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Netw Neurosci. 2019 Feb 1;3(2):427-454. doi: 10.1162/netn_a_00071. eCollection 2019.

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

Author information

1
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
2
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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.

KEYWORDS:

Artifact; Confound; Dynamic functional connectivity; Dynamic networks; Motion; Resting-state fMRI

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

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