Purpose Assessment of brain hemodynamics without exogenous contrast agents is of increasing importance in clinical applications. This study aims to develop an MR perfusion technique that can provide non-contrast and multi-parametric estimation of hemodynamic markers. Methods We devised an Arterial-Spin-Labeling (ASL) method based on the principle of MR Fingerprinting (MRF), referred to as MRF-ASL. By taking advantage of the rich information contained in MRF sequence, up to seven hemodynamic parameters can be estimated concomitantly. Feasibility demonstration, flip angle optimization, comparison with Look-Locker ASL, reproducibility test, sensitivity to hypercapnia challenge, and initial clinical application in an intracranial steno-occlusive process, Moyamoya disease, were performed to evaluate this technique. Results MRF-ASL provided estimation of up to seven parameters, including B1+, tissue T1, cerebral blood flow (CBF), tissue bolus-arrival-time (BAT), pass-through arterial BAT, pass-through blood volume, and pass-through blood travel time. Coefficients-of-variation (CoV) of the estimated parameters ranged from 0.2% to 9.6%. Hypercapnia resulted in an increase in CBF by 57.7%, and a decrease in BAT by 13.7% and 24.8% in tissue and vessels, respectively. Patients with Moyamoya disease showed diminished CBF and lengthened BAT that could not be detected with regular ASL. Conclusion MRF-ASL is a promising technique for non-contrast, multi-parametric perfusion assessment.

SUMMARY The relationship between functional brain activity and gene expression has not been fully explored in the human brain. Here, we identify significant correlations between gene expression in the brain and functional activity by comparing fractional Amplitude of Low Frequency Fluctuations (fALFF) from two independent human fMRI resting state datasets to regional cortical gene expression from a newly generated RNA-seq dataset and two additional gene expression datasets to obtain robust and reproducible correlations. We find significantly more genes correlated with fALFF than expected by chance, and identify specific genes correlated with the imaging signals in multiple expression datasets in the default mode network. Together, these data support a population-level relationship between regional steady state brain gene expression and resting state brain activity.