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A paradigm for longitudinal complex network analysis over patient cohorts in neuroscience

Published online by Cambridge University Press:  05 August 2019

Heather Shappell ,
Yorghos Tripodis ,
Ronald J. Killiany  and
Eric D. Kolaczyk
Heather Shappell*
Affiliation:
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
Yorghos Tripodis
Affiliation:
Department of Biostatistics, Boston University, Boston, MA, USA (email: Yorghos@bu.edu)
Ronald J. Killiany
Affiliation:
Department of Anatomy and Neurobiology, Boston University, Boston, MA, USA (email: killiany@bu.edu)
Eric D. Kolaczyk
Affiliation:
Department of Mathematics and Statistics, Boston University, Boston, MA, USA (email: kolaczyk@bu.edu)
*
*Corresponding author. Email: hshappe1@jh.edu
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Abstract

The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Many of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs and an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models to the neuroscience setting. Stochastic Actor-Oriented Models are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e. network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state functional magnetic resonance imaging setting with data from the Alzheimer’s Disease Neuroimaging Initiative study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with Alzheimer’s disease.

Keywords

network analysislongitudinal network analysiscomplex brain networksStochastic Actor-Oriented ModelsAlzheimer’s diseaseresting-state fMRIfunctional connectivity
Type
Original Article
Information
Network Science , Volume 7 , Issue 2 , June 2019 , pp. 196 - 214
Copyright
© Cambridge University Press 2019 

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Footnotes

†

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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