A semi-automated modelling pipeline to predict the mechanics of multiple sclerosis lesion afflicted brains from magnetic resonance images

Multiple Sclerosis (MS) is a demyelinating and degenerative autoimmune disease that affects the brain and spinal cord. Its causes, mechanisms, and outcomes are yet to be fully understood. One relatively unexplored area is the understanding of changes in brain biomechanics during MS disease progression, despite the likelihood that demyelination significantly alters the overall mechanical structure of the brain. Such changes have the potential to hinder the propagation of nerve signals essential for cognition and motor function. The aim of this work was to create a computational model to explore the mechanics of brains with MS, separating the brain into grey matter, white matter and lesions. Changes were observed when the surface of the brain was subjected to a ramped uniform pressure tangential to the faces of a finite element model, generated from patient- and time-specific MRI scans. The resulting displacements, stresses and strains can all be gauged using the model. The key benefit of this study was to observe the impact of changes in tissue morphology in real brains using non-invasive methods. Ensuring the accuracy of the axiomatic input tissue parameters of the models was critically important, as exploring the range of values from literature, adjusted by their error margins, revealed a significant variability in outcomes, especially in the case of volumetric strain of lesions. The model has the potential to track changes in mechanical tissue properties assuming the availability of a longitudinal dataset, and if further developed, has the potential to serve as the foundation for creating a digital twin. This could enhance medical practice and provide a non-invasive approach to advancing the understanding of MS and its progression on a patient-specific basis.