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Data-driven denoising in spinal cord fMRI with principal component analysis.

Kimberly J Hemmerling^1,2, Andrew D Vigotsky^2,3, Charlotte Glanville²

¹Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Biorxiv : the Preprint Server for Biology

|February 3, 2025

Summary

We evaluated SpinalCompCor, a new method for denoising spinal cord functional MRI (fMRI) data using principal component analysis (PCA). While effective at noise reduction, it did not significantly improve motor task activation maps compared to existing models.

Keywords:

denoising functional MRI nuisance regression physiological noise principal component analysis spinal cord

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Area of Science:

Neuroimaging
Biomedical Engineering
Functional Magnetic Resonance Imaging (fMRI)

Background:

Spinal cord functional MRI (fMRI) data requires denoising to remove artifacts.
Principal component analysis (PCA)-based denoising techniques are established for brain fMRI but less evaluated for spinal cord applications.
Systematic evaluation of spinal cord fMRI denoising methods is lacking.

Purpose of the Study:

To formalize and evaluate a PCA-based denoising technique, SpinalCompCor, for spinal cord fMRI.
To determine the optimal number of nuisance regressors derived from a noise region of interest (ROI).
To assess the impact of SpinalCompCor on group-level activation maps for motor tasks.

Main Methods:

SpinalCompCor was developed using PCA on a noise ROI outside the spinal cord and cerebrospinal fluid.
The optimal number of principal components (regressors) was determined across three fMRI datasets (motor task, breathing task, resting state).
First-level fMRI modeling assessed noise reduction, and group-level motor task activation maps were compared with and without SpinalCompCor regressors.

Main Results:

SpinalCompCor effectively modeled noise, particularly physiological noise, in resting-state fMRI.
A median of 11 principal component regressors were identified as optimal across datasets.
Group-level motor task activation maps showed no clear benefit from including SpinalCompCor regressors compared to the original denoising model.

Conclusions:

SpinalCompCor is a viable PCA-based denoising method for spinal cord fMRI, effectively capturing noise.
The method did not demonstrate a significant advantage for group-level motor task activation compared to existing approaches.
Potential collinearity between task-correlated noise and principal component regressors should be considered in future research.