Network Analyses and Nervous System Disorders

TL;DR

This paper reviews how network analysis of neuroimaging data helps understand, characterize, and predict nervous system disorders by modeling brain connectivity disruptions associated with various clinical syndromes.

Contribution

It highlights the application of graph theory to neuroimaging data for modeling complex nervous system disorders and discusses insights into disease mechanisms and intervention targets.

Findings

Disorders disrupt brain's small-world network organization.

Network analysis aids in understanding neurodegeneration and dysfunction.

Potential for identifying anatomical targets for interventions.

Abstract

Network analyses in nervous system disorders involves constructing and analyzing anatomical and functional brain networks from neuroimaging data to describe and predict the clinical syndromes that result from neuropathology. A network view of neurological disease and clinical syndromes facilitates accurate quantitative characterizations and mathematical models of complex nervous system disorders with relatively simple tools drawn from the field of graph theory. Networks are predominantly constructed from in vivo data acquired using physiological and neuroimaging techniques at the macroscale of nervous system organization. Studies support the emerging view that neuropsychiatric and neurological disorders result from pathological processes that disrupt the brain's economically wired small-world organization. The lens of network science offers theoretical insight into progressive neurodegeneration, neuropsychological dysfunction, and potential anatomical targets for intervensions ranging form pharmacological agents to brain stimulation.