Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

Ali M Golestani; J Jean Chen

doi:10.3389/fnins.2024.1223230

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

Front Neurosci. 2024 Feb 6:18:1223230. doi: 10.3389/fnins.2024.1223230. eCollection 2024.

Authors

Ali M Golestani^{1

2}, J Jean Chen^{3

4

5}

Affiliations

¹ Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.
² Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
³ Rotman Research Institute at Baycrest, Toronto, ON, Canada.
⁴ Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
⁵ Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.

Abstract

Introduction: Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution.

Methods: In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI).

Results: Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group.

Discussion: While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.

Keywords: brain connectivity; data-driven; fMRI; physiological noise; resting-state.

Grants and funding

The authors would like to acknowledge financial support from Canadian Institutes of Health Research (FRN# 126164) and the Canada Research Chairs Program (JC).