Distributed fMRI patterns coupled to low-frequency cardiorespiratory dynamics provide markers of aging

Area of Science:

• Neuroscience
• Gerontology
• Physiology

Background:

• Aging impacts brain-body communication, specifically the coupling between brain activity and autonomic functions.
• Previous studies identified age-related changes in univariate associations between brain regions and physiological signals.
• The generalizability of age-related changes in whole-brain fMRI patterns linked to physiological processes remains unclear.

Purpose of the Study:

• To investigate age-related changes in whole-brain spatial functional magnetic resonance imaging (fMRI) patterns associated with physiological processes.
• To determine if chronological age can be predicted from fMRI-physiology coupling patterns.

Main Methods:

• Analysis of functional magnetic resonance imaging (fMRI) data from human participants across the adult lifespan.
• Statistical modeling to assess the relationship between low-frequency fMRI-physiology coupling and chronological age.
• Examination of brain regions contributing to age prediction and analysis of residual blood oxygen level-dependent (BOLD) signal variability.

Main Results:

• Chronological age was statistically predictable from low-frequency fMRI-physiology coupling patterns.
• Age prediction remained significant after controlling for physiological signal characteristics and brain anatomy.
• Brain regions involved in central autonomic regulation, such as the insula and middle cingulate cortex, were key predictors of age.
• Residual BOLD signal variability, after accounting for physiological effects, also reliably indicated age.

Conclusions:

• The coupling between brain activity and physiological processes exhibits age-dependent changes.
• fMRI-physiology coupling patterns can serve as a potential biomarker for the aging process.
• These findings emphasize the integrated nature of brain and body physiology during aging.