Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

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TitleBenchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.
Publication TypeJournal Article
Year of Publication2017
AuthorsCiric, R, Wolf, DH, Power, JD, Roalf, DR, Baum, GL, Ruparel, K, Shinohara, RT, Elliott, MA, Eickhoff, SB, Davatzikos, C, Gur, RC, Gur, RE, Bassett, DS, Satterthwaite, TD
JournalNeuroimage
Volume154
Pagination174-187
Date Published2017 Jul 01
ISSN1095-9572
KeywordsAdolescent, Adult, Benchmarking, Child, Connectome, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Young Adult
Abstract

Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.

DOI10.1016/j.neuroimage.2017.03.020
Alternate JournalNeuroimage
PubMed ID28302591
PubMed Central IDPMC5483393
Grant ListP50 MH096891 / MH / NIMH NIH HHS / United States
R01 EB022573 / EB / NIBIB NIH HHS / United States
RC2 MH089973 / MH / NIMH NIH HHS / United States
R01 MH107235 / MH / NIMH NIH HHS / United States
R01 DC009209 / DC / NIDCD NIH HHS / United States
R21 MH106799 / MH / NIMH NIH HHS / United States
R01 MH101111 / MH / NIMH NIH HHS / United States
RC2 MH089924 / MH / NIMH NIH HHS / United States
K01 MH102609 / MH / NIMH NIH HHS / United States
R01 HD086888 / HD / NICHD NIH HHS / United States
R01 NS085211 / NS / NINDS NIH HHS / United States
R01 MH107703 / MH / NIMH NIH HHS / United States
RC2 MH089983 / MH / NIMH NIH HHS / United States