Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer's disease or depression have increasingly adapted tools from graph theory to characterize dif...

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Bibliographic Details
Published in:bioRxiv
Main Authors: Hallquist, Michael N, Hillary, Frank G
Format: Paper
Language:English
Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 04.04.2018
Cold Spring Harbor Laboratory
Edition:1.3
Subjects:
Alzheimer's disease
Brain architecture
Functional morphology
Nervous system
Neural networks
Neurodegenerative diseases
Neuroscience
network neuroscience
proportional thresholding
brain disorders
graph theory
ISSN:2692-8205, 2692-8205
Online Access:Get full text
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Summary:Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer's disease or depression have increasingly adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four key challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common across studies, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2692-8205
2692-8205
DOI:10.1101/243741