Performance of Temporal and Spatial Independent Component Analysis in Identifying and Removing Low-Frequency Physiological and Motion Effects in Resting-State fMRI

Ali M Golestani; J Jean Chen

doi:10.3389/fnins.2022.867243

Performance of Temporal and Spatial Independent Component Analysis in Identifying and Removing Low-Frequency Physiological and Motion Effects in Resting-State fMRI

Front Neurosci. 2022 Jun 10:16:867243. doi: 10.3389/fnins.2022.867243. eCollection 2022.

Authors

Ali M Golestani¹, J Jean Chen^{2

3

4}

Affiliations

¹ Department of Psychology, Toronto Neuroimaging Facility, University of Toronto, Toronto, ON, Canada.
² Rotman Research Institute at Baycrest, Toronto, ON, Canada.
³ Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
⁴ Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.

Abstract

Effective separation of signal from noise (including physiological processes and head motion) is one of the chief challenges for improving the sensitivity and specificity of resting-state fMRI (rs-fMRI) measurements and has a profound impact when these noise sources vary between populations. Independent component analysis (ICA) is an approach for addressing these challenges. Conventionally, due to the lower amount of temporal than spatial information in rs-fMRI data, spatial ICA (sICA) is the method of choice. However, with recent developments in accelerated fMRI acquisitions, the temporal information is becoming enriched to the point that the temporal ICA (tICA) has become more feasible. This is particularly relevant as physiological processes and motion exhibit very different spatial and temporal characteristics when it comes to rs-fMRI applications, leading us to conduct a comparison of the performance of sICA and tICA in addressing these types of noise. In this study, we embrace the novel practice of using theory (simulations) to guide our interpretation of empirical data. We find empirically that sICA can identify more noise-related signal components than tICA. However, on the merit of functional-connectivity results, we find that while sICA is more adept at reducing whole-brain motion effects, tICA performs better in dealing with physiological effects. These interpretations are corroborated by our simulation results. The overall message of this study is that if ICA denoising is to be used for rs-fMRI, there is merit in considering a hybrid approach in which physiological and motion-related noise are each corrected for using their respective best-suited ICA approach.

Keywords: fMRI; head motion; physiological noise; resting-state; spatial ICA; temporal ICA.