Computational model of cardiac cycle driven brain pulsation: a novel tool for brain disease exploration.

Effective non-invasive detection and tracking of disease progression remains a challenge in medical research. This is particularly critical for brain-related conditions such as Chiari malformations, hydrocephalus or neurodegenerative disorders, as an early detection can significantly improve patient outcomes. One promising approach looks to identify changes in the patterns of brain movement during a cardiac cycle, using aMRI or ultrasound, as a diagnostic proxy for a given condition. During the cardiac cycle, pressure variations in the blood vessels and cerebrospinal fluid (CSF) ventricular system cause motion and deformation of brain tissues. Microstructural changes in the brain due to malformations or disease progression can alter deformation patterns. In this work, we present a computational model to examine brain deformation during the cardiac cycle, with the goal of detecting the progression of underlying diseases from altered brain motion. Our approach uses finite element (FE) simulations to model brain motion in a biologically realistic head model, incorporating deformable brain tissue and CSF, and applying a pressure pulse through the arterial vasculature. We illustrate the use of our model in the context of a Chiari-type I malformation (CM-I), and demonstrate that our model can capture differences in the deformation patterns between healthy and CM-I brains, in agreement with recent aMRI data. While applied here to CM-I, our model can be used as a digital twin for a variety of brain disease evolution whereby changes in microstructure are expected to lead to different brain pulsation patterns.