LAUSR

Abstract Aging and disease‐related changes in the arteriovasculature have been linked to elevated levels of cardiac cycle‐induced pulsatility in the cerebral microcirculation. Functional magnetic resonance imaging (fMRI), acquired fast enough to unalias the cardiac frequency contributions, can be used to study these physiological signals in the brain. Here, we propose an iterative dual regression analysis in the frequency domain to model single voxel power spectra of echo planar imaging (EPI) data using external recordings of the cardiac and respiratory cycles as input. We further show that a data‐driven variant, without external physiological traces, produces comparable results. We use this framework to map and quantify cardiac and respiratory contributions in healthy aging. We found a significant increase in the spatial extent of cardiac modulated white matter voxels with age, whereas the overall strength of cardiac‐related EPI power did not show an age effect. HighlightsWe present a dual regression method to model power spectra of ultra‐fast EPI data to map and quantify physiological contributions.The method was used to study cardiac and respiratory contributions to the EPI data in healthy aging.We found a significant increase in the spatial extent of cardiac modulated voxels in white matter with age.The strength of cardiac‐related EPI power did not show an age effect.