Gold, Benjamin P.; Goodale, Sarah E.; Zhao, Chong; Pourmotabbed, Haatef; de Zwart, Jacco A.; Özbay, Pinar S.; Bolt, Taylor S.; Duyn, Jeff H.; Chen, Jingyuan E.; Chang, Catie. “Functional MRI signals exhibit stronger covariation with peripheral autonomic measures as vigilance decreases.” Imaging Neuroscience 2 (2024): 1-25. https://doi.org/10.1162/imag_a_00287.
Vigilance—our level of alertness or attention—naturally rises and falls over time. These shifts are known to affect signals seen in brain scans, such as those from functional magnetic resonance imaging (fMRI), though the exact cause of these changes isn’t fully understood. Separate studies have connected changes in vigilance not only to brain signal patterns but also to changes in physical responses controlled by the autonomic nervous system, such as breathing and heart rate. This raises the question: could some of the brain signal changes actually be caused by these bodily responses?
To explore this, we recorded fMRI scans alongside measures of brain activity (EEG), breathing, and blood oxygen levels, while people were either resting or doing a task that required attention. We found that the link between the body’s automatic functions (like pulse and respiration) and brain signals became stronger as people became less alert. These body-related signals first showed quick positive connections with brain activity, then slower negative ones, with some later positive responses in fluid-filled spaces of the brain.
We also saw that fluctuations in EEG (a measure of brainwave activity) depended on alertness level and were related to both brain and body signals. Additionally, the strength of communication between different brain regions (called functional connectivity) increased when people were less alert—especially during rest. But when we removed the influence of body signals from the fMRI data, this increase mostly disappeared.
Overall, our results show that changes in alertness affect not just brain activity but also how the body and brain interact. This understanding helps scientists more accurately interpret fMRI data by highlighting the important role of physiological changes.
Fig 1.
Comparing EEG, fMRI, autonomic, and behavioral measures across time windows. (A) Simultaneous EEG, fMRI, and autonomic data were divided into non-overlapping windows of 126 s each. This panel shows three representative, contiguous windows (the fourth, fifth, and sixth windows from rest participant 3), including their “fast” (i.e., seconds-level) and baseline (i.e., window-averaged) EEG alpha/theta ratios, for a participant in the process of falling asleep. (B) For the task scans, we compared the mean of the EEG alpha/theta power ratio within each window (which we define as “baseline vigilance”) to the mean reaction time in each window with Spearman’s rank correlations for non-normal distributions. Significant negative correlations, whether excluding trials without responses (“Responses only”) or including them as indicating arbitrarily long reaction times of 4 s (“All trials”), support the use of an EEG alpha/theta ratio as a measure of vigilance in this study. (C) The temporal variance of the percent signal change in the fMRI global signal also exhibited a negative relationship with baseline vigilance levels (shifted by 4.2 s in this case to accommodate the hemodynamic delay of the fMRI signal). This effect was significant for both resting-state and task data, indicating greater global fMRI variability as baseline vigilance decreases. Although the correlation values shown in (B–C) are based on non-parametric statistics, we include least-squares trend lines for visualization. RV = respiratory volume, HR = heart rate, PWA = pulse wave amplitude.
