Multi-scale and multi-physics modelling of (un)disturbed cerebral autoregulation.

Cerebral autoregulation (CA) is the body's ability to maintain a constant cerebral blood flow (CBF) under varying cerebral perfusion pressure. Pathologies such as traumatic brain injury can disrupt CA, leading to secondary damage. However, despite its clinical relevance, the mechanisms underlying CA remain incompletely understood, complicating the treatment of disturbed CA. Current treatments focus on restoring physiological CBF without treating the underlying disturbed CA.

In this doctoral research, a computational model of (un)disturbed CA will be developed, considering its multi-scale and multi-physics nature. This endeavor offers clear novelty compared to state-of-the-art models, which mainly rely on phenomenological representations of CA. By adopting a physics-based approach, results with higher physical interpretability can be achieved and the model can be calibrated and validated more easily. The latter entails comparing the computational results to piglet experiments conducted by the co-promoter. The physics-based approach also facilitates the integration of CA disturbances, caused by a traumatic brain injury, into the model, thereby improving disturbed CA understanding and enabling the study of treatments targeted at CA repair, in an in silico environment. This in silico approach reduces the need for animal experiments and provides additional insights by enabling the manipulation of CA parameters to assess their individual impact on CBF.