Combining network topology and information theory to construct representative brain networks.
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Publication Date
2021Journal Title
Netw Neurosci
ISSN
2472-1751
Publisher
MIT Press - Journals
Volume
5
Issue
1
Pages
96-124
Language
eng
Type
Article
This Version
AM
Physical Medium
Electronic-eCollection
Metadata
Show full item recordCitation
Luppi, A., & Stamatakis, E. (2021). Combining network topology and information theory to construct representative brain networks.. Netw Neurosci, 5 (1), 96-124. https://doi.org/10.1162/netn_a_00170
Abstract
Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain's network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologies, known as the portrait divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional, and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functional networks based on efficiency-cost optimization-though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.
Sponsorship
Medical Research Council (MR/M009041/1)
Identifiers
External DOI: https://doi.org/10.1162/netn_a_00170
This record's URL: https://www.repository.cam.ac.uk/handle/1810/315144
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