Revealing the Spatial Pattern of Brain Hemodynamic Sensitivity to Healthy Aging through Sparse Dynamic Causal Model.

Area of Science:

• Neuroscience
• Medical Imaging
• Aging Research

Background:

• Age-related BOLD signal changes may stem from neurovascular coupling, not just neural decline.
• Understanding hemodynamic sensitivity to aging is crucial for interpreting functional neuroimaging data.

Purpose of the Study:

• To decouple neuronal and vascular contributions to the BOLD signal using sparse dynamic causal modeling (sDCM).
• To map the spatial patterns of hemodynamic sensitivity across the lifespan.
• To evaluate hemodynamic features as predictors of biological age.

Main Methods:

• Applied sDCM to resting-state fMRI data from 126 healthy individuals.
• Estimated subject- and region-specific hemodynamic response functions (HRFs).
• Developed and validated an age-classification model using HRF features on an independent cohort of 338 subjects.

Main Results:

• Identified spatially heterogeneous age effects on hemodynamic sensitivity, varying by brain region and population.
• Demonstrated that hemodynamic features, particularly in the right hemisphere, interact with age.
• Confirmed hemodynamic features as independent predictors of biological aging.

Conclusions:

• Age-related hemodynamic changes are region- and population-specific, cautioning against universal correction methods.
• Hemodynamic factors play a significant role in biological aging, independent of neural function.
• sDCM is a valuable tool for dissecting neurovascular contributions to aging-related BOLD signal alterations.