Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON M6J 1A8, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building, Room 407, 164 College St, Toronto, ON M5S 3G9, Canada.
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building, Room 407, 164 College St, Toronto, ON M5S 3G9, Canada; The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON M5S 3G4, Canada.
Neuroimage Clin. 2018;20:1176-1190. doi: 10.1016/j.nicl.2018.10.015. Epub 2018 Oct 17.
Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive impairment. Given the neuroimaging evidence for disrupted resting-state network dynamics in depression, we investigated whether seizure therapy (ECT and MST) selectively modifies brain network dynamics for therapeutic efficacy.
EEG microstate analysis was used to evaluate resting-state network dynamics in patients at baseline and following seizure therapy, and in healthy controls. Microstate analysis defined four classes of brain states (labelled A, B, C, D). Source localization identified the brain regions associated with these states.
An increase in duration and decrease in frequency of microstates was specific to responders of seizure therapy. Significant changes in the dynamics of States A, C and D were observed and predicted seizure therapy outcome (specifically ECT). Relative change in the duration of States C and D was shown to be a strong predictor of ECT response. Source localization partly associated C and D to the salience and frontoparietal networks, argued to be impaired in depression. An increase in duration and decrease in frequency of microstates was also observed following MST, however it was not specific to responders.
This study presents the first evidence for the modulation of global brain network dynamics by seizure therapy. Successful seizure therapy was shown to selectively modulate network dynamics for therapeutic efficacy.
电抽搐疗法(ECT)对治疗抵抗性抑郁症非常有效,但其作用机制仍不清楚。了解 ECT 的作用机制可以推进优化磁惊厥治疗(MST),以提高疗效和减少认知障碍。鉴于抑郁症静息态网络动力学紊乱的神经影像学证据,我们研究了惊厥治疗(ECT 和 MST)是否选择性地改变了大脑网络动力学以达到治疗效果。
使用脑电图微状态分析来评估基线时、惊厥治疗后和健康对照组的静息态网络动力学。微状态分析定义了四种脑状态(标记为 A、B、C、D)。源定位确定了与这些状态相关的大脑区域。
惊厥治疗的反应者的微状态持续时间增加和频率降低是特异性的。观察到状态 A、C 和 D 的动力学发生了显著变化,并预测了惊厥治疗的结果(特别是 ECT)。状态 C 和 D 的持续时间的相对变化被证明是 ECT 反应的一个强有力的预测指标。源定位部分将 C 和 D 与突显和额顶叶网络相关联,这些网络被认为在抑郁症中受损。MST 后也观察到微状态持续时间增加和频率降低,但它不是反应者特异性的。
本研究首次提供了惊厥治疗对全局大脑网络动力学调节的证据。成功的惊厥治疗显示出对治疗效果的网络动力学的选择性调节。