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Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Neha A Reddy1,2, Kristina M Zvolanek1,2, Stefano Moia3,4,5

  • 1Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.

Biorxiv : the Preprint Server for Biology
|July 28, 2023
PubMed
Summary

Multi-echo independent component analysis (ME-ICA) effectively reduces head motion artifacts in motor-task functional MRI (fMRI) data. This method improves the reliability of brain activation analysis, especially for clinical populations with significant motion.

Keywords:
BOLD fMRIindependent component analysismotor taskmulti-echotask-correlated head motion

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Area of Science:

  • Neuroimaging
  • Clinical Neuroscience
  • Biomedical Engineering

Background:

  • Motor-task functional magnetic resonance imaging (fMRI) is vital for studying neurological conditions like stroke and Parkinson's disease.
  • Task-correlated head motion is a significant confound in motor-task fMRI, particularly in clinical populations, affecting activation results.
  • Multi-echo independent component analysis (ME-ICA) shows promise in separating head motion from the BOLD signal but requires validation in high-motion motor-task datasets.

Approach:

  • Simulated high head motion in healthy participants performing a hand grasp task to mimic clinical populations.
  • Analyzed fMRI data using single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA models.
  • Compared model performance in mitigating head motion effects at both subject and group levels.

Key Points:

  • ME-ICA demonstrated superior dissociation of head motion from the BOLD signal and reduced noise at the subject level.
  • Both ME models enhanced t-statistics in motor regions; ME-ICA further mitigated artifacts and stabilized estimates in high-motion scans.
  • Group-level analysis with all models revealed expected motor activation clusters, suggesting averaging can resolve subject-specific motion.

Conclusions:

  • ME-ICA is a valuable tool for subject-level analysis of motor-task fMRI data with substantial task-correlated head motion.
  • ME-ICA's improvements are crucial for enhancing the reliability of subject-level activation maps in clinical settings where group analysis may be limited.
  • This technique is particularly relevant for patient cohorts, such as chronic stroke survivors, where individual variability in lesion location and severity complicates group analyses.