An electroencephalographic signature predicts antidepressant response in major depression
An electroencephalographic signature predicts antidepressant response in major depression
复制标题
脑电图特征可预测重度抑郁症的抗抑郁反应
DOI：
10.1038/s41587-019-0397-3
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发表时间：
2020-02-10
影响因子：
46.9
通讯作者：
Etkin, Amit
中科院分区：
文献类型：
--
作者：
Wu, Wei;Zhang, Yu;Etkin, Amit
抗抑郁药的疗效是通过脑电图特征来预测的。抗抑郁药被广泛使用，但与安慰剂相比，它们的疗效并不明显，部分原因是重度抑郁症的临床诊断包含生物学上的异质性。在这里，我们试图确定与安慰剂相比，抗抑郁药治疗反应的神经生物学特征。我们设计了一种针对静息状态脑电图（EEG）量身定制的潜在空间机器学习算法，并将其应用于最大的成像耦合、安慰剂对照抗抑郁药物研究（n = 309）的数据。对抗抑郁药舍曲林（相对于安慰剂）的症状改善进行了强有力的预测，并在不同的研究地点和脑电图设备上进行了推广。这种舍曲林预测脑电图特征推广到两个抑郁症样本，其中它反映了一般抗抑郁药物的反应性，并与重复经颅磁刺激治疗结果有差异。此外，我们发现舍曲林静息状态脑电图特征与经颅磁刺激和脑电图同时测量的前额叶神经反应性有关。我们的研究结果通过脑电图定制计算模型推进了抗抑郁药物治疗的神经生物学理解，并为抑郁症的个性化治疗提供了临床途径。
The efficacy of an antidepressant is predicted from an EEG signature.Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.