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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

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, United States.

Imaging Neuroscience (Cambridge, Mass.)

|February 25, 2026

Summary

SpinalCompCor, a new method using principal component analysis (PCA) for spinal cord functional MRI (fMRI) denoising, effectively models noise but shows no clear group-level benefit. It is best used when physiological recordings are unavailable.

Keywords:

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

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Area of Science:

Neuroimaging
Biomedical Engineering
Data Science

Background:

Spinal cord functional MRI (fMRI) data requires denoising to remove artifacts.
Principal component analysis (PCA)-based denoising techniques, like CompCor, are established for brain fMRI.
Systematic evaluation of PCA-based denoising for spinal cord fMRI 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 effectiveness of SpinalCompCor in reducing noise and improving group-level activation maps.

Main Methods:

SpinalCompCor derives nuisance regressors using PCA from a noise ROI outside the spinal cord and cerebrospinal fluid.
A systematic analysis determined a median of 9 regressors across four fMRI datasets (motor task, breathing task, resting state).
First-level fMRI modeling was used to assess the fit of PCA-derived regressors to noise.

Main Results:

PCA-derived regressors effectively modeled noise, including physiological noise from blood vessels, though effectiveness varied with acquisition parameters.
Group-level activation maps did not demonstrate a clear benefit from including SpinalCompCor regressors.
Potential collinearity between task and regressors was identified as a concern for task-correlated noise.

Conclusions:

SpinalCompCor can model noise in spinal cord fMRI data.
Its utility in improving group-level analyses is not consistently demonstrated.
Denoising with SpinalCompCor is recommended primarily when physiological recordings are unavailable, as it may not consistently replicate recording-based denoising outcomes.