Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity.

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TitleEvaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity.
Publication TypeJournal Article
Year of Publication2019
AuthorsLydon-Staley, DM, Ciric, R, Satterthwaite, TD, Bassett, DS
JournalNetw Neurosci
Volume3
Issue2
Pagination427-454
Date Published2019
ISSN2472-1751
Abstract

Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced by participant motion. This report provides a systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths (ages 8-22 years). Each strategy was evaluated according to a number of benchmarks, including (a) the residual association between participant motion and edge dispersion, (b) distance-dependent effects of motion on edge dispersion, (c) the degree to which functional subnetworks could be identified by multilayer modularity maximization, and (d) measures of module reconfiguration, including node flexibility and node promiscuity. Results indicate variability in the effectiveness of the evaluated pipelines across benchmarks. Methods that included global signal regression were the most consistently effective de-noising strategies.

DOI10.1162/netn_a_00071
Alternate JournalNetw Neurosci
PubMed ID30793090
PubMed Central IDPMC6370491
Grant ListR01 NS099348 / NS / NINDS NIH HHS / United States