Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation

Abstract

This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data‐driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal‐to‐noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal–noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting‐state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR‐corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.

Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer’s disease

“…We found that the tSNR difference between RETROICOR without WM/CSF regression (Figure 3, the top row) and RETROICOR with WM/CSF regression (Figure 3, the 2nd row) was relatively minor, suggesting that WM/CSF regression and RETORICOR can be used together without inflating the noise. These results were consistent with previous studies 37,40 using both WM/CSF regression and RETROICOR to suppress the physiological noise.…”

White Matter fMRI Activation Cannot Be Treated as a Nuisance Regressor: Overcoming a Historical Blind Spot

“…Several recent papers that used white matter as a nuisance regressor even cited literature supporting the existence of physiological signals in white matter, but then proceeded regardless of this evidence, offering little in the form of counterargument. For example, Bartoň et al (2019) acknowledged the existence of fMRI detectable white matter activation, but then continued to use white matter signals as a nuisance regressor, with no justification or rationale. Similarly, Yang et al (2019) acknowledged some of the white matter activation literature, but then also disregarded it, citing the “lack of neurons in white matter,” a statement that is clearly anatomically incorrect (Kukley et al, 2007; García-Marín et al, 2010).…”

Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

“…These bundles may convey information related to execution and goal-directed tasks ( 77 – 79 ). Although the white matter BOLD signal was considered as a nuisance signal that was usually regressed out from the dataset during the preprocessing ( 80 ), several studies have reported the white matter fMRI-BOLD signal and disregarded its previous categorization as a blind spot in the functional imaging ( 81 – 85 ). Our robust white matter EEG-fMRI associations with the reduced HRF peak and the amplified HRF trough corroborate this recent blind spot hypothesis.…”