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 - 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 -