Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression

IF 9.6 1区 医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Yong Jiao, Kanhao Zhao, Xinxu Wei, Nancy B. Carlisle, Corey J. Keller, Desmond J. Oathes, Gregory A. Fonzo, Yu Zhang
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引用次数: 0

Abstract

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 R2 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.

Abstract Image

多模态脑网络的深度图学习定义了重度抑郁症的治疗预测特征
重度抑郁症(MDD)是一种严重的健康负担,治疗反应率低。由于重度抑郁症复杂多变的神经病理,预测抗抑郁药物的疗效具有挑战性。确定抗抑郁治疗的生物标志物需要对临床试验数据进行彻底的分析。多模式神经成像与先进的数据驱动方法相结合,可以增强我们对影响治疗结果的神经生物学过程的理解。为了解决这个问题,我们分析了130名接受舍曲林治疗的患者和135名接受安慰剂治疗的患者的静息状态fMRI和EEG连接数据,这些数据来自于临床护理中抗抑郁药物反应的调节因子和生物特征的建立(EMBARC)研究。使用图神经网络开发了一个深度学习框架,以整合数据增强连接和跨模态相关性,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,我们的模型具有良好的预测准确性,舍曲林的R2值为0.24,安慰剂的R2值为0.20。它还显示出仅使用脑电图传递预测的潜力。预测舍曲林反应的关键脑区包括颞下回(fMRI)和后扣带皮层(EEG),而安慰剂反应的关键脑区是楔前叶(fMRI)和辅助运动区(EEG)。此外,两种模式都确定了颞上回和后扣带皮层是舍曲林反应的重要指标,而前扣带皮层和后中枢回是安慰剂组的常见预测指标。此外,额顶叶控制、腹侧注意、背侧注意和边缘网络的变化与重度抑郁症治疗显著相关。通过整合功能磁共振成像和脑电图,我们的研究建立了新的多模态脑网络特征,以预测重度抑郁症患者对舍曲林和安慰剂的个体反应,提供可解释的神经回路模式,可能指导未来的针对性干预。临床治疗抑郁症(EMBARC)的抗抑郁药物反应调节因子和生物特征的建立临床试验注册:ClinicalTrials.gov编号:NCT#01407094。
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来源期刊
Molecular Psychiatry
Molecular Psychiatry 医学-精神病学
CiteScore
20.50
自引率
4.50%
发文量
459
审稿时长
4-8 weeks
期刊介绍: Molecular Psychiatry focuses on publishing research that aims to uncover the biological mechanisms behind psychiatric disorders and their treatment. The journal emphasizes studies that bridge pre-clinical and clinical research, covering cellular, molecular, integrative, clinical, imaging, and psychopharmacology levels.
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