Distributed fMRI Patterns Coupled to Low-Frequency Cardiorespiratory Dynamics Provide Markers of Aging

Abstract

How aging affects brain–body connections can be investigated through changes in the coupling between functional magnetic resonance imaging (fMRI) signals and bodily autonomic processes across the adult lifespan. Recent studies using univariate approaches have identified age-related changes in the association between fMRI signals from multiple individual brain regions and low-frequency respiratory and cardiac activity. Here, we investigate if whole-brain spatial fMRI patterns associated with low-frequency physiological processes (heart rate and respiratory volume fluctuations) present generalizable changes with age. Data from human participants of both sexes are included in the analysis. We find that chronological age can be predicted statistically beyond chance from patterns of low-frequency fMRI–physiological coupling, even after accounting for individual differences in physiological signal characteristics and brain anatomy. Notably, brain areas implicated in central autonomic regulation, including nodes within salience and ventral attention networks (e.g., insula and middle cingulate cortex), are among the strongest contributors to age prediction. Furthermore, we observe that after removing physiological effects from fMRI data, the residual blood oxygen level-dependent signal variability is still a reliable indicator of age. Together, these findings underscore the close integration between brain and body physiology and highlight this interaction as a potential biomarker of the aging process.