Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI
Kangjoo Lee1, Hui Ming Khoo2, Constance Fourcade3
1Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Duff Medical Building, 3775 Rue University, Montreal, QC H3A 2B4, Canada; Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada.
Magnetic Resonance Imaging
|January 30, 2019
Summary
This study introduces an automated method to remove physiological noise from brain imaging data, improving the accuracy of brain network analysis. The new technique significantly reduces noise, enhancing the reliability of identifying brain connectivity hubs.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Biomedical Engineering
Background:
- Resting state functional magnetic resonance imaging (fMRI) measures brain functional connectivity via signal correlations.
- Sparse dictionary learning decomposes fMRI signals into temporal features (atoms) representing resting state networks.
- The Sparsity-based Analysis of Reliable K-hubness (SPARK) method identifies brain hubs by counting atoms per voxel, but is susceptible to physiological noise.
Purpose of the Study:
- To develop an automated method for classifying and removing physiological noise-related atoms in resting state fMRI data.
- To improve the accuracy and reliability of the SPARK method for analyzing brain network hubs.
- To address the limitations of manual noise classification, which is subjective and time-consuming.
Main Methods:
- Proposed an automated classification of noise-related atoms using spatial priors and stepwise regression, focusing on sagittal sinus proximity.
- Measured atom contribution to noise-characteristic time-courses and used bootstrap resampling for subject-specific thresholds.
- Validated the method on real fMRI data from 25 healthy subjects, comparing automated removal to manual classification.
Main Results:
- Automated noise removal reduced sagittal sinus-related noise from 65% to 19% based on manual classification.
- The automated method demonstrated high accuracy (AUC=0.89) in classifying noise compared to manual reference.
- Reduced k-hubness values in sagittal sinus voxels, indicating improved SPARK analysis at individual and group levels.
Conclusions:
- The proposed automated method effectively identifies and removes physiological noise from resting state fMRI data.
- This significantly enhances the reliability of the SPARK method for brain network hub analysis.
- The automated approach offers a more objective and efficient alternative to manual noise classification, crucial for clinical applications.
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