blocks of four different monkey facial expressions (neutral,
threat, fear grin, and lip smack) and scrambled faces (Fig. 1A).
The imaged region included the entire temporal lobe and the
amygdala (e.g., Fig. 1C). Typically, posterior regions of visual
cortex (including V1–V4) and the frontal lobe were not covered.
Perception of Faces: “Face-Responsive“ Versus “Face-Selective” Re-
gions. Relative to scrambled images, the images of neutral faces
elicited widespread activation throughout the temporal cortex as
well as in the amygdala in all three monkeys. As described above,
these regions were defined as face-responsive. As illustrated in
Fig. 2, this face-responsive activation extended bilaterally along
IT cortex, including STS and the IT gyrus, as well as in the lunate
volume included more anterior regions) face-responsive activa-
tion was also found in prefrontal cortex, within the left inferior
bank of the arcuate sulcus, and in the lateral portion of the
orbitofrontal cortex. In all monkeys, the strongest face-
responsive activation was consistently elicited anteriorly within
area TE, at the level of the anterior middle temporal sulcus, and
posteriorly within STS, proximal to the anterior tip of the
Author contributions: R.B.H.T. designed research; T.A.K. performed research; F.H.-B. and
A.H.B. analyzed data; and F.H.-B., L.G.U., and R.B.H.T. wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
†To whom correspondence may be addressed at: National Institute of Mental Health,
Laboratory of Brain and Cognition, 49 Convent Drive, Building 49/1B80, Bethesda,
MD 20892. E-mail: firstname.lastname@example.org or email@example.com.
This article contains supporting information online at www.pnas.org/cgi/content/full/
© 2008 by The National Academy of Sciences of the USA
www.pnas.org?cgi?doi?10.1073?pnas.0800489105 PNAS ?
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posterior middle temporal sulcus (red regions in Fig. 2). Addi-
tionally in monkey E, strong face-responsive activation was
found in the posterior visual cortex, within both the lunate and
the inferior occipital sulci.
We also mapped the activation produced in monkeys E and R
by the presentation of neutral faces relative to non-face objects
[black outlines in Fig. 2, see supporting information (SI) Fig. 6].
These regions were defined as being face-selective. Consistent
with prior fMRI studies (20–22), we found two face-selective
regions (‘‘patches’’) within IT cortex: (i) an anterior face patch,
located in area TE, and (ii) a posterior face patch, located
posteriorly, near/within area TEO (27). As in previous studies,
the anterior face patch was smaller and less robust than the
posterior face patch; in two of four hemispheres mapped the
anterior patch was not statistically significant.
Effect of Facial Expression in Face-Responsive and Face-Selective
Regions Within IT Cortex. Within IT cortex we examined the effect
of facial expression in both face-responsive and face-selective
regions, using size-matched regions of interest (ROIs) in each
region, independently in monkeys E and R (Fig. 3). Face-
selective regions could not be mapped in monkey B for technical
reasons. In each hemisphere, two face-selective ROIs were
two nearby face-responsive ROIs were also selected (see Mate-
rials and Methods). An ANOVA with repeated measures tested
for the effects of (i) ROI selectivity (two levels corresponding to
face-selective and face-responsive); (ii) ROI location (two levels
corresponding to anterior and posterior); (iii) expression (four
levels corresponding to the different expressions); and (iv) the
interaction between the different factors. In both monkeys, a
main effect was found for ROI selectivity [F1–37? 20.0, P ?
0.001; F1–31? 184.3, P ? 0.001 (for monkeys E and R, respec-
tively)]; a stronger response was found to all faces within the
face-selective ROIs as compared with the face-responsive ROIs.
A main effect of ROI location was found only for monkey E
(F1–37? 7.7, P ? 0.009); a stronger response was found for all
faces in the anterior face-selective ROI compared with that in
the posterior face-selective ROI. Additionally, a main effect of
expression was found for both animals [F3–111? 5.6, P ? 0.001;
F3–93? 3.0, P ? 0.033 (for monkeys E and R, respectively)]; the
fear grin expressions consistently elicited a greater response
relative to the neutral expressions. A significant interaction was
also found between ROI selectivity and expression for both
animals [F3–111 ? 3.6, P ? 0.02; F3–93 ? 5.4, P ? 0.003 (for
NEUTRAL THREATFEAR GRINLIP SMACK
700 ms followed by a mask for 300 ms. Four facial expressions were presented
from each of eight different monkeys. (C) Sagittal view of a monkey anatom-
ical scan illustrating the typical location of the slices (in red).
faces (shown in yellow/red), whereas face-selective regions were defined as those showing greater activation for neutral faces relative to non-face objects
(outlined in black). The frontal lobe was imaged only in monkey E. as, arcuate sulcus; ios, inferior occipital sulcus; ls, lateral sulcus; los, lateral orbital sulcus; lus,
lunate sulcus; pmts, posterior middle temporal sulcus; sts, superior temporal sulcus.
Face-responsive versus face-selective regions. Lateral and ventral inflated views show face-responsive regions and face-selective regions in both
www.pnas.org?cgi?doi?10.1073?pnas.0800489105 Hadj-Bouziane et al.
monkeys E and R, respectively)]; the modulation by fear grin
expressions was greater within the face-selective ROIs as com-
pared with face-responsive ROIs. In addition, some idiosyncratic
effects were found across animals; monkey R showed a signif-
icantly decreased response to open-mouthed threats (relative to
neutral, P ? 0.05), whereas monkey E did not. However, all six
hemispheres in our sample showed the largest fMRI increase in
response to fear grins relative to neutral expressions (see SI Fig.
7 for monkey B).
In addition to examining valence effects within these specific
ROIs, we also mapped the distribution of valence effects across
the full expanse of IT cortex. Specifically, we measured the
difference in the fMRI signal evoked by any facial expression
(e.g., threat, fear grin, or lip smack) relative to the neutral facial
expression, within all face-responsive and face-selective regions.
The resulting maps showed that the valence effect was not
distributed uniformly across IT cortex (Fig. 4A). Moreover,
although face-selective regions (black outlines in Fig. 4A) some-
times showed strong valence effects (e.g., the anterior face patch
in the left hemisphere of monkey E), just as often they did not
(e.g., the posterior face patches in the right hemispheres of both
monkeys E and R).
To quantify this relationship further, we calculated the cor-
relation between the magnitude of the valence effect relative to
the magnitude of either (i) face selectivity (face ? object) or (ii)
face responsivity (face ? scrambled) in a voxel-by-voxel manner,
averaged across both animals (Fig. 4B). These tests revealed that
none of the three maps (valence, face selectivity, or face
responsivity) was significantly correlated with any of the other
maps (Fig. 4B). Thus, the voxels showing the strongest face-
selective or face-responsive variation did not correspond to the
voxels with the highest valence modulation, except by chance
solely on fear grins (the expression that produced the strongest
response), no correlation emerged. Thus, in IT cortex, the map
of valence variations is apparently independent of the maps of
variations in face selectivity and face responsivity.
Effect of Facial Expression Within the Amygdala. We also found
face-responsive regions in the amygdala, located bilaterally
within the dorsal portion of the basal and the lateral nuclei of the
amygdala, in all three animals (Fig. 5 A and B). However, no
the more stringent standard of face selectivity.
Fig. 5C shows the amygdala responses to the different facial
expressions from the right hemisphere of monkey B (see also SI
Fig. 8). The fear grin expression evoked significantly greater
activation than the neutral expression across all animals (signif-
icant effect within each hemisphere for each animal, P ? 0.05).
This response profile resembled that found in the visual cortex
(described above), although the response magnitude was con-
siderably smaller in the amygdala (see SI Fig. 8).
Valence Effects in IT Cortex. Using fMRI in monkeys we demon-
strated that the perception of facial expression modulates
activity in some subregions of IT cortex, confirming fMRI
findings in humans (5–9) and single-unit studies in monkeys
(16–18). Our results extend recent findings from Hoffman et
al. (19), who also used fMRI in monkeys; that earlier study
showed a valence effect in amygdala but virtually none in the
visual cortex. Although we cannot explain this discrepancy, it
could be due to technical differences in the two studies (e.g.,
higher contrast/noise due to MION versus BOLD, different
amounts of signal averaging).
Interestingly, we found that the valence modulation was not
uniformly distributed; instead it varied across IT cortex. Fur-
thermore, this valence modulation was not simply overlaid on
previously described functional maps of either face selectivity (2,
21) or face responsivity (e.g., refs. 24–26). In other words, in IT
cortex, the voxels that showed the greatest difference between
faces and non-face objects or between faces and scrambled
images were not necessarily those that showed the greatest
valence modulation. Even the voxels that showed the highest
selectivity for faces (faces ? objects) were not simply the more
activated voxels in the map of face responsivity (faces ? scram-
bled images). The mutual independence of these maps strongly
suggests that there are correspondingly independent neural
mechanisms underlying face perception, valence perception, and
Does the Amygdala Modulate IT Activity? As reported previously
(e.g., refs. 12, 15, and 19), we found face-responsive regions in
the amygdala in the dorsal part of the lateral nucleus, extending
into the basal nucleus. The fMRI signal in these nuclei was
modulated by facial expressions, consistent with prior single-unit
studies (e.g., refs. 12 and 15) and one recent fMRI study (19).
Neuroanatomical studies in monkeys have revealed that visual
information reaches the amygdala through the ventral (‘‘object
recognition’’) pathway, which projects from the primary visual
the anterior IT cortex. Information from area TE then projects
to the amygdala, mainly to the lateral nucleus (28, 29). Feedback
projections from the amygdala arise mainly from the basal
nucleus and terminate in virtually all areas of the ventral visual
pathway, including V1.
It has been proposed that the valence effects evoked in human
visual cortex by fearful faces reflect feedback signals generated
in the amygdala (7, 30), and our results are consistent with this
expressions within two face-selective and two face-responsive ROIs in the left
hemispheres of monkeys E and R. The lateral view illustrates the approximate
location of the selected ROIs. For both the face-selective and face-responsive
regions, the histograms on the left indicate activations in the anterior ROIs,
to the neutral condition for each ROI (P ? 0.05; errors bars indicate the SEM).
N, neutral; T, threat; F, fear grin; L, lip smack.
Hadj-Bouziane et al.
April 8, 2008 ?
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model. First, the valence effect found in the visual cortex
resembled that found in the amygdala, with fear grins producing
the strongest response in all six hemispheres tested. Second,
activation in the amygdala was localized within the nuclei
receiving input from or projecting to the visual cortex (28, 29).
Direct support for this view comes from a recent fMRI study
showing that valence effects were absent within face-responsive
regions of the visual cortex in patients with extended amygdala
% signal change (face>scrambled)
% signal change (face>object)
-1.5 -1-0.50 0.510.511.522.530
% signal change
r = -0.07r = -0.05
of the valence effect within the face-selective regions (outlined in black) and the more extensive face-responsive regions. The significance of the valence effect
reflects the difference in the fMRI signal of any facial expression relative to the neutral expression (i.e., threat versus neutral or fear grin versus neutral or lip
smack versus neutral). (B) Correlation between valence effect and either face responsivity (Left) or face selectivity (Right) across monkeys E and R.
Map of valence effects throughout IT cortex. (A) Lateral inflated views of the left and right hemispheres of monkeys E and R, showing the magnitude
nuclei of the amygdala in all animals. (C) Percent signal change within face-responsive regions in the amygdala in one hemisphere of one monkey, evoked by
nucleus; CE, central nucleus; L, lateral nucleus; M, medial nucleus; N, neutral; T, threat; F, fear grin; L, lip smack.
Valence effect within the amygdala. (A) Coronal section illustrating face-responsive regions within the amygdala for all three animals. (B) Magnified
www.pnas.org?cgi?doi?10.1073?pnas.0800489105Hadj-Bouziane et al.
lesions (31). However, it remains unclear exactly how these
valence effects influence visual cortical processing.
Social Cognition in Primates. Monkey studies of facial expression
have compared the effects of open-mouthed threat versus ‘‘ap-
peasing/submissive’’ expressions (e.g., refs. 16–19). In those
earlier studies, fear grin expressions were not systematically
tested (17, 18)—or, when tested, fear grins were averaged
together with lip smack expressions (19). This is indeed appro-
priate if variations in macaque facial expression are graded along
the axis of dominance–submission. However, lip smack could
instead be considered an affiliative behavior, whereas fear grin
could be an avoidance response, based on a classification along
three axes: dominance, avoidance, and affiliation (32–34).
Here, open-mouthed threat produced varied responses
across animals. Such individual differences in the pattern of
‘‘threat’’ modulation suggest that different animals may per-
ceive distinct expressions according to their level in the social
hierarchy. By contrast, fear grin consistently elicited the
greatest response across animals, within both IT cortex and the
amygdala—analogous to the consistently enhanced response
to fearful faces in human fMRI. Although fear grins in
monkeys and fearful human expressions are physically dissim-
ilar, they presumably convey similar emotional states. Such
increased activity in response to facial expressions could
reflect the ambiguity related to the fear-inducing situations
(35, 36). Angry human faces (as in open-mouthed threat in
monkeys) provide information about both the presence and
the source of a threat, whereas fearful faces provide informa-
tion about the presence of threat but not its source. Thus, a
stronger neural response to fearful faces would presumably
reflect greater attentional engagement to select the most
appropriate behavioral response (35, 36).
Materials and Methods
Subjects and General Procedures. Three male macaque monkeys were used
(Macaca mulatta, 3–5 years, 3–5 kg). All procedures were in accordance with
Massachusetts General Hospital guidelines and are described in detail else-
where (37, 38). Briefly, each monkey was surgically implanted with a plastic
head post under anesthesia. After recovery, monkeys were trained to sit in a
sphinx position in a plastic restraint barrel (Applied Prototype) with their
scanning, gaze location was monitored by using an infrared pupil tracking
Stimuli and Task. Stimuli were presented by using a custom Matlab (Math-
works) program, including PsychToolbox, and displayed via a LCD projector
magnet bore. All stimuli used in this experiment were colored images of
macaque faces, ?25° wide. Images were acquired from eight unfamiliar
monkeys (i.e., eight identities), each with four different expressions in frontal
view (Fig. 1A): (i) neutral, (ii) aggressive (open-mouthed threat), (iii) fearful
(fear grin), and (iv) submissive (lip smack) (39).
Stimuli from each condition were presented in blocks of 40 s each. These
block conditions included the four different emotional expression condi-
tions listed above (equated for the eight identities), a fixation condition
(gray background), and scrambled faces (mosaic scrambled and Fourier
phase scrambled). Each stimulus was presented for 700 ms and was fol-
lowed by a 300-ms mask period (Fig. 1B). Each image was presented five
times per block, with a total of 40 images presented per block. The order
of blocks was randomized across runs. Each stimulus was overlaid with a
small (0.2°) central fixation point, which the monkeys were required to
fixate to receive a liquid reward. In the reward schedule, the frequency of
reward increased as the duration of fixation increased (e.g., refs. 21, 37,
We also mapped the location of face-selective regions in two animals
(monkeys E and R) in a separate study. In those experiments, each block was
devoted to one of four visual stimulus categories: neutral faces, body parts,
objects, and places. Each block lasted 32 s, during which each of 16 images
(?22 ? 22°) was presented for 2 s.
Scanning. Before each experiment, an exogenous contrast agent [monocrys-
fMRI signals (37, 38). Imaging data were collected by using a 3-T Allegra
were obtained by using a multiecho gradient echo sequence, i.e., using two
echoes with alternating phase-encoding direction: TR, 4 s; TE, 30/71 ms; flip
angle, 90°; field of view (FOV), 96 mm; matrix, 64 ? 64; voxel size, 1.5 mm
isotropic; 28 coronal slices (no gap). For monkey E, the FOV was reduced to 80
mm to increase the spatial resolution (1.25 mm isotropic), but all other
scanning parameters remained constant.
In separate scan sessions, high-resolution anatomical scans were obtained
from each monkey under anesthesia (3D MPRAGE; TR, 2.5 s; TE, 4.35 ms; flip
angle, 8°; matrix, 384 ? 384; voxel size, 0.35 mm isotropic). These anatomical
scans were used as an underlay for the functional data and to create anatom-
FreeSurfer (40, 41).
Data Analysis. Functional data were analyzed by using AFNI (42). Images were
smoothed by using a 2-mm full-width half-maximum Gaussian kernel. Signal
intensity was normalized to the mean signal value within each run. A total of
3,720, 3,360, and 3,720 volumes were collected and analyzed for monkeys E,
R, and B, respectively, across two scan sessions per monkey. Data were ana-
lyzed by using a general linear model and a MION kernel to model the
scrambled conditions were used as regressors of interest. Regressors of no
interest included baseline, movement parameters from realignment correc-
tions, and signal drifts (linear as well as quadratic). Note that all fMRI signals
throughout the article have been inverted so that an increase in cerebral
blood volume is represented by an increase in signal intensity. We identified
brain regions that responded more strongly to neutral faces compared with
scrambled images (face-responsive regions) or compared with objects (face-
selective regions). Statistical maps were thresholded to at least P ? 10?9
uncorrected and overlaid onto anatomical scans and/or inflated cortical sur-
Analysis of Activity in Face-Responsive and Face-Selective Regions of Interest.
One main goal was to compare the activity produced by different expressions
within face-responsive and face-selective ROIs. We defined ROIs of ?30 (?5)
the peak activation. For the face-responsive regions, we chose regions medial
to both face-selective regions (one anterior and one posterior) within the STS
and generated an ROI of the same size. The percent signal change was
and averaged these values over the ?35 presentation blocks for each condi-
tion. We performed an ANOVA with three levels, testing for the effect of ROI
sion (neutral, threat, fear grin, and lip smack), followed by multiple paired t
tests and tests for interactions. Analogous tests were performed in the amyg-
Valence Effects Within Face-Responsive and Face-Selective Regions. To further
examine the effect of valence across face-responsive and face-selective regions,
in the fMRI signal between neutral versus threat, neutral versus fear grin, or
neutral versus lip smack expressions. We then tested the relationship between
absolute difference of any expression (threat, fear grin, or lip smack) relative to
neutral expressions. Data from both animals were equated for the number of
voxels sampled and grouped together for this analysis.
ACKNOWLEDGMENTS. We thank Shruti Japee, Ziad Saad, Gang Chang, and
Jennifer Becker for help with the analysis; Katalin Gothard for providing the
original monkey facial expression images; and Helen Deng for her assistance
with animal training. We also thank Byoung Wu Kim for normalizing and
preparing the stimuli, Hans Breiter for discussions and support, and Wim
Vanduffel for his contribution to imaging at Massachusetts General Hospital.
and R01 EY017081 (to R.B.H.T.), the Athinoula A. Martinos Center for Bio-
medical Imaging, the National Center for Research Resources, and the Na-
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