Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Abstract

Vigilance and wakefulness modulate estimates of functional connectivity, and, if unaccounted for, they can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project. Our results confirm the presence of those fluctuations in the HCP sample despite this data having relatively small inflow weighting. Moreover, we show that fluctuations of a similar frequency appear in large portions of grey matter with different temporal delays, and that they can substantially influence estimates of functional connectivity. Finally, we demonstrate that the temporal evolution of this signal cannot only help us reproduce previously reported overall sleep patterns in resting-state data, but also predict individual periods of eye closure with 70% accuracy, matching predictions attainable using the amplitude of the global signal (a common fMRI marker of arousal). In summary, our results demonstrate the ubiquitous presence of this signal in a large, publicly available, fMRI sample, its value as a marker of arousal in absence of a better metric, its relationship to the global signal, and its potential nuisance effects on functional connectivity estimates when ignored.

Widespread Autonomic Physiological Coupling Across the Brain-Body Axis

“…Spontaneous fluctuations in respiratory volume (RVT), heart rate (HR), peripheral vasculature (low-frequency PPG and PPG pulse amplitude) and pupil dilation signals show strong correlations with the global BOLD signal ( Figure 2C ). Consistent with previous reports of an anti-correlated dynamic between global gray matter BOLD signals and cerebrospinal fluid (CSF) signals (Gonzalez-Castillo et al, 2022; Picchioni et al, 2022), the spatial weights of the global BOLD signal (PC1; Figure 2A ) from the ME-REST and HCP-REST datasets encode an anti-correlated fluctuation between voxels of the ventricles and those of the gray and white matter. Consistent with this pattern, correlations between the time courses of ventricle BOLD signals and the global BOLD signal exhibit strong anti-correlation across subjects ( Figure 2C1 ).…”

“…First, is the anti-correlated fluctuations in BOLD signals observed between gray/white matter tissue and ventricles ( Figure 1 ). While our study did not explicitly measure CSF inflow effects (Fultz et al, 2019), these findings are consistent with anti-correlated fluctuations observed between the CSF (measured from an ROI in the fourth ventricle) and global BOLD signals that become more prominent during periods of low vigilance (Gonzalez-Castillo et al, 2022; Picchioni et al, 2022). Second, is the time-lag or traveling wave structure of spontaneous global BOLD fluctuations and those observed in response to voluntary respiratory changes.…”

“…Further analysis suggested that global BOLD fluctuations and respiratory volume fluctuations are more strongly correlated during these low vigilance episodes. These findings suggest that the amplitude of global BOLD fluctuations (as well as CSF fluctuations) may serve as a potential biomarker of participant vigilance states (Gonzalez-Castillo et al, 2022).…”

“…For the HCP-REST dataset, CSF signals were extracted from subject-level Freesurfer anatomical parcellations in MNI space output from the HCP minimal-preprocessing pipeline (Glasser et al, 2013) as in Gonzalez-Castillo et al, 2022. Lateral and fourth ventricle masks were combined to create a ventricle mask per subject.…”

Task-related hemodynamic responses in human early visual cortex are modulated by task difficulty and behavioral performance

“…Our findings may be related to previous studies identifying a component of the fMRI-BOLD signal related to arousal, but further research is necessary to test if this component and the TRR reflect the same phenomenon ( Chang et al, 2016 ; Özbay et al, 2019 ; Gonzalez-Castillo et al, 2021 ; Goodale et al, 2021 ). One of these studies Goodale et al, 2021 found that the most responsive voxels measured were in the primary sensory cortices, including early visual cortex, and that these voxels alone sufficed for prediction of behavioral performance and an EEG measure of arousal.…”