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<p>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 <i>n</i> = 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 (<i>p</i> &lt; 0.05) motion overestimation scores and 38% (17/45) had significant underestimation scores. Censoring at framewise displacement (FD) &lt; 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.</p>

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Motion impact score for detecting spurious brain-behavior associations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • 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

摘要

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.