Mapping epileptic directional brain networks using intracranial EEG data
Mapping epileptic directional brain networks using intracranial EEG data
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DOI：
10.1093/biostatistics/kxz056
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发表时间：
2021-07-01
期刊：
影响因子：
2.1
通讯作者：
Zhang，Tingting
中科院分区：
文献类型：
--
作者：
Li，Huazhang;Wang，Yaotian;Zhang，Tingting
人脑是一个定向网络系统，其中大脑区域是网络节点，一个区域对另一个区域施加的影响是网络边缘。我们将这种从一个地区到另一个地区的定向信息流称为定向连接。癫痫发作是由癫痫定向网络引起的；异常神经元活动从癫痫发作起始区开始，并通过网络传播到其他健康的大脑区域。因此，有效的癫痫诊断和治疗需要准确识别区域之间的方向性联系，即绘制癫痫患者的大脑网络图。本文旨在通过对癫痫患者多个脑区脑电活动的记录来了解癫痫患者的脑网络。最流行的定向连接模型使用常微分方程组(ODE)。然而，ODE模型对数据噪声很敏感，而且计算代价很高。为了解决这些问题，我们提出了一种高维状态空间多元自回归(SSMAR)模型，用于描述大脑的方向连接。与标准的多元自回归和SSMAR模型不同，SSMAR模型具有簇结构，其中大脑网络由几个密集连接的脑区簇组成。我们开发了一种期望最大化算法来估计所提出的模型，并使用它来绘制癫痫患者在不同发作阶段的区域间网络。我们的方法揭示了癫痫发展过程中大脑网络的演变。
The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another asdirectional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. As such, effective epilepsy diagnosis and treatment require accurate identification of directional connections among regions, i.e., mapping of epileptic patients’ brain networks. This article aims to understand the epileptic brain network using intracranial electroencephalographic data—recordings of epileptic patients’ brain activities in many regions. The most popular models for directional connectivity use ordinary differential equations (ODE). However, ODE models are sensitive to data noise and computationally costly. To address these issues, we propose a high-dimensional state-space multivariate autoregression (SSMAR) model for the brain’s directional connectivity. Different from standard multivariate autoregression and SSMAR models, the proposed SSMAR features a cluster structure, where the brain network consists of several clusters of densely connected brain regions. We develop an expectation–maximization algorithm to estimate the proposed model and use it to map the interregional networks of epileptic patients in different seizure stages. Our method reveals the evolution of brain networks during seizure development.