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Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

Authors: Javier O. Garcia; Arian Ashourvan; Sarah F. Muldoon; Jean M. Vettel; Danielle S. Bassett;

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

Abstract

ABSTRACTThe human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

Subjects by Vocabulary

Microsoft Academic Graph classification: Cognitive science Modularity maximization medicine.diagnostic_test Computer science business.industry media_common.quotation_subject Community structure Cognition Human brain Electroencephalography Machine learning computer.software_genre Network dynamics Graph medicine.anatomical_structure Neuroimaging Perception Neural function medicine Artificial intelligence business computer media_common

Keywords

Article, Electrical and Electronic Engineering

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    71
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
71
Top 1%
Top 10%
Top 1%
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NSF| CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, NIH| Virtual Resection to Treat Epilepsy
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  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01NS099348-03
  • Funding stream: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
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NIH| Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 3R21MH106799-02S1
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH
iis
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NIH| Multimodal brain maturation indices modulating psychopathology and neurocognition
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01MH107235-01
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH
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