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.
Highlights
Resting-state functional magnetic resonance imaging has fundamentally expanded our understanding of the spatiotemporal organization of spontaneous brain activity in health and disease across the life span (Bassett & Bullmore, 2009; Betzel et al, 2014; Biswal, 2012; van den Heuvel & Pol, 2010)
Each de-noising pipeline was evaluated according to the following benchmarks: (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 such as node flexibility and node promiscuity
RESULTS we present the performance of the de-noising pipelines for mitigating motion artifact on (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 such as node flexibility and node promiscuity
Summary
Resting-state functional magnetic resonance imaging (rs-fMRI) has fundamentally expanded our understanding of the spatiotemporal organization of spontaneous brain activity in health and disease across the life span (Bassett & Bullmore, 2009; Betzel et al, 2014; Biswal, 2012; van den Heuvel & Pol, 2010). Emerging network-based tools to describe dynamic rs-fMRI activity (Bassett et al, 2013; Khambhati, Sizemore, Betzel, & Bassett, 2018; Sizemore & Bassett, 2018) have begun to provide important insights into dynamic brain function These insights include an appreciation for how functional connectivity among brain regions (Allen et al, 2014) and the organization of functional brain network architecture (Chai et al, 2017; Smith et al, 2012) change within persons across the length of scans, how these dynamics relate to normative development (Hutchison & Morton, 2016; Medaglia et al, 2018) and cognition (Bassett, Yang, Wymbs, & Grafton, 2015; Braun et al, 2015; Shine, Koyejo, & Poldrack, 2016), and how they are associated with healthy variations in mood (Betzel, Satterthwaite, Gold, & Bassett, 2017) as well as psychopathology (Braun et al, 2016; Damaraju et al, 2014; Demirtaset al., 2016)
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