Exploring dynamic hubs for face perception in the brain: A graph theoretical measure approach

Abstract

The human brain operates as a highly complex system, characterized by extensive communication among various sub-networks while perceiving a face. The challenge lies in identifying the distinct active modules responsible while executing the task of face perception within the human brain. Here, we have attempted to investigate the dynamics of hubs in face perception networks using graph measure analysis. EEG data was acquired from 15 healthy subjects while presenting the face-object paradigm to participants. Hub-related measures (transitivity, modularity, characteristic path length, global efficiency) and centrality measures (betweenness, closeness, eigenvector centrality, participation coefficient) are evaluated over time after stimulus onset. These measures are also evaluated across different EEG frequency bands and over the time length of stimuli at each frequency band. Our findings revealed that the processing of face perception in the brain unfolds, exhibiting information processing in both intra-module and inter-modules. Moreover, we identified community networks dedicated to face processing in the brain over time and in different frequency bands, illustrating the evolving nature of these communities following stimulus onset. This comprehensive exploration delves into the brain network dynamics of face perception in the human brain and sheds light on their relevance in understanding neurological disorders and cognitive functions.