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.

抗抑郁药被广泛使用，但其相对于安慰剂的疗效有限，部分原因是重度抑郁症的临床诊断包含生物学异质性病症。在这里，我们试图确定与安慰剂相比抗抑郁治疗反应的神经生物学特征。我们设计了一种专为静息态脑电图 (EEG) 设计的潜在空间机器学习算法，并将其应用于最大的成像耦合、安慰剂对照抗抑郁研究 ( n  = 309) 的数据。以抗抑郁药舍曲林（与安慰剂相比）特有的方式以及在不同研究地点和脑电图设备中普遍适用的方式，对症状改善进行了强有力的预测。这种舍曲林预测脑电图特征适用于两个抑郁症样本，其中它反映了一般抗抑郁药物反应性，并与重复经颅磁刺激治疗结果存在差异相关。此外，我们发现舍曲林静息态脑电图特征索引了前额神经反应性，这是通过同时经颅磁刺激和脑电图测量的。我们的研究结果通过脑电图定制的计算模型推进了抗抑郁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。