Elsevier

NeuroImage

Volume 154, 1 July 2017, Pages 174-187
NeuroImage

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

https://doi.org/10.1016/j.neuroimage.2017.03.020Get rights and content
Under a Creative Commons license
open access

Highlights

  • We evaluate 14 participant-level de-noising pipelines for functional connectivity.

  • Pipeline performance is markedly heterogeneous.

  • GSR minimizes the impact of motion but introduces distance dependence.

  • Censoring reduces motion and improves network identifiability.

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.

Keywords

fMRI
Functional connectivity
Artifact
Confound
Motion
Noise

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