Abstract

The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use intracranial electroencephalography (iEEG) to record the patient’s intracranial brain activity in many small regions. iEEG data are high-dimensional multivariate time series. We build a state-space multivariate autoregression (SSMAR) for iEEG data to model the underlying directional brain network. To produce scientifically interpretable network results, we incorporate into the SSMAR the scientific knowledge that the underlying brain network tends to have a cluster structure. Specifically, we assign to the SSMAR parameters a stochastic-blockmodel-motivated prior, which reflects the cluster structure. We develop a Bayesian framework to estimate the SSMAR, infer directional connections, and identify clusters for the unobserved network edges. The new method is robust to violations of model assumptions and outperforms existing network methods. By applying the new method to an epileptic patient’s iEEG data, we reveal seizure initiation and propagation in the patient’s directional brain network and discover a unique directional connectivity property of the SOZ. Overall, the network results obtained in this study bring new insights into epileptic patients’ normal and abnormal epileptic brain mechanisms and have the potential to assist neurologists and clinicians in localizing the SOZ—a long-standing research focus in epilepsy diagnosis and treatment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

摘要

人脑是涉及定向连通性的大脑区域的定向网络系统。癫痫发作是一种定向网络现象，因为异常的神经元活动从癫痫发作区 (SOZ) 开始并传播到其他健康区域。为了定位癫痫患者的 SOZ，临床医生使用颅内脑电图 (iEEG) 来记录患者颅内许多小区域的大脑活动。iEEG 数据是高维多元时间序列。我们为 iEEG 数据构建了一个状态空间多元自回归 (SSMAR)，以对潜在的定向大脑网络进行建模。为了产生科学可解释的网络结果，我们将底层大脑网络倾向于具有集群结构的科学知识纳入 SSMAR。具体来说，我们为 SSMAR 参数分配了一个随机块模型驱动的先验，它反映了集群结构。我们开发了一个贝叶斯框架来估计 SSMAR，推断方向连接，并为未观察到的网络边缘识别集群。新方法对违反模型假设具有鲁棒性，并且优于现有的网络方法。通过将新方法应用于癫痫患者的 iEEG 数据，我们揭示了患者定向脑网络中癫痫发作的启动和传播，并发现了 SOZ 独特的定向连接特性。全面的，本研究中获得的网络结果为癫痫患者的正常和异常癫痫脑机制带来了新的见解，并有可能帮助神经科医生和临床医生定位 SOZ——这是癫痫诊断和治疗的长期研究重点。本文的补充材料，包括对可用于复制作品的材料的标准化描述，可作为在线补充材料获得。