Cited by 1,042Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

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

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Ciric, Rastko, Wolf, Daniel H., Power, Jonathan D., Roalf, David R., Baum, Graham L., Ruparel, Kosha, Shinohara, Russell T., Elliott, Mark A. +6 more·2017·NeuroImage

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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Authors
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., Gur, R. C., Gur, R. E., Bassett, D. S., & Satterthwaite, T. D.
Affiliations
  • University of Pennsylvania (US)
  • Weill Cornell Medicine (US)
  • Forschungszentrum Jülich (DE)
Corresponding author
Satterthwaite, Theodore D.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2017-07
Volume
154
Pages
174-187
ISSN
1053-8119 (ISSN-L)
OpenAlex
W2597348720
Cited by
1,042
References
84
Citations by year
2026: 108 · 2025: 105 · 2024: 118 · 2023: 103 · 2022: 124 · 2021: 108 · 2020: 130 · 2019: 123 · 2018: 85 · 2017: 37
FWCI
58.83
Type
journal article
Language
en
Open access
Locations
  • NeuroImage (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute of Child Health and Human Development (R01HD086888-01)
  • National Science Foundation (BCS-1441502, PHY-1554488)
  • Office of Naval Research
  • Alfred P. Sloan Foundation
  • John D. and Catherine T. MacArthur Foundation
  • Army Research Office (R01-DC-009209-11, W911NF-14-1-0679, W911NF-10-2-0022)
  • National Institute of Mental Health (R01MH107703, R01MH107235, R01NS089630)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
motion artifacts, functional connectivity, global signal regression, censoring, network identifiability, modular network architecture, connectome, degrees of freedom, benchmarking
MeSH terms
Adolescent, Adult, Child, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Benchmarking, Young Adult, Connectome

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