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

Published on Jul 1, 2017in NeuroImage5.812
· DOI :10.1016/j.neuroimage.2017.03.020
Rastko Ciric12
Estimated H-index: 12
(UPenn: University of Pennsylvania),
Daniel H. Wolf39
Estimated H-index: 39
(UPenn: University of Pennsylvania)
+ 11 AuthorsTheodore D. Satterthwaite44
Estimated H-index: 44
(UPenn: University of Pennsylvania)
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.
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