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

使用数据驱动分析建立多模式脑生物标志物以选择抑郁症的治疗方法

基本信息

批准号：
10660219
负责人：
Yu Zhang
金额：
$ 72.08万
依托单位：
LEHIGH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2028-02-29
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10660219
关键词：
Adopted Antidepressive Agents Attention Biological Markers Brain Brain region Caring Clinic Clinical Trials Communities Conflict (Psychology)Coupled Data Data Set Derivation procedure Development Disease Disease remission Electroencephalography Emotional Exhibits Functional Magnetic Resonance Imaging Health Heterogeneity Individual Machine Learning Major Depressive Disorder Medicine Mental Depression Mental disorders Methods Modality Modeling National Institute of Mental Health Neurobiology Outcome Patients Pharmaceutical Preparations Phenotype Placebo Effect Placebos Prediction of Response to Therapy Prevalence Procedures Protocols documentation Psychiatry Publishing Regulation Reproducibility Research Research Personnel Rest Sampling Selection for Treatments Selective Serotonin Reuptake Inhibitor Sertraline Software Tools Space Models Symptoms Techniques Testing Treatment outcome Validation World Health Organization analytical tool biomarker identification biomarker validation biosignature clinical care clinical diagnosis clinical effect clinical heterogeneity clinical outcome assessment clinical practice clinical predictors clinically relevant cohort data archive data-driven model depressed patient design disability effective intervention functional magnetic resonance imaging/electroencephalography individual response machine learning model multimodal data multimodal neuroimaging multimodality neural circuit neurobiological mechanism neuroimaging novel outcome prediction personalized medicine predictive marker predictive modeling predictive signature randomized placebo-controlled clinical trial randomized, clinical trials recruit response software development tool translational impact treatment effect treatment response

项目摘要

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.

项目摘要