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

Meysam Hashemi,Viktor K Jirsa,Huifang Wang,Sandra Diaz-Pier,Marmaduke M Woodman,Alexander Peyser,Fabrice Bartolomei,Maxime Guye,Anirudh N Vattikonda,Viktor Sip

doi:10.1371/journal.pcbi.1009129

Abstract

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.

Highlights

Mathematical models have long played a key role in understanding the biological systems
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
The functional brain network organization is integrated with the spatial map of epileptogenicity across different brain regions to build the generative Virtual Epileptic Patient (VEP) brain model

Summary

Introduction

Mathematical models have long played a key role in understanding the biological systems. Bayesian framework offers a powerful approach to deal with model uncertainty in parameter estimation, and a principled method for model prediction with a broad range of applications [5,6,7,8,9,10,11,12,13]. Bayesian inference techniques provide the posterior distribution of unknown parameters of the underlying data generating process given only observed responses and prior information about the underlying generative process [14, 15]. The posterior values define distributions over plausible parameter ranges consistent with observed data by updating information from both the prior and the observations. The estimated posterior is dependent on the level of information in the prior distribution, as well as the conditional probability of the observed data given the model parameters in the form of likelihood function. MCMC-based sampling of posterior distributions, which converge to the desired target distribution for large enough sample size, is a challenging task due to the large curvature in the typical set [17, 18] or non-linear correlations between parameters in high-dimensional parameter spaces [19,20,21]

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