Patient-Specific Large-Scale Brain Networks in Diseases*

Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in brain disorders. Customization of healthcare with medical decisions tailored to the individual patient is a key aspect of personalized medicine.
When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. In partial epilepsy seizures originate in a local network, the so-called epileptogenic zone, before recruiting other brain regions. We build a Virtual Epileptic Patient (VEP) brain model that integrates patient-specific information, and we demonstrate that VEP derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome [1]. Individual brain models for 15 patients are validated against the presurgical electroencephalography data and the standard clinical evaluation, and are used to show that VEP brain models account for the patient seizure propagation patterns and explain the variability in postsurgical success [2].
Finally, we demonstrate how to develop novel personalized strategies towards therapy and intervention, by the example of applying linear stability analysis to the VEP in order to identify the minimal number of links that need to be removed for stopping the seizure propagation [3]. This suggests a less invasive paradigm of surgical interventions to treat and manage partial epilepsy.

References
[1] VK Jirsa et al. NeuroImage 145:377 (2017)
[2] T Proix et al. Brain 140: 641 (2017)
[3] S Olmi et al., Plos CB, [in press] (2019)