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Analysis of population functional connectivity data via multilayer network embeddings

Published online by Cambridge University Press:  21 October 2020

James D. Wilson Open the ORCID record for James D. Wilson [Opens in a new window] ,
Melanie Baybay ,
Rishi Sankar ,
Paul Stillman  and
Abbie M. Popa
James D. Wilson*
Affiliation:
Department of Mathematics and Statistics, University of San Francisco, San Francisco, CA94117, USA The Data Institute, University of San Francisco, San Francisco, CA94117, USA (e-mail: [email protected])
Melanie Baybay
Affiliation:
Department of Computer Science, University of San Francisco, San Francisco, CA94117, USA (e-mail: [email protected])
Rishi Sankar
Affiliation:
Department of Computer Science, University of California, Los Angeles, CA90095, USA (e-mail: [email protected])
Paul Stillman
Affiliation:
Department of Marketing, Yale School of Management, New Haven, CT06511, USA (e-mail: [email protected])
Abbie M. Popa
Affiliation:
The Data Institute, University of San Francisco, San Francisco, CA94117, USA (e-mail: [email protected])
*
*Corresponding author. Email: [email protected]
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Abstract

Population analyses of functional connectivity have provided a rich understanding of how brain function differs across time, individual, and cognitive task. An important but challenging task in such population analyses is the identification of reliable features that describe the function of the brain, while accounting for individual heterogeneity. Our work is motivated by two particularly important challenges in this area: first, how can one analyze functional connectivity data over populations of individuals, and second, how can one use these analyses to infer group similarities and differences. Motivated by these challenges, we model population connectivity data as a multilayer network and develop the multi-node2vec algorithm, an efficient and scalable embedding method that automatically learns continuous node feature representations from multilayer networks. We use multi-node2vec to analyze resting state fMRI scans over a group of 74 healthy individuals and 60 patients with schizophrenia. We demonstrate how multilayer network embeddings can be used to visualize, cluster, and classify functional regions of the brain for these individuals. We furthermore compare the multilayer network embeddings of the two groups. We identify significant differences between the groups in the default mode network and salience network—findings that are supported by the triple network model theory of cognitive organization. Our findings reveal that multi-node2vec is a powerful and reliable method for analyzing multilayer networks. Data and publicly available code are available at https://github.com/jdwilson4/multi-node2vec.

Keywords

multilayer networksnetwork embeddingnode2vecSkip-gramfunctional connectivityimagingnetwork neuroscience
Type
Research Article
Information
Network Science , Volume 9 , Issue 1 , March 2021 , pp. 99 - 122
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

Action Editor: Filippo Menczer

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