Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation

Cerebral blood flow is essential for brain function and is governed by cerebral perfusion pressure and physiological mechanisms collectively referred to as cerebral vascular regulation (CVR). Direct measurement of cerebral blood flow and individual CVR mechanisms is challenging and often unavailable clinically, limiting personalization of blood flow and CVR targets, particularly in neurological injury. Physics-informed digital twins enable estimation, tracking, and forecasting of unmeasured physiological states from limited data. Therefore, a digital twin of cerebral hemodynamics that includes CVR mechanisms could help overcome measurement barriers. Here, we introduce CereBRLSIM (Cerebral Blood Regulation Latent State Inference and Modeling), a digital twin that assimilates physiological knowledge and patient data to infer CVR function and predict intracranial hemodynamics. Using in vivo experiments and simulated data, CereBRLSIM predicted cerebral blood flow and estimated myogenic, endothelial, and metabolic CVR mechanism dynamics. When personalized to data from six neurocritical care patients, CereBRLSIM differentiated cerebral hemodynamic pressure-flow phenotypes, predicted outcomes, and forecasted blood flow with higher accuracy than machine learning models. This work provides an interpretable and clinically compatible approach for quantifying CVR function and forecasting cerebral blood flow, potentially enabling precision diagnostics and understanding cerebral hemodynamics.