On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
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DOI：
10.1371/journal.pcbi.1009129
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发表时间：
2021-07
影响因子：
4.3
通讯作者：
Jirsa VK
中科院分区：
文献类型：
--
作者：
Hashemi M;Vattikonda AN;Sip V;Diaz-Pier S;Peyser A;Wang H;Guye M;Bartolomei F;Woodman MM;Jirsa VK
个体解剖信息已被用作全脑网络模型贝叶斯推理范式的先验知识。然而，对于这些个性化信息的实际敏感度，在前面仍然是未知的。在这项研究中，我们介绍了使用完全贝叶斯信息标准和对受试者特定信息的留一交叉验证技术，以评估关于病理脑区域位置的不同致痫性假设，这些假设基于来自动力系统特性的先验知识。贝叶斯虚拟癫痫患者（BVEP）模型是一种基于个体结构数据融合、癫痫样放电生成模型和自调谐蒙特卡罗采样算法的模型，用于推断不同脑区的癫痫致发性空间图。我们的研究结果表明，测量具有信息先验的BVEP模型的样本外预测精度可以可靠和有效地评估关于不同大脑区域致痫程度的潜在假设。相反，当使用非信息性先验时，信息标准无法提供关于脑区域致痫性的有力证据。我们还表明，完全贝叶斯标准正确地评估了不同个体的全脑模型的结构和功能成分的不同假设。本研究中使用的完全基于贝叶斯信息理论的方法建议在癫痫的生成脑网络模型中进行癫痫性假设检验的患者特异性策略，以改善手术结果。由于大脑网络的影响、大脑时空组织的非线性和先验信息的不确定性，对致痫区（EZ）的可靠预测是一项具有挑战性的任务。基于全脑建模方法，将患者解剖信息与癫痫样放电生成模型相融合，构建个性化的癫痫扩散大尺度脑模型。在这里，我们将信息标准和交叉验证技术应用于癫痫扩散的全脑模型，以推断和验证不同脑区的致痫性空间图。根据定义，经典信息标准独立于先验信息，其中惩罚项（参数数量和观测数据数量）在不同的EZ候选中是相同的，这使得它们无法在一组致痫性假设中确定最佳。相比之下，完全贝叶斯信息标准和交叉验证使我们能够整合先验信息，以提高EZ识别的样本外预测精度。利用癫痫全脑传播模型的动力系统特性，并依赖于先验信息水平，该方法提供了关于不同脑区致痫程度的准确可靠的估计。我们的完全贝叶斯方法依赖于自动推理，建议在治疗干预之前进行EZ预测和假设检验的患者特异性策略。
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. 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.