Jiao Yong, Zhao Kanhao, Wei Xinxu, Carlisle Nancy B, Keller Corey J, Oathes Desmond J, Fonzo Gregory A, Zhang Yu
Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
Mol Psychiatry. 2025 Mar 31. doi: 10.1038/s41380-025-02974-6.
Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R value of 0.24 for sertraline and 0.20 for placebo. It also exhibited potential in transferring predictions using only EEG. Key brain regions identified for predicting sertraline response included the inferior temporal gyrus (fMRI) and posterior cingulate cortex (EEG), while for placebo response, the precuneus (fMRI) and supplementary motor area (EEG) were critical. Additionally, both modalities identified the superior temporal gyrus and posterior cingulate cortex as significant for sertraline response, while the anterior cingulate cortex and postcentral gyrus were common predictors in the placebo arm. Additionally, variations in the frontoparietal control, ventral attention, dorsal attention, and limbic networks were notably associated with MDD treatment. By integrating fMRI and EEG, our study established novel multimodal brain network signatures to predict individual responses to sertraline and placebo in MDD, providing interpretable neural circuit patterns that may guide future targeted interventions. Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) ClinicalTrials.gov Identifier: NCT#01407094.
重度抑郁症(MDD)带来了沉重的健康负担,且治疗有效率较低。由于MDD复杂多样的神经病理学特征,预测抗抑郁药的疗效具有挑战性。确定抗抑郁治疗的生物标志物需要对临床试验数据进行全面分析。多模态神经成像与先进的数据驱动方法相结合,可以增进我们对影响治疗结果的神经生物学过程的理解。为了解决这一问题,我们分析了来自临床护理中抗抑郁反应的调节因素和生物标志物研究(EMBARC)的130例接受舍曲林治疗的患者以及135例接受安慰剂治疗的患者的静息态功能磁共振成像(fMRI)和脑电图(EEG)连接性数据。利用图神经网络开发了一个深度学习框架,以整合数据增强的连接性和跨模态相关性,旨在通过揭示多模态脑网络特征来预测个体症状变化。结果表明,我们的模型显示出有前景的预测准确性,舍曲林组的R值为0.24,安慰剂组为0.20。该模型还展现出仅使用EEG进行预测转移的潜力。确定的预测舍曲林反应的关键脑区包括颞下回(fMRI)和后扣带回皮质(EEG),而对于安慰剂反应,楔前叶(fMRI)和辅助运动区(EEG)至关重要。此外,两种模态均确定颞上回和后扣带回皮质对舍曲林反应具有显著性,而前扣带回皮质和中央后回是安慰剂组的常见预测指标。此外,额顶叶控制网络、腹侧注意网络、背侧注意网络和边缘网络的变化与MDD治疗显著相关。通过整合fMRI和EEG,我们的研究建立了新的多模态脑网络特征,以预测MDD患者对舍曲林和安慰剂的个体反应,提供了可解释的神经回路模式,可能为未来的靶向干预提供指导。试验注册:抑郁症临床护理中抗抑郁反应的调节因素和生物标志物研究(EMBARC),ClinicalTrials.gov标识符:NCT#01407094。