  Whilst grading neurovascular abnormalities is critical for prompt surgical
repair, no statistical markers are currently available for predicting the risk
of adverse events, such as stroke, and the overall resilience of a network to
vascular complications. The lack of compact, fast, and scalable simulations
with network perturbations impedes the analysis of the vascular resilience to
life-threatening conditions, surgical interventions and long-term follow-up. We
introduce a graph-based approach for efficient simulations, which statistically
estimates biomarkers from a series of perturbations on the patient-specific
vascular network. Analog-equivalent circuits are derived from clinical
angiographies. Vascular graphs embed mechanical attributes modelling the
impedance of a tubular structure with stenosis, tortuosity and complete
occlusions. We evaluate pressure and flow distributions, simulating healthy
topologies and abnormal variants with perturbations in key pathological
scenarios. These describe the intrinsic network resilience to pathology, and
delineate the underlying cerebrovascular autoregulation mechanisms. Lastly, a
putative graph sampling strategy is devised on the same formulation, to support
the topological inference of uncertain neurovascular graphs.
