2025/09/29 by Benjamin P. Kay, David F. Montez, Scott Marek +35 · 1 voice · 6 citations
Medicine · Neuroscience · Psychology · #Advanced Neuroimaging Techniques and Applications #Censoring (clinical trials) #Cognition #Functional Brain Connectivity Studies #Mental Health Research Topics #Motion (physics) #Motion sickness #Residual #Spurious relationship
paper · pdf · doi:10.1038/s41467-025-63661-2
published in Nature Communications 16(1), 8614 (Nature Portfolio)
openalex publication_date 2025/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.