Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.29.177436v1?rss=1 Authors: Maffei, A., Sessa, P. Abstract: Face perception arises from a collective activation of brain regions in the occipital, parietal and temporal cortices. Despite wide acknowledgement that these regions act in an intertwined network, the network behavior itself is poorly understood. Here we present a study in which time-varying connectivity estimated from EEG activity elicited by facial expressions presentation was characterized using graph-theoretical measures of node centrality and global network topology. Results revealed that face perception results from a dynamic reshaping of the network architecture, characterized by the emergence of hubs located in the occipital and temporal regions of the scalp. The importance of these nodes can be observed from early stages of visual processing and reaches a climax in the same time-window in which the face-sensitive N170 is observed. Furthermore, using Granger causality, we found that the time-evolving centrality of these nodes is predictive of ERP amplitude, providing a direct link between the network state and local neural response. Additionally, investigating global network topology by means of small-worldness and modularity, we found that face processing requires a functional network with a strong small-world organization that maximizes integration, at the cost of segregated modular subdivisions. Interestingly, we found that this architecture is not static, but instead it is dynamically implemented by the network from stimulus onset to ~200 msec. Altogether, this study shows that recognizing facial expressions relies on a distributed information processing mechanism that dynamically weights the contribution of the cortical regions involved. Copy rights belong to original authors. Visit the link for more info