Tuesday 14 October 2025

Host: Angus Chadwick

Speaker: Stephen José Hanson (Rutgers University)

Title: Scale-Free Brain Networks

Abstract: Scale-free networks SFN arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). It is known that stable hub structure is governed by a phase transition at exponents (>2.0) causing a dramatic change in network structure including a loss of global connectivity, an increasing minimum dominating node set, and a shift towards increasing connectivity growth compared to node growth. Is this SFN shift identifiable in atypical brain activity? The Pareto Distribution (P(O)~O^-β) is a signature of scale-free networks. During resting-state, we assess exponents across a large range of neurotypical and atypical subjects. We use graph complexity theory to provide a predictive theory of the brain network structure. Results. We show that neurotypical resting-state fMRI brain activity possess scale-free Pareto exponents (1.8 se .01) in a single individual scanned over 66 days as well as in 60 different individuals (1.8 se .02) (<2.0). We also show that 60 individuals with Autistic Spectrum Disorder, and 60 individuals with Schizophrenia have significantly higher (>2.0) scale-free exponents ( 2.4 se .03, 2.3 se .04), indicating more fractionated and less controllable dynamics in common brain networks revealed in resting state. We also show using independent classifiers that these exponents and the underlying graph structure and that they also vary with phenotypic measures of Atypical disease severity. This implies that the global topology of the network itself can provide diagnostic biomarkers for atypical brain activity.