Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity
Open accessAbstract
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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aCompCor has been shown to be particularly effective for dealing with residual motion relative to other methods.
“Although the influence of head motion cannot be completely removed, aCompCor has been shown to be particularly effective for dealing with residual motion relative to other methods 78.”
From History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity (2019) · cites this paper as [78]
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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.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 154
- Pages
- 174-187
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5483393
- 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
- hybrid
- doi.org
- Licence
- 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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