M-BIC Lecture; Viktor Jirsa

Abstract:

In the past twenty years, we have made significant progress in creating digital models of an individual’s brain, so called virtual brain twins. By combining brain imaging data with mathematical models, we can predict outcomes more accurately than using each method separately. Our approach has helped us understand normal brain states, their operation and conditions like healthy aging, dementia and epilepsy. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability, and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data and explain features of the brain’s microstate organization. Examples for clinical translation are taken from drug resistant epilepsy and mental disorders. The digital brain twin augments the value of empirical data by completing missing data, allowing clinical hypothesis testing and optimizing treatment strategies for the individual patient. Virtual Brain Twins are part of the European infrastructure called EBRAINS, which supports researchers worldwide in digital neuroscience.