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Automatic physiological waveform processing for FMRI noise correction and analysis.

Daniel J Kelley1, Terrence R Oakes, Larry L Greischar

  • 1Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin, Madison, Wisconsin, United States of America. djkelley@wisc.edu

Plos One
|March 19, 2008
PubMed
Summary
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Researchers developed PhysioNoise, an open-source Python program, to process physiological signals for functional MRI (fMRI) noise correction. This tool helps analyze brain connectivity and activation by addressing a gap in current fMRI software capabilities.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Resting-state and connectivity functional MRI (fMRI) studies analyze low-frequency neural fluctuations.
  • These fluctuations overlap with physiological signals from cardiac and respiratory systems.
  • A processing gap exists for high-field physiological data in fMRI analysis.

Purpose of the Study:

  • To address the lack of automated software for processing physiological signals in high magnetic fields.
  • To develop a tool that bridges the gap between physiological data acquisition and fMRI analysis.
  • To enable accurate physiological noise correction and functional brain analyses.

Main Methods:

  • Developed an open-source physiological signal processing program named PhysioNoise.

Related Experiment Videos

  • Implemented the program in the Python language.
  • Tested automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory data.

    • Main Results:

    • PhysioNoise successfully and consistently identifies physiological fluctuations.
    • The software generates necessary covariates for subsequent fMRI analyses.
    • Demonstrated utility for both physiological noise correction and functional connectivity/activation studies.

    Conclusions:

    • PhysioNoise effectively fills the identified gap in the fMRI processing pathway.
    • The tool facilitates improved accuracy in noise correction and functional brain analyses.
    • Provides a valuable open-source resource for the neuroimaging community.