TY - JOUR
T1 - Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity
AU - Ciric, Rastko
AU - Wolf, Daniel H.
AU - Power, Jonathan D.
AU - Roalf, David R.
AU - Baum, Graham L.
AU - Ruparel, Kosha
AU - Shinohara, Russell T.
AU - Elliott, Mark A.
AU - Eickhoff, Simon B.
AU - Davatzikos, Christos
AU - Gur, Ruben C.
AU - Gur, Raquel E.
AU - Bassett, Danielle S.
AU - Satterthwaite, Theodore D.
N1 - Funding Information:
Thanks to the acquisition and recruitment team, including Karthik Prabhakaran and Jeff Valdez. Thanks to Chad Jackson for data management and systems support. Thanks to Monica Calkins for phenotyping expertise. Supported by grants from the National Institute of Mental Health: R01MH107703 (TDS), R01MH107235 (RCG), and R01NS089630 (CD). The PNC was funded through NIMH RC2 grants MH089983, and MH089924 (REG). Additional support was provided by R21MH106799 (DSB & TDS), R01MH101111 (DHW), K01MH102609 (DRR), P50MH096891 (REG), R01NS085211 (RTS), and the Dowshen Program for Neuroscience. DSB acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Mental Health (R01-DC-009209-11), the National Institute of Child Health and Human Development (R01HD086888-01), the Office of Naval Research, and the National Science Foundation (BCS-1441502 and PHY-1554488). Support for developing statistical analyses (RTS & TDS) was provided by a seed grant by the Center for Biomedical Computing and Image Analysis (CBICA) at Penn. Data deposition: The data reported in this paper have been deposited in database of Genotypes and Phenotypes (dbGaP), www.ncbi.nlm.nih.gov/gap(accession no. phs000607.v1.p1).
Publisher Copyright:
© 2017 The Authors
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
KW - Artifact
KW - Confound
KW - Functional connectivity
KW - Motion
KW - Noise
KW - fMRI
UR - http://www.scopus.com/inward/record.url?scp=85016190559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016190559&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2017.03.020
DO - 10.1016/j.neuroimage.2017.03.020
M3 - Article
C2 - 28302591
AN - SCOPUS:85016190559
SN - 1053-8119
VL - 154
SP - 174
EP - 187
JO - NeuroImage
JF - NeuroImage
ER -