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Mitigating head motion artifact in functional connectivity MRI.

Rastko Ciric1, Adon F G Rosen1, Guray Erus2

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Participant motion in functional MRI (fMRI) can distort connectivity data. This protocol offers a robust denoising strategy to effectively remove motion artifacts, improving the accuracy of functional connectivity analyses.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Participant motion during functional magnetic resonance imaging (fMRI) introduces significant signal artifacts.
  • These motion artifacts can confound functional connectivity measures and bias statistical inferences.
  • Accurate analysis of brain connectivity requires effective mitigation of motion-related noise.

Purpose of the Study:

  • To present a validated, high-performance denoising protocol for fMRI data.
  • To counteract motion artifacts and reduce motion-related variance in functional connectivity studies.
  • To provide a method for assessing the performance of fMRI denoising strategies.

Main Methods:

  • Implementation of a denoising strategy combining physiological signals, motion estimates, and mathematical expansions.
  • Targeting both widespread and focal effects of subject movement during fMRI acquisition.
  • Utilizing the eXtensible Connectivity Pipeline (XCP) software and established image-processing libraries.

Main Results:

  • Reduction of motion-related variance to near zero in functional connectivity studies.
  • Achieving up to a 100-fold improvement over minimal-processing approaches in large datasets.
  • The protocol includes methods for assessing denoising strategy performance.

Conclusions:

  • The described protocol effectively denoises fMRI data, minimizing motion-related artifacts.
  • This approach significantly enhances the reliability of functional connectivity analyses.
  • The protocol and associated software provide a comprehensive solution for motion artifact mitigation in neuroimaging research.