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Neuroimage. 2017 Jul 1;154:174-187. doi: 10.1016/j.neuroimage.2017.03.020. Epub 2017 Mar 14.

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

Author information

1
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
2
Department of Psychiatry, Weill Cornell Medical College, NY, NY, USA.
3
Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
4
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
5
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany.
6
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
7
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
8
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: sattertt@mail.med.upenn.edu.

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:

Artifact; Confound; Functional connectivity; Motion; Noise; fMRI

PMID:
28302591
PMCID:
PMC5483393
DOI:
10.1016/j.neuroimage.2017.03.020
[Indexed for MEDLINE]
Free PMC Article

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