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Whilst grading neurovascular abnormalities is critical for prompt surgicalrepair, no statistical markers are currently available for predicting the riskof adverse events, such as stroke, and the overall resilience of a network tovascular complications. The lack of compact, fast, and scalable simulationswith network perturbations impedes the analysis of the vascular resilience tolife-threatening conditions, surgical interventions and long-term follow-up. Weintroduce a graph-based approach for efficient simulations, which statisticallyestimates biomarkers from a series of perturbations on the patient-specificvascular network. Analog-equivalent circuits are derived from clinicalangiographies. Vascular graphs embed mechanical attributes modelling theimpedance of a tubular structure with stenosis, tortuosity and completeocclusions. We evaluate pressure and flow distributions, simulating healthytopologies and abnormal variants with perturbations in key pathologicalscenarios. These describe the intrinsic network resilience to pathology, anddelineate the underlying cerebrovascular autoregulation mechanisms. Lastly, aputative graph sampling strategy is devised on the same formulation, to supportthe topological inference of uncertain neurovascular graphs.

Towards Quantifying Neurovascular Resilience