Applications of Community Detection Techniques to Brain Graphs: Algorithmic Considerations and Implications for Neural Function

The 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 insig...

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Vydáno v:Proceedings of the IEEE Ročník 106; číslo 5; s. 846 - 867
Hlavní autoři: Garcia, Javier O., Ashourvan, Arian, Muldoon, Sarah, Vettel, Jean M., Bassett, Danielle S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive systems
Algorithms
Brain
Brain modeling
Brain networks
Cognition
Communities
community detection
Detection algorithms
Electroencephalography
Graph theory
Graphical representations
Graphs
Human behavior
Magnetic resonance imaging
Maximization
Medical imaging
modu-larity
Modular construction
Modularity
multilayer
multiscale
Neuroimaging
Neurology
Neuroscience
Time series analysis
modularity
brain networks
multi-scale
community detection
multi-layer
ISSN:0018-9219, 1558-2256
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Popis
Shrnutí:The 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 identify communities or modules: 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 application 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 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 usefulness of these approaches in uncovering behaviorally relevant network dynamics in neuroimaging data.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2017.2786710