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Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data.

Molly G Bright1, Kevin Murphy

  • 1Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, CF10 3AT Cardiff, UK. BrightMG@cardiff.ac.uk

Neuroimage
|September 26, 2012
PubMed
Summary
This summary is machine-generated.

Short echo time functional MRI data can correct for noise artifacts in blood oxygenation level dependent (BOLD) signals. This novel method improves functional connectivity analysis by reducing variance caused by head motion and physiological noise.

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Area of Science:

  • Neuroimaging
  • Functional Magnetic Resonance Imaging (fMRI)
  • Signal Processing

Background:

  • Functional connectivity (FC) measures can be confounded by differing noise variances across populations, leading to artifactual group differences.
  • Head motion and physiological fluctuations are significant sources of noise in fMRI data, impacting the reliability of FC analyses.

Purpose of the Study:

  • To investigate the utility of short echo time (TE) fMRI data for correcting noise in blood oxygenation level dependent (BOLD)-weighted time series.
  • To develop and validate a method using dual-echo fMRI to mitigate noise-induced artifacts in FC measures.

Main Methods:

  • A dual-echo fMRI sequence acquired simultaneous short TE (3.3 ms) and BOLD-weighted TE (35 ms) data.
  • Voxelwise regression of short TE data from BOLD data was employed to remove noise variance.
  • Simulated non-compliant behavior (head motion, physiological fluctuations) and resting-state/task-based fMRI data were analyzed.

Main Results:

  • Short TE data correlated significantly with traditional motion and physiological noise regressors.
  • Short TE correction explained additional variance, significantly correlated with head motion (r²=0.93, p<0.0001).
  • Variance reduction post-correction also correlated with head motion (r²=0.48, p=0.027), and activation strength decreased in task data.

Conclusions:

  • Short TE fMRI data effectively capture noise sources, improving FC analysis accuracy.
  • The proposed dual-echo approach offers a cost-effective method for noise correction without increasing scan time.
  • Short TE correction shows promise in distinguishing true FC from noise-driven correlations, particularly in high-noise conditions.