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Resting-state functional MRI (RS-fMRI) connectivity maps are distorted by venous signals. A phase regressor technique effectively suppresses these macrovascular signals, improving brain network analysis.

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Area of Science:

  • Neuroimaging
  • Brain Connectivity
  • Magnetic Resonance Imaging

Background:

  • Resting-state functional MRI (RS-fMRI) infers brain connectivity but is susceptible to BOLD signal bias from macroscopic veins.
  • Physiological temporal coherences in large veins exacerbate this venous signal distortion in RS-fMRI.
  • Gradient echo based EPI, common in RS-fMRI, shows strong phase responses in veins, enabling signal identification.

Purpose of the Study:

  • To suppress macrovascular venous signal contributions in high-field whole-brain RS-fMRI data.
  • To evaluate the impact of venous signal suppression on spatial localization and correlations of brain networks.
  • To assess the effectiveness of a phase regressor technique for improving RS-fMRI data quality.

Main Methods:

  • Employed a phase regressor technique to identify and remove venous signals from RS-fMRI data.
  • Applied the technique to high-field whole-brain RS-fMRI datasets.
  • Analyzed changes in spatial localization and node correlations at individual and group levels.

Main Results:

  • Suppression of macrovascular signals led to significant changes in the spatial localization of brain networks.
  • Venous signal removal altered correlations between network nodes.
  • These effects were consistent across individual and group analyses, even at lower resolutions.

Conclusions:

  • Venous contamination is a significant confounding factor in RS-fMRI studies.
  • Phase regression effectively suppresses macrovascular signals, improving RS-fMRI data.
  • This method aids in better identification, delineation, and interpretation of large-scale brain networks.