  Cerebrovascular disease is a leading cause of death globally. Prevention and
early intervention are known to be the most effective forms of its management.
Non-invasive imaging methods hold great promises for early stratification, but
at present lack the sensitivity for personalized prognosis. Resting-state
functional magnetic resonance imaging (rs-fMRI), a powerful tool previously
used for mapping neural activity, is available in most hospitals. Here we show
that rs-fMRI can be used to map cerebral hemodynamic function and delineate
impairment. By exploiting time variations in breathing pattern during rs-fMRI,
deep learning enables reproducible mapping of cerebrovascular reactivity (CVR)
and bolus arrive time (BAT) of the human brain using resting-state CO2
fluctuations as a natural 'contrast media'. The deep-learning network was
trained with CVR and BAT maps obtained with a reference method of
CO2-inhalation MRI, which included data from young and older healthy subjects
and patients with Moyamoya disease and brain tumors. We demonstrate the
performance of deep-learning cerebrovascular mapping in the detection of
vascular abnormalities, evaluation of revascularization effects, and vascular
alterations in normal aging. In addition, cerebrovascular maps obtained with
the proposed method exhibited excellent reproducibility in both healthy
volunteers and stroke patients. Deep-learning resting-state vascular imaging
has the potential to become a useful tool in clinical cerebrovascular imaging.
