The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

众所周知，精神疾病在神经生物学和临床上具有异质性，因此对精神疾病的理解和治疗可以受益于数据驱动的疾病亚型识别。在这里，我们报告了基于强大且独特的功能连接模式（主要在额顶控制网络和默认模式网络内）对创伤后应激障碍（PTSD）和重度抑郁症（MDD）两种临床相关亚型的识别。我们通过无监督和监督机器学习，分析了 PTSD 和 MDD 患者的四个数据集中的高密度静息态脑电图重建信号的基于功率包络的连接性，从而确定了疾病亚型，并表明这些亚型是可转移的跨越在不同条件下记录的独立数据集。功能连接与健康对照组差异最大的亚型对 PTSD 的心理治疗反应较差，并且对 MDD 的抗抑郁药物没有反应。相比之下，两种亚型对两种不同形式的重度抑郁症重复经颅磁刺激疗法的反应同样良好。我们的数据驱动方法可能构成基于连接组的诊断的通用解决方案。