Topological analysis of overlapping dynamic brain networks under naturalistic stimuli based on sparse non-negative matrix factorization algorithm

Abstract

Based on the overlapping hypothesis of human brain networks, the sparse non-negative matrix factorization algorithm was used to decompose the dynamic brain functional networks under naturalistic stimuli into a set of spatiotemporal overlapping subgraphs and weight coefficients. This is followed by an analysis of how these subgraphs characterize the topographies in dynamic brain networks. It is also found that specific local topology is modulated by emotional activities. Default Mode Network, Ventral Attention Network, Auditory Network, Visual Network, and some brain regions in other cognitive networks are mainly involved. Our results are expected to provide solutions for future brain function detection and diagnosis of brain diseases based on naturalistic stimuli.