Abstract

Between-participant differences in head motion introduce systematic bias to resting state fMRI brain-wide association studies (BWAS) that is not completely removed by denoising algorithms. Researchers who study traits, or phenotypes associated with in-scanner head motion (e.g. psychiatric disorders) need to know if trait-functional connectivity (FC) effects are biased by residual motion artifact in order to avoid reporting false positive results. We devised an adaptable method, Split Half Analysis of Motion Associated Networks (SHAMAN), to assign a motion impact score to specific trait-FC effects. The SHAMAN approach distinguishes between motion artifact causing overestimation or underestimation of trait-FC effects. SHAMAN was > 95% specific at sample sizes of n = 100 and above. SHAMAN was powered to detect motion overestimation scores 80% of the time at sample sizes of n = 5,000 but could detect motion underestimation scores only 50% of the time at n = 5000, making it most useful for researchers seeking to avoid overestimating trait-FC effects in large BWAS. We computed motion impact scores for trait-FC effect with 45 demographic, biophysical, cognitive, and personality traits from n = 7,270 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) of traits had significant motion underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced the proportion of traits with significant motion overestimation scores from 42% to 2% (1/45) but did not decrease the number of traits with significant motion underestimation scores.