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 adapted tools from graph theory to characterize differences between h...

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Veröffentlicht in:Network neuroscience (Cambridge, Mass.) Jg. 3; H. 1; S. 1 - 26
Hauptverfasser: Hallquist, Michael N., Hillary, Frank G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 2019
MIT Press Journals, The
The MIT Press
Schlagworte:
Alzheimer's disease
Brain
Brain architecture
Brain disorders
Brain research
Combinatorics
Disorders
Functional morphology
Graph theory
Graphs
Literature reviews
Medical imaging
Mental disorders
Modular structures
Network neuroscience
Neural networks
Neurodegenerative diseases
Neuroimaging
Neurosciences
Populations
Proportional thresholding
Review
Topology
Proportional thresholding
Graph theory
Network neuroscience
Brain disorders
ISSN:2472-1751, 2472-1751
Online-Zugang:Volltext
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Zusammenfassung: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 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 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, 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.
Bibliographie:December, 2018
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Competing Interests: The authors have declared that no competing interests exist.
Handling Editor: Martijn van den Heuvel
ISSN:2472-1751
2472-1751
DOI:10.1162/netn_a_00054