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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Brain Imaging Analysis

Background:

  • Individual differences in head motion during MRI scans can introduce confounding factors in functional connectivity analyses.
  • Accurate interpretation of functional magnetic resonance imaging (fMRI) data relies on minimizing motion artifacts.

Purpose of the Study:

  • To investigate the impact of head motion on functional connectivity using connectome-based predictive modeling (CPM).
  • To assess the predictability of head motion from fMRI data and identify brain regions most affected.

Main Methods:

  • Utilized publicly available fMRI data from 414 individuals with low frame-to-frame motion.
  • Employed connectome-based predictive modeling (CPM) with leave-one-out and twofold cross-validation for motion prediction.
  • Analyzed both task-based and resting-state fMRI data, considering absolute and relative head motion metrics.

Main Results:

  • Strong linear associations were found between observed and predicted head motion.
  • Motion prediction accuracy was higher for task-fMRI than rest-fMRI, and for absolute motion (d) than relative motion (Δd).
  • The cerebellum and default-mode network (DMN) showed consistent vulnerability to head motion effects on connectivity.

Conclusions:

  • Head motion significantly influences functional connectivity patterns, especially within the cerebellum and DMN.
  • Cerebellar and DMN connectivity may reflect motor control signals during fMRI.
  • Findings highlight the importance of accounting for head motion in neuroimaging studies.