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Dynamic encoding of face information in the human fusiform gyrus.

Ghuman AS, Brunet NM, Li Y, Konecky RO, Pyles JA, Walls SA, Destefino V, Wang W, Richardson RM - Nat Commun (2014)

Bottom Line: Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face.Activity between 200 and 500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations.These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses.

View Article: PubMed Central - PubMed

Affiliation: 1] University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, Pennsylvania 15213, USA [2] Department of Neurological Surgery, University of Pittsburgh, 200 Lothrop St, Pittsburgh, Pennsylvania 15213, USA [3] Center for the Neural Basis of Cognition, 4400 Fifth Ave., Pittsburgh, Pennsylvania 15213, USA.

ABSTRACT
Humans' ability to rapidly and accurately detect, identify and classify faces under variable conditions derives from a network of brain regions highly tuned to face information. The fusiform face area (FFA) is thought to be a computational hub for face processing; however, temporal dynamics of face information processing in FFA remains unclear. Here we use multivariate pattern classification to decode the temporal dynamics of expression-invariant face information processing using electrodes placed directly on FFA in humans. Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face. Activity between 200 and 500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations. Long-lasting (500+ms) broadband gamma frequency activity predicted task performance. These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses.

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Locations of electrodes used in the study and their neighboring electrodes on subjects’ native pial surface reconstructionElectrodes in red denote the ones used in the experiment and electrodes in white denote the other contacts on the same electrode strip. A high resolution MRI was not available for pial surface reconstruction of P4 and thus the electrode is visualized on a low resolution T1 MRI slice. MNI coordinates of electrodes are as follows: P1 - (35, -59, −22), (33, −53, −22), (42, −56, −26); P2 - (40, −57, −23); P3 - (−33, −44, −31); P4 - (−38, −36, −30). All electrodes are over the fusiform gyrus.
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Figure 1: Locations of electrodes used in the study and their neighboring electrodes on subjects’ native pial surface reconstructionElectrodes in red denote the ones used in the experiment and electrodes in white denote the other contacts on the same electrode strip. A high resolution MRI was not available for pial surface reconstruction of P4 and thus the electrode is visualized on a low resolution T1 MRI slice. MNI coordinates of electrodes are as follows: P1 - (35, -59, −22), (33, −53, −22), (42, −56, −26); P2 - (40, −57, −23); P3 - (−33, −44, −31); P4 - (−38, −36, −30). All electrodes are over the fusiform gyrus.

Mentions: Finally, how FFA contributes to task-related stages of face processing is undetermined. Specifically, previous studies have described a late, long-lasting (lasting many hundreds of milliseconds) face specific broadband gamma frequency (40+ Hz) activity 6,26,27. Broadband gamma activity is closely related to the underlying population firing rates 28,29, both of which are face selective for many hundreds of milliseconds after seeing a face 14,26,27, extending well beyond the timeframe of face individuation seen in non-human primates 14. It is unknown what role this long-lasting activity plays in face processing. Here we examine whether this long-lasting gamma band activity reflects the maintenance of face information in support of perceptual decision-making and working memory processes 30,31. We used intracranial ECoG in humans and multivariate machine learning methods to document the temporal dynamics of face information processing in the FFA from the moment a face is first viewed through response-related processing. Multivariate pattern classification was used to decode the contents and timecourse of information processing in FFA in order to elucidate the dynamics and computational role of this area in face perception. Electrophysiological activity (specifically the timecourse of the single-trial voltage potentials and broadband gamma frequency power) from the epileptically unaffected FFA was assessed while each of four patients (P1-4) participated in two face processing experiments (see Fig. 1 for electrode locations; all face sensitive electrodes appear to be in mid-fusiform, lateral to the mid-fusiform sulcus, see Weiner et al. [2014] for a detailed description regarding the face sensitive regions of the fusiform). Experiment 1 was adopted to examine the temporal dynamics of face sensitivity and specificity in FFA (e.g. face detection) and experiment 2 was employed to examine the temporal dynamics of face individuation and categorization invariant with respect to facial expression. The results of these experiments demonstrate that within 75 ms of presentation, FFA activity encodes the presence of a face (face detection), between 200-450 ms FFA activity encodes which face it is (face individuation), and late (500+ ms) broadband gamma FFA activity encodes task-related information about faces. These results demonstrate the dynamic contribution of FFA to multiple, temporally distinct face processing stages.


Dynamic encoding of face information in the human fusiform gyrus.

Ghuman AS, Brunet NM, Li Y, Konecky RO, Pyles JA, Walls SA, Destefino V, Wang W, Richardson RM - Nat Commun (2014)

Locations of electrodes used in the study and their neighboring electrodes on subjects’ native pial surface reconstructionElectrodes in red denote the ones used in the experiment and electrodes in white denote the other contacts on the same electrode strip. A high resolution MRI was not available for pial surface reconstruction of P4 and thus the electrode is visualized on a low resolution T1 MRI slice. MNI coordinates of electrodes are as follows: P1 - (35, -59, −22), (33, −53, −22), (42, −56, −26); P2 - (40, −57, −23); P3 - (−33, −44, −31); P4 - (−38, −36, −30). All electrodes are over the fusiform gyrus.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4339092&req=5

Figure 1: Locations of electrodes used in the study and their neighboring electrodes on subjects’ native pial surface reconstructionElectrodes in red denote the ones used in the experiment and electrodes in white denote the other contacts on the same electrode strip. A high resolution MRI was not available for pial surface reconstruction of P4 and thus the electrode is visualized on a low resolution T1 MRI slice. MNI coordinates of electrodes are as follows: P1 - (35, -59, −22), (33, −53, −22), (42, −56, −26); P2 - (40, −57, −23); P3 - (−33, −44, −31); P4 - (−38, −36, −30). All electrodes are over the fusiform gyrus.
Mentions: Finally, how FFA contributes to task-related stages of face processing is undetermined. Specifically, previous studies have described a late, long-lasting (lasting many hundreds of milliseconds) face specific broadband gamma frequency (40+ Hz) activity 6,26,27. Broadband gamma activity is closely related to the underlying population firing rates 28,29, both of which are face selective for many hundreds of milliseconds after seeing a face 14,26,27, extending well beyond the timeframe of face individuation seen in non-human primates 14. It is unknown what role this long-lasting activity plays in face processing. Here we examine whether this long-lasting gamma band activity reflects the maintenance of face information in support of perceptual decision-making and working memory processes 30,31. We used intracranial ECoG in humans and multivariate machine learning methods to document the temporal dynamics of face information processing in the FFA from the moment a face is first viewed through response-related processing. Multivariate pattern classification was used to decode the contents and timecourse of information processing in FFA in order to elucidate the dynamics and computational role of this area in face perception. Electrophysiological activity (specifically the timecourse of the single-trial voltage potentials and broadband gamma frequency power) from the epileptically unaffected FFA was assessed while each of four patients (P1-4) participated in two face processing experiments (see Fig. 1 for electrode locations; all face sensitive electrodes appear to be in mid-fusiform, lateral to the mid-fusiform sulcus, see Weiner et al. [2014] for a detailed description regarding the face sensitive regions of the fusiform). Experiment 1 was adopted to examine the temporal dynamics of face sensitivity and specificity in FFA (e.g. face detection) and experiment 2 was employed to examine the temporal dynamics of face individuation and categorization invariant with respect to facial expression. The results of these experiments demonstrate that within 75 ms of presentation, FFA activity encodes the presence of a face (face detection), between 200-450 ms FFA activity encodes which face it is (face individuation), and late (500+ ms) broadband gamma FFA activity encodes task-related information about faces. These results demonstrate the dynamic contribution of FFA to multiple, temporally distinct face processing stages.

Bottom Line: Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face.Activity between 200 and 500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations.These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses.

View Article: PubMed Central - PubMed

Affiliation: 1] University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, Pennsylvania 15213, USA [2] Department of Neurological Surgery, University of Pittsburgh, 200 Lothrop St, Pittsburgh, Pennsylvania 15213, USA [3] Center for the Neural Basis of Cognition, 4400 Fifth Ave., Pittsburgh, Pennsylvania 15213, USA.

ABSTRACT
Humans' ability to rapidly and accurately detect, identify and classify faces under variable conditions derives from a network of brain regions highly tuned to face information. The fusiform face area (FFA) is thought to be a computational hub for face processing; however, temporal dynamics of face information processing in FFA remains unclear. Here we use multivariate pattern classification to decode the temporal dynamics of expression-invariant face information processing using electrodes placed directly on FFA in humans. Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face. Activity between 200 and 500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations. Long-lasting (500+ms) broadband gamma frequency activity predicted task performance. These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses.

Show MeSH