Burns Samuel P, Sritharan Duluxan, Jouny Christophe, Bergey Gregory, Crone Nathan, Anderson William S, Sarma Sridevi V
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4684-7. doi: 10.1109/EMBC.2012.6347012.
Seizures are events that spread through the brain's network of connections and create pathological activity. To understand what is occurring in the brain during seizure we investigated the time progression of the brain's state from seizure onset to seizure suppression. Knowledge of a seizure's dynamics and the associated spatial structure is important for localizing the seizure foci and determining the optimal location and timing of electrical stimulation to mitigate seizure development. In this study, we analyzed intracranial EEG data recorded in 2 human patients with drug-resistant epilepsy prior to undergoing resection surgery using network analyses. Specifically, we computed a time sequence of connectivity matrices from iEEG (intracranial electroencephalography) recordings that represent network structure over time. For each patient, connectivity between electrodes was measured using the coherence in the band of frequencies with the strongest modulation during seizure. The connectivity matrices' structure was analyzed using an eigen-decomposition. The leading eigenvector was used to estimate each electrode's time dependent centrality (importance to the network's connectivity). The electrode centralities were clustered over the course of each seizure and the cluster centroids were compared across seizures. We found, for each patient, there was a consistent set of centroids that occurred during each seizure. Further, the brain reliably evolved through the same progression of states across multiple seizures including characteristic onset and suppression states.
癫痫发作是通过大脑的连接网络传播并产生病理活动的事件。为了了解癫痫发作期间大脑中发生了什么，我们研究了从癫痫发作开始到发作抑制期间大脑状态的时间进程。了解癫痫发作的动态变化及其相关的空间结构对于确定癫痫病灶的位置以及确定减轻癫痫发作发展的电刺激的最佳位置和时机非常重要。在本研究中，我们使用网络分析方法分析了2例耐药性癫痫患者在接受切除手术前记录的颅内脑电图数据。具体而言，我们从颅内脑电图（iEEG）记录中计算出一系列表示网络结构随时间变化的连通性矩阵。对于每位患者，使用癫痫发作期间调制最强的频段内的相干性来测量电极之间的连通性。使用特征分解分析连通性矩阵的结构。主导特征向量用于估计每个电极随时间变化的中心性（对网络连通性的重要性）。在每次癫痫发作过程中对电极中心性进行聚类，并比较不同癫痫发作中的聚类中心。我们发现，对于每位患者，每次癫痫发作期间都有一组一致的聚类中心。此外，大脑在多次癫痫发作中可靠地经历相同的状态进展，包括特征性的发作起始和抑制状态。
