Establishing Multimodal Brain Biomarkers Using Data-driven Analyticsfor Treatment Selection in Depression

项目摘要 根据世界卫生组织的数据，严重的抑郁症是全球健康状况不佳和残疾的主要原因 组织。尽管在对这种疾病的了解和发展方面取得了重大进展 作为治疗的支柱，抗抑郁药物仅对约50%的患者有效，部分原因是 抑郁症的神经生物学和临床异质性。通过以下方式开发高级数据驱动技术 利用机器学习和来自随机临床试验的大规模多模式神经成像数据 为我们提供了一个独特的机会来探索脑生物标志物，以确定预测治疗的神经生物学 表型。建立这样的生物标记物对于减少多个药物试验的需要和加快 通过加强对治疗靶点的搜索来缓解。然而，对多模式数据的综合分析 识别抑郁症的生物标志物和区分个体对治疗的反应仍然很高 挑战和探索不足。在这项提案中，我们将开发新的数据驱动的分析工具来量化 联合来自治疗前功能磁共振成像(FMRI)的多模式主持人和信号 以及脑电(EEG)数据，用于预测抗抑郁药物的治疗反应。 在目标1中，我们将使用来自建立的主持人的数据来确定治疗效果的多模式主持人 和临床护理抗抑郁剂反应(EMBARC)试验的生物签名。典型关联 基于分析的数据驱动模型旨在提取融合在一起的组合要素 来自功能磁共振成像和脑电模式的补充信息。意向治疗预测线性混合模型 将用于探索抗抑郁药舍曲林与安慰剂治疗反应的多模式调节剂。在……里面 目标2，建立特征融合和预测建模相结合的有监督潜在空间模型 应用它来量化可以预测舍曲林的个体治疗反应的多模式脑信号 而不是安慰剂药物。在目标3中，我们将招募50名抑郁症患者作为独立队列接受 舍曲林治疗，以优化和验证已识别的多模式生物标记物。FMRI和EEG都将是 在基线收集，然后用抗抑郁药物舍曲林治疗(以平行的方式 EMBARC程序)和临床结果评估。我们将发布开发的软件工具和 收集的数据将向研究界公开，以促进多模式神经成像研究 在其他精神障碍方面。

Project Abstract Major depression is the leading cause of ill health and disability worldwide according to the World Health Organization. Although significant progress has been made in understanding the disease and developing treatments, antidepressants, as the treatment mainstay, are effective for only about 50% of patients, in part due to the neurobiological and clinical heterogeneity in depression. Developing advanced data-driven techniques by leveraging machine learning with large-scale multimodal neuroimaging data from randomized clinical trials provides us a unique opportunity to explore brain biomarkers to identify treatment-predictive neurobiological phenotypes. Establishing such biomarkers is crucial for reducing the need for multiple drug trials and expediting remission by sharpening the search for treatment targets. However, integrative analysis of multimodal data for identifying biomarkers and differentiating individual responses to treatment in depression remains highly challenging and underexplored. In this proposal, we will develop new data-driven analytical tools to quantify multimodal moderators and signatures jointly from pre-treatment functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data for the prediction of treatment response to antidepressant medication. In Aim 1, we will identify multimodal moderators of treatment effect using data from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial. A canonical correlation analysis-based data-driven model will be designed to extract combined features that fuse together complementary information from both fMRI and EEG modalities. Intent-to-treat prediction linear mixed models will be used to probe multimodal moderators of antidepressant sertraline versus placebo treatment response. In Aim 2, we will build a supervised latent space model that unifies the feature fusion and predictive modeling and apply it to quantify multimodal brain signatures that can predict individual treatment responses to sertraline versus placebo medication. In Aim 3, we will recruit 50 depressed patients as an independent cohort undergoing sertraline treatment to optimize and validate the identified multimodal biomarkers. Both fMRI and EEG will be collected at baseline followed by treatment with the antidepressant medication sertraline (in a manner paralleling EMBARC procedures) and clinical assessment of outcomes. We will release the developed software tools and collected data to be publicly available to the research community to facilitate multimodal neuroimaging studies in other mental disorders.