On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread

Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.Author summary: Reliable prediction of the Epileptogenic Zone (EZ) is a challenging task due to nontrivial brain network effects, non-linearity involved in spatiotemporal brain organization, and uncertainty in prior information. Based on the whole-brain modeling approach, the anatomical information of patients can be merged with a generative model of epileptiform discharges to build a personalized large-scale brain model of epilepsy spread. Here, we apply information criteria and cross-validation technique to a whole-brain model of epilepsy spread to infer and validate the spatial map of epileptogenicity across different brain areas. By definition, classical information criteria are independent of prior information, in which the penalty term (number of parameters and observed data) is the same across different EZ candidates, making them infeasible to determine the best among a set of epileptogenicity hypotheses. In contrast, the fully Bayesian information criteria and cross-validation enable us to integrate our prior information to improve out-of-sample prediction accuracy for EZ identification. Using the dynamical system properties of a whole-brain model of epilepsy spread, and dependent on the level of prior information, the proposed approach provides accurate and reliable estimation about the degree of epileptogenicity across different brain areas. Our fully Bayesian approach relying on automatic inference suggests a patient-specific strategy for EZ prediction and hypothesis testing before therapeutic interventions.