Burns Samuel P, Santaniello Sabato, Yaffe Robert B, Jouny Christophe C, Crone Nathan E, Bergey Gregory K, Anderson William S, Sarma Sridevi V
Institute for Computational Medicine and.
Institute for Computational Medicine and
Proc Natl Acad Sci U S A. 2014 Dec 9;111(49):E5321-30. doi: 10.1073/pnas.1401752111. Epub 2014 Nov 17.
The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.
人类大脑是一个动态的网络系统。部分癫痫发作的患者在发作期间有局灶性区域，这些区域会周期性地偏离正常脑网络动态。我们使用图论技术，对12例颞叶、枕叶或额叶部分性发作患者的连续皮层脑电图（ECoG）记录（每位患者5.4±1.7天，平均值±标准差）进行分析，研究发作前、发作期间和发作后的脑连接演变。每个电极被视为图中的一个节点，节点对之间的边根据其在一个频段内的相干性加权。捕捉网络结构的连接矩阵的主特征向量随时间进行跟踪并聚类，以揭示有限的一组脑网络状态。在所有患者中，我们发现：（i）网络连接具有结构性，并定义了有限的一组脑状态；（ii）发作具有一致的状态序列特征；（iii）一部分节点在发作开始时与网络隔离，并在发作接近结束时与网络的连接性增加；（iv）这些孤立节点可能以高特异性和敏感性识别发作起始区。为了定位发作，临床医生通过目视检查从多个颅内电极触点记录的发作情况，这是一个耗时的过程，而且并不总是能确定定位。我们表明，从所有ECoG通道计算出的网络指标捕捉到了发作起始区与正常整体网络结构不同时的动态变化。这表明状态空间模型可用于帮助在ECoG记录中定位发作起始区。
