OHBM 2022 | 84 | Educational Course | Generative Modelling of Brain Dynamics | Part 5
Title: Responsible full brain modeling with clinical translation.
Session: Educational Course
Speaker: Viktor Jirsa
Over the past decade we have demonstrated that the fusion of subject-specific structural information of the human brain with mathematical dynamic models allows building biologically realistic brain network models, which have a predictive value, beyond the explanatory power of each approach independently. The network nodes hold neural population models, which are derived using mean field techniques from statistical physics expressing ensemble activity via collective variables. Our hybrid approach fuses data-driven with forward-modeling-based techniques and has been successfully applied to explain healthy brain function and clinical translation including aging, stroke and epilepsy. Here we illustrate the workflow along the example of epilepsy: we reconstruct personalized connectivity matrices of human epileptic patients using Diffusion Tensor weighted Imaging (DTI). Subsets of brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other healthy brain regions and propagate activity through large brain networks. The identification of the EZ is crucial for the success of neurosurgery and presents one of the historically difficult questions in clinical neuroscience. However, estimations of model parameters are a huge challenge due to neurodegeneracy and identifiability issues and have been referred to as the key obstacle to progress in neuroscience (Frégnac, 2017). A responsible treatment of these issues demands the use of proper estimates of confidence and diagnostics of performance of the inference, which is provided by latest techniques in Bayesian inference, in particular Hamiltonian Monte Carlo techniques. The example of epilepsy nicely underwrites the predictive value of personalized large-scale brain network models. The workflow of end-to-end modeling is an integral part of the European neuroinformatics platform EBRAINS and enables neuroscientists worldwide to build and estimate personalized virtual brains.
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