Year: 2025
Type: article
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.
Source: Nature Communications
Authors: 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
Institutions: Washington University in St. Louis, University of Minnesota, NYU Langone Health, United States Department of Health and Human Services, National Institutes of Health, National Institute of Mental Health, University of California San Diego, Oregon Health & Science University, Minnesota Supercomputing Institute
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: U.S. Department of Health and Human Services, University of Pittsburgh, National Spasmodic Dysphonia Association, National Institutes of Health, McDonnell Center for Systems Neuroscience, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Neurological Disorders and Stroke
Awards: G1020259976, G1569016918, G2218553252, G244748725, G2945291071, Motion-robust brain MRI for infants, Brain trajectories in ADHD, Individual-specific functional subdivisions of the human hippocampus in children revealed by precision functional mapping, PEDIATRIC BRAIN INJURY RECOVERY VIA USE-DRIVEN FUNCTIONAL NETWORK REORGANIZATION, G3304296203, G3382646295, G3425616478, Precision Functional Mapping in Treatment-Resistant Depression, G3545437856, G3642085636, Neurodevelopmental profiles related to transdiagnostic and disorder-specific symptoms of anxiety and depression, G3755973507, G3779796333, G4066048162, Visual biofeedback to reduce head motion during MRI scans, G5081895789, G5139799579, Solving the MRI motion problem with Framewise Integrated Real-Time MRI Monitoring (FIRMM) software, Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD, G687916792, G6887863799, G7069890734, G7271102572, Human network plasticity caused by disuse, Neurodevelopmental profiles related to transdiagnostic and disorder-specific symptoms of anxiety and depression, G7458042070, G7543798910, G7546158950, G7652160222, Child Neurologist Career Development Program, G771167098, G7772632206, G7886710174, NINDS CENTER CORE FOR BRAIN IMAGING, G8109884871, G8281824583, Concussion and Attention-Deficit/Hyperactivity Disorder: Investigating the relationship between cognitive outcome, prescription stimulants, and functional MRI biomarkers in the ABCD study., G8511212390, G8562653982, G8762821959, G9000004149, G9000006386, G9000008241, G9000034221, G9000050007, G9000052733, G9000108491, G9000112367, G9000112882, G9000147827, G9000159234, G9000159794, G9000163168, G9000172771, G9000205938, G9000220761, G9000241459, G9000262551, G9000267206, G9000269410, G9000279844, G9000319168, 9/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U MICHIGAN, Precision Functional Mapping in Treatment-Resistant Depression, 8/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT CHLA, 6/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT OHSU, Behavioral feedback and rewards for improving functional brain mapping in presurgical pediatric patients, Characterizing pubertal and age mechanisms of neurodevelopment and association with rising internalizing symptoms, Precision mapping of Parkinson's Disease pathophysiology in the Somato-Cognitive Action Network’s DBS targets, Leveraging complementary big data methods and patient intervention designs to optimize neural markers of adolescent cannabis use, Developmental Neuroscience and Child Psychopathology, Interdisciplinary Training in Cognitive, Computational and Systems Neuroscience (CCSN), 2/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UMB, Functional Connectivity Markers of Cognitive Network Disfunction in Chiari Malformation, G923039674
OpenAlex ID: W4414601623