Abstract Vascular health has been shown to be an important factor in the development of neurodegenerative diseases like Alzheimer’s and Parkinson’s diseases. Hence, the ability to measure reliably and quantitatively early hemodynamic changes in the aging brain can be a powerful tool for diagnosing, studying, and developing treatments. Arterial Spin Labeling (ASL) magnetic resonance imaging can yield quantitative perfusion images without the use of contrast agents. We propose that combining new ASL techniques, such as Velocity Selective Inversion (VSI) labeling pulses and magnetic resonance fingerprinting (MRF) with deep learning regression methods will allow quantification of multiple hemodynamic parameters beyond perfusion, thus providing a much more nuanced picture of the state of the vasculature. We also expect that the new technique will offer dramatic improvements in SNR, specificity and sensitivity of ASL, and that the proposed techniques will have many other applications in research and in the clinic. We propose to use these techniques to fill the knowledge gap regarding the relationship between vascular changes and Parkinson’s disease and its symptoms, particularly fatigue, whose pathogenesis is not well understood. If we are successful in this application, future work will use the hemodynamic parameters of interest as biomarkers to assess risk of neurodegeneration, determine therapeutic targets, and guide in the development of new therapies.