Deng Zhi-De, McClinctock Shawn M, Lisanby Sarah H
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:2203-6. doi: 10.1109/EMBC.2015.7318828.
Electroconvulsive therapy (ECT), the most efficacious antidepressant therapy for treatment-resistant depression, has been reported to alter functional brain network architecture by down-regulating connectivity in frontotemporal circuitry. Magnetic seizure therapy (MST), which induces therapeutic seizures with high dose repetitive transcranial magnetic stimulation, has been introduced to improve the seizure therapy risk/benefit ratio. Unfortunately, there is limited understanding of seizure therapy's underlying mechanisms of action. In this study, we apply graph theory-based connectivity analysis to peri-treatment, resting-state, topographical electroencephalography (EEG) in patients with depression receiving seizure therapy. Functional connectivity was assessed using the de-biased weighted phase lag index, a measure of EEG phase synchronization. Brain network structure was quantified using graph theory metrics, including betweenness centrality, clustering coefficient, network density, and characteristic path length. We found a significant reduction in the phase synchronization and aberration of the small-world architecture in the beta frequency band, which could be related to acute clinical and cognitive effects of seizure therapy.
电休克疗法（ECT）是治疗难治性抑郁症最有效的抗抑郁疗法，据报道，它通过下调额颞叶回路的连通性来改变功能性脑网络结构。磁惊厥疗法（MST）通过高剂量重复经颅磁刺激诱发治疗性惊厥，已被引入以改善惊厥治疗的风险/效益比。不幸的是，人们对惊厥治疗的潜在作用机制了解有限。在本研究中，我们将基于图论的连通性分析应用于接受惊厥治疗的抑郁症患者治疗期间、静息状态下的地形脑电图（EEG）。使用去偏加权相位滞后指数评估功能连通性，这是一种脑电图相位同步的测量方法。使用图论指标对脑网络结构进行量化，包括介数中心性、聚类系数、网络密度和特征路径长度。我们发现β频段的相位同步和小世界结构异常显著降低，这可能与惊厥治疗的急性临床和认知效应有关。
