Motion impact score for detecting spurious brain-behavior associations
- Benjamin P. Kay
- David F. Montez
- Scott Marek
- Brenden Tervo-Clemmens
- Joshua S. Siegel
- Babatunde Adeyemo
- Timothy O. Laumann
- Athanasia Metoki
- Roselyne J. Chauvin
- Andrew N. Van
- Vahdeta Suljic
- Samuel R. Krimmel
- Ryland L. Miller
- Dillan J. Newbold
- Annie Zheng
- Nicole A. Seider
- Kristen M. Scheidter
- Julia S. Monk
- Eric Feczko
- Anita Randolph
- Óscar Miranda-Domínguez
- Lucille A. Moore
- Anders J. Perrone
- Gregory M. Conan
- Eric A. Earl
- Stephen M. Malone
- Michaela Cordova
- Olivia Doyle
- Benjamin J. Lynch
- James C. Wilgenbusch
- Thomas Pengo
- Alice M. Graham
- Jarod L. Roland
- Evan M. Gordon
- Abraham Z. Snyder
- Deanna M. Barch
- Damien A. Fair
- Nico U. F. Dosenbach
2025-09-29
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.