Motion impact score for detecting spurious brain-behavior associations

In-scanner head motion introduces systematic bias to resting-state fMRI functional connectivity (FC) not completely removed by denoising algorithms. Researchers studying traits associated with motion (e.g. psychiatric disorders) need to know if their trait-FC relationships are impacted by residual motion to avoid reporting false positive results. We devised Split Half Analysis of Motion Associated Networks (SHAMAN) to assign a motion impact score to specific trait-FC relationships. SHAMAN distinguishes between motion causing overestimation or underestimation of trait-FC effects. We assessed 45 traits from n = 7270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) had significant underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced significant overestimation to 2% (1/45) of traits but did not decrease the number of traits with significant motion underestimation scores.

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Publication Details

Journal
Nature Communications
Published
2025-09-29
DOI
https://doi.org/10.1038/s41467-025-63661-2
Citations
4
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
5.72

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article

Motion impact score for detecting spurious brain-behavior associations

Andrew N. Van, Kristen M. Scheidter, James C. Wilgenbusch, Annie Zheng et al.
4 citations
Nature Communications
Functional Brain Connectivity Studies
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article

Motion impact score for detecting spurious brain-behavior associations

Andrew N. Van, Kristen M. Scheidter, James C. Wilgenbusch, Annie Zheng, Olivia Doyle, Dillan J. Newbold, Benjamin P. Kay, Nico U.F. Dosenbach, Eric Feczko, Deanna M. Barch, Abraham Z. Snyder, Lucille A. Moore, David F. Montez, Michaela Cordova, Roselyne J. Chauvin, Stephen M. Malone, Damien A. Fair, Scott Marek, Brenden Tervo‐Clemmens, Julia Monk, Evan M. Gordon, Jarod L. Roland, Gregory M. Conan, Óscar Miranda-Domínguez, Benjamin J. Lynch, Eric Earl, Samuel R. Krimmel, Joshua S. Siegel, Ryland L. Miller, Timothy O. Laumann, Anders Perrone, Nicole A. Seider, Thomas Pengo, Athanasia Metoki, Vahdeta Suljic, Anita Randolph, Alice M. Graham, Babatunde Adeyemo
article en
4 citations

Abstract

In-scanner head motion introduces systematic bias to resting-state fMRI functional connectivity (FC) not completely removed by denoising algorithms. Researchers studying traits associated with motion (e.g. psychiatric disorders) need to know if their trait-FC relationships are impacted by residual motion to avoid reporting false positive results. We devised Split Half Analysis of Motion Associated Networks (SHAMAN) to assign a motion impact score to specific trait-FC relationships. SHAMAN distinguishes between motion causing overestimation or underestimation of trait-FC effects. We assessed 45 traits from n = 7270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) had significant underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced significant overestimation to 2% (1/45) of traits but did not decrease the number of traits with significant motion underestimation scores.

Nature CommunicationsVol. 16(1)
University of Minnesota (US), University of San Diego (US), Oregon Health & Science University (US), Washington University in St. Louis (US), University of California San Diego (US), NYU Langone Health (US), Mallinckrodt (United States) (US), National Institute of Mental Health (US)
University of Pittsburgh, National Institutes of Health, McDonnell Center for Systems Neuroscience, National Institute of Mental Health
Openalex Percentile: Top 4%
Functional Brain Connectivity Studies
5.72
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