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In-scanner head motion biases resting-state fMRI functional connectivity (FC) findings. A new method, SHAMAN, quantifies this motion bias, revealing that standard processing overestimates many trait-FC relationships, while underestimation persists even after motion censoring.

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Brain Imaging

Background:

  • In-scanner head motion is a significant confound in resting-state fMRI (rs-fMRI) studies.
  • Residual motion bias can lead to false positive findings in trait-functional connectivity (FC) research.
  • Existing denoising methods do not fully eliminate motion-related artifacts.

Purpose of the Study:

  • To develop and validate a method (SHAMAN) for quantifying motion's impact on trait-FC relationships.
  • To assess the extent of motion-induced overestimation and underestimation of trait-FC effects.
  • To evaluate the effectiveness of motion censoring in mitigating these biases.

Main Methods:

  • Developed Split Half Analysis of Motion Associated Networks (SHAMAN) to score motion impact on trait-FC relationships.
  • Applied SHAMAN to 45 traits in 7270 participants from the Adolescent Brain Cognitive Development (ABCD) Study.
  • Compared results from standard denoising without censoring to censoring at framewise displacement (FD) < 0.2 mm.

Main Results:

  • Without motion censoring, 42% of traits showed significant motion overestimation and 38% showed significant underestimation.
  • Motion censoring (FD < 0.2 mm) drastically reduced significant overestimation to 2%.
  • Motion censoring did not reduce the number of traits with significant motion underestimation.

Conclusions:

  • Residual head motion significantly biases trait-FC relationships in rs-fMRI data.
  • SHAMAN provides a valuable tool for identifying and characterizing motion-related biases.
  • Standard denoising and motion censoring strategies are insufficient to fully correct for motion-induced underestimation biases.