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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
Published on: June 30, 2018
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
Summary
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.
- 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.
