Tong Xiaoyu, Xie Hua, Wu Wei, Keller Corey, Fonzo Gregory, Chidharom Matthieu, Carlisle Nancy, Etkin Amit, Zhang Yu
Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA.
medRxiv. 2023 May 28:2023.05.24.23290434. doi: 10.1101/2023.05.24.23290434.
Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.
抗抑郁药物对重度抑郁症（MDD）患者的治疗效果并不理想，相较于安慰剂仅有适度优势。这种适度疗效部分归因于抗抑郁反应机制难以捉摸以及患者对治疗反应存在无法解释的异质性——获批的抗抑郁药物仅使一部分患者受益，因此需要基于个体治疗反应预测的个性化精神病学。规范建模是一种量化心理病理学维度个体偏差的框架，为精神疾病的个性化治疗提供了一条有前景的途径。在本研究中，我们利用来自三个独立队列健康对照的静息态脑电图（EEG）连接性数据构建了一个规范模型。我们刻画了MDD患者相对于健康规范的个体偏差，并据此训练了MDD患者治疗反应的稀疏预测模型。我们成功预测了接受舍曲林治疗患者（r = 0.43，p < 0.001）和接受安慰剂治疗患者（r = 0.33，p < 0.001）的治疗结果。我们还表明，规范建模框架成功区分了受试者之间的亚临床和诊断变异性。从预测模型中，我们识别出静息态EEG中抗抑郁治疗的关键连接特征，表明治疗反应之间神经回路参与存在差异。我们的研究结果和具有高度可推广性的框架推进了对抗抑郁反应潜在途径的神经生物学理解，使MDD治疗更具针对性和有效性。
