Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2019
- Source ID
- 10.1162/netn_a_00071
Entities
People
- Danielle Bassett
- David M Lydon
- Rastko Ciric
- Theodore D Satterthwaite
Organizations
- Alfred P. Sloan Foundation
- Army Research Office
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Institute for Scientific Interchange
- John D. and Catherine T. MacArthur Foundation
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke
- National Science Foundation
- Office of Naval Research
- United States Army Research Laboratory
- University of Pennsylvania