The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia

The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia Face recognition is supported by collaborative work of multiple face-responsive regions in the brain. Based on findings from individuals with normal face recognition ability, a neural model has been proposed with the occipital face area (OFA), fusiform face area (FFA), and face-selective posterior superior temporal sulcus (pSTS) as the core face network (CFN) and the rest of the face-responsive regions as the extended face network (EFN). However, little is known about how these regions work collaboratively for face recognition in our daily life. Here we focused on individuals suffering developmental prosopagnosia (DP), a neurodevelopmental disorder specifically impairing face recognition, to shed light on the infrastructure of the neural model of face recognition. Specifically, we used a variant of global brain connectivity method to comprehensively explore resting-state functional connectivity (FC) among face-responsive regions in a large sample of DPs (N = 64). We found that both the FCs within the CFN and those between the CFN and EFN were largely reduced in DP. Importantly, the right OFA and FFA served as the dysconnectivity hubs within the CFN, i.e., FCs concerning these two regions within the CFN were largely disrupted. In addition, DPs' right FFA also showed reduced FCs with the EFN. Moreover, these disrupted FCs were related to DP's behavioral deficit in face recognition, with the FCs from the FFA to the anterior temporal lobe (ATL) and pSTS the most predictive. Based on these findings, we proposed a revised neural model of face recognition demonstrating the relatedness of interactions among face-responsive regions to face recognition. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroimage Elsevier

The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Inc.
ISSN
1053-8119
eISSN
1095-9572
D.O.I.
10.1016/j.neuroimage.2017.12.023
Publisher site
See Article on Publisher Site

Abstract

Face recognition is supported by collaborative work of multiple face-responsive regions in the brain. Based on findings from individuals with normal face recognition ability, a neural model has been proposed with the occipital face area (OFA), fusiform face area (FFA), and face-selective posterior superior temporal sulcus (pSTS) as the core face network (CFN) and the rest of the face-responsive regions as the extended face network (EFN). However, little is known about how these regions work collaboratively for face recognition in our daily life. Here we focused on individuals suffering developmental prosopagnosia (DP), a neurodevelopmental disorder specifically impairing face recognition, to shed light on the infrastructure of the neural model of face recognition. Specifically, we used a variant of global brain connectivity method to comprehensively explore resting-state functional connectivity (FC) among face-responsive regions in a large sample of DPs (N = 64). We found that both the FCs within the CFN and those between the CFN and EFN were largely reduced in DP. Importantly, the right OFA and FFA served as the dysconnectivity hubs within the CFN, i.e., FCs concerning these two regions within the CFN were largely disrupted. In addition, DPs' right FFA also showed reduced FCs with the EFN. Moreover, these disrupted FCs were related to DP's behavioral deficit in face recognition, with the FCs from the FFA to the anterior temporal lobe (ATL) and pSTS the most predictive. Based on these findings, we proposed a revised neural model of face recognition demonstrating the relatedness of interactions among face-responsive regions to face recognition.

Journal

NeuroimageElsevier

Published: Apr 1, 2018

References

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