Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
Neuroimage Clin. 2020;26:102080. doi: 10.1016/j.nicl.2019.102080. Epub 2019 Nov 6.
Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies.
电抽搐治疗 (ECT) 起效迅速，已被广泛用于治疗抑郁症 (DEP)。然而，确定预测 ECT 反应的生物标志物仍然是个性化治疗和理解治疗机制的首要任务。本研究使用基于连接组学的预测模型 (CPM) 方法对 122 名 DEP 患者进行分析，以确定 ECT 前全脑功能连接 (FC) 是否可以预测抑郁评分的变化和 ECT 后的缓解状态（122 名患者中的 47 名，占样本的 38.5%），以及 ECT 前和纵向（ECT 前后）的区域脑网络生物标志物是否与与治疗相关的抑郁评分变化相关。结果表明，预测 ECT 反应的最佳网络是负（反相关）FC 网络，这些网络可以预测 ECT 后抑郁严重程度（连续测量），其缓解预测的准确率为 76.23%。具有最大预测能力的 FC 网络主要集中在前额和颞叶皮质和皮质下核团，包括额下回 (IFG)、额上回 (SFG)、颞上回 (STG)、颞中回 (ITG)、基底节 (BG) 和丘脑 (Tha)。这些大脑区域中的几个也被确定为 FC 网络中的节点，这些网络在 ECT 前后显示出显著变化，但这些网络与治疗反应无关。本研究设计在纵向设计和缺乏对照组方面存在局限性，这限制了对治疗后状态机制的因果推理。尽管预测生物标志物仍低于潜在转化的推荐阈值，但分析方法和结果证明了生物标志物在推进个性化治疗策略方面的前景和通用性。
