Identifying brain network topology changes in task processes and psychiatric disorders

Abstract

A central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders.

Resting-state network topology characterizing callous-unemotional traits in adolescence

“…Modeling this as well as other important network function and architecture involves modeling the way networks are arranged, or their topology ( De Vico Fallani et al, 2014 ). Network topology captures important features including relations between regions that have shown to be important for understanding health and mental health disorders ( Rezaeinia et al, 2020 , Stiso and Bassett, 2018 ) making it appropriate to leverage for understanding CU traits.…”

Resting-state network topology characterizing callous-unemotional traits in adolescence

“…Modeling this as well as other important network function and architecture involves modeling the way networks are arranged, or their topology (De Vico Fallani et al, 2014). Network topology captures important features including relations between regions that have shown to be important for understanding health and mental health disorders (Rezaeinia et al, 2020;Stiso & Bassett, 2018) making it appropriate to leverage for understanding CU traits.…”

Metrics for Graph Comparison: A Practitioner’s Guide

“…The relevance of the current study to this burgeoning research area stems from the exploration of the relationship between the structural features characteristic of several graph ensembles and the sensitivity of the distances to these features. The distributions associated with these features can then be used to define a probability measure associated with a given graph (e.g., [79] where the distribution of hitting times is used to characterize a functional brain connectivity network).…”