Viktor Jirsa (@ViktorJirsa) is Director of the Institute de Neurosciences des Systèmes and Director of Research at the CNRS. Since the late 90s he has made contributions to the understanding of how network structure constrains the emergence of functional dynamics using methods from nonlinear dynamic system theory and computational neuroscience. Dr. Jirsa has been awarded several international and national awards for his research including the Early Career Distinguished Scholar Award in 2004 and the Francois Erbsmann Prize in 2001. He serves on various Editorial Boards and has published more than 80 scientific articles and book chapters, as well as co-edited several books including the Handbook of Brain Connectivity.
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, derived using mean field techniques for 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 stroke and epilepsy. For epilepsy, we reconstruct personalized connectivity matrices of epileptic patients. 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 brain regions and propagate through large networks, not necessarily epileptogenic. The identification of the EZ is crucial for neurosurgery and requires unambiguous criteria for the degree of epileptogenicity of brain regions. These results provide guidance in the presurgical evaluation of epileptogenicity based on electrographic signatures in intracerebral electroencephalograms validated in small-scale clinical trials. The example of epilepsy nicely underwrites the predictive value of personalized large-scale brain network models.
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