Identifying brain network topology changes in task processes and psychiatric disorders.

Paria Rezaeinia,François G Meyer,R Mckell Carter,Piya Pal,Kim Fairley

doi:10.1162/netn_a_00122

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.

Highlights

IntroductionHow do we link dynamic changes in functional brain structure to the processing of information?
How do we link dynamic changes in functional brain structure to the processing of information? Brain activity organizes into stable networks that vary in strength and change with task demands (Greicius, Krasnow, Reiss, & Menon, 2003; Smith et al, 2009)
We focus on the tails of random-walk network connectivity distributions to address the following four key questions. (a) How does the relative isolation of a linear chain of nodes change the distribution of connectivity in a synthetic network? (b) Are there subnetworks in restingstate cortex that have properties similar to a linear chain of nodes? (c) How are linear-chain subnetworks changed by task demands? (d) Does the characterization of network topology have value in understanding and diagnosing psychiatric disorders?

Summary

Introduction

How do we link dynamic changes in functional brain structure to the processing of information? Brain activity organizes into stable networks that vary in strength and change with task demands (Greicius, Krasnow, Reiss, & Menon, 2003; Smith et al, 2009). Because of its ease of implementation and relatively low cost, the analysis of resting functional magnetic resonance imaging (rfMRI) data (Raichle et al, 2001) in particular has had a tremendous impact, leading. Brain network topologies in task and disorder fMRI: Functional Magnetic Resonance Imaging: A resonance imaging technique used to detect blood oxygen levels changes in the brain, used as an aggregate measure of neural activity in the imaged region. Functional connectivity: A measure of the functional relationship between two regions of the brain, generally via a measure of similarity between time courses from those regions.

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Conclusion