A Hypersampling Method to Resolve All Physiological Pulsations in fMRI Signals, Revealing Age-related Differences

Motivation: fMRI captures rich physiological signals, but cardiac pulsations are typically undersampled due to low temporal resolution. Goal(s): To develop a hypersampling method that resolves all physiological frequencies (vasomotion, respiration, cardiac pulsation) in fMRI data and study age-related differences across brain regions. Approach: By leveraging slice-timing offsets in 2D acquisitions, we hypersampled fMRI signals to analyze physiological power in fluid (arterial/venous blood and CSF) and tissue (gray and white matter) compartments across ages. Results: (1) Hypersampling successfully resolved three physiological pulsations; (2) Significant age-related differences were observed in all brain compartments; (3) Hypersampling was validated in a fast fMRI dataset. Impact: Our innovative hypersampling technique demonstrates the ability to extract physiological signals from fMRI data that are limited by temporal resolution. This method enables researchers to analyze existing fMRI datasets to investigate all physiological signals associated with brain function and disease.