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，严重抑郁症是全球健康状况不佳和残疾的主要原因。 Organization.尽管在了解这种疾病和发展 治疗方面，抗抑郁药作为主要治疗药物，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。开发先进的数据驱动技术， 将机器学习与来自随机临床试验的大规模多模态神经成像数据相结合 为我们提供了一个独特的机会，探索大脑生物标志物，以确定治疗预测神经生物学 表型建立这样的生物标志物对于减少多个药物试验的需要和加快 通过加强寻找治疗目标来缓解。然而，多模态数据的综合分析， 识别生物标志物和区分个体对抑郁症治疗的反应仍然高度 具有挑战性且探索不足。在本提案中，我们将开发新的数据驱动分析工具， 多模态主持人和签名联合从治疗前的功能磁共振成像（fMRI） 和脑电图（EEG）数据用于预测抗抑郁药物的治疗反应。 在目标1中，我们将使用来自确立调节者的数据来确定治疗效果的多模式调节者 和临床护理抗结核反应的生物特征（EMBARC）试验。典型相关 基于分析的数据驱动模型将被设计为提取融合在一起的组合特征 功能磁共振成像和脑电图的互补信息。意向治疗预测线性混合模型 将用于探测抗抑郁药舍曲林与安慰剂治疗反应的多模式调节剂。在 目标二，建立一个有监督的特征融合和预测建模相结合的潜在空间模型， 将其应用于量化多模式大脑特征，可以预测舍曲林的个体治疗反应 与安慰剂相比。在目标3中，我们将招募50名抑郁症患者作为一个独立的队列， 舍曲林治疗以优化和验证所鉴定的多模式生物标志物。功能磁共振成像和脑电图都将 在基线时收集，然后用抗抑郁药物舍曲林治疗（以与 EMBARC手术）和临床结局评估。我们将发布开发的软件工具， 收集的数据将公开提供给研究界，以促进多模式神经成像研究 其他精神疾病。

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.