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:
  • 金额:
    $ 72.08万
  • 依托单位:
  • 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

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

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Yu Zhang其他文献

Shape phase transitions in Nuclei: Effectice order parameters and trajectories
原子核中的形状相变:有效顺序参数和轨迹
  • DOI:
  • 发表时间:
    2011
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yu Zhang;Houi ZhengFang;Liu YuXin
  • 通讯作者:
    Liu YuXin
The integrated scheduling problem in container terminal with dual-cycle operation
双周期作业集装箱码头综合调度问题

Yu Zhang的其他文献

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{{ truncateString('Yu Zhang', 18)}}的其他基金

Assess Neural Circuits and Subtypes Underlying Dimensions of Neuropsychiatric Symptoms in Alzheimer's Disease
评估阿尔茨海默病神经精神症状的神经回路和亚型
  • 批准号:
    10741906
  • 财政年份:
    2023
  • 资助金额:
    $ 72.08万
  • 项目类别:
Identifying Transdiagnostic Functional Connectivity Biomarkers for Cognitive Health and Psychopathology
识别认知健康和精神病理学的跨诊断功能连接生物标志物
  • 批准号:
    10667086
  • 财政年份:
    2023
  • 资助金额:
    $ 72.08万
  • 项目类别:
Toward novel translucent and strong nanostructured dental zirconia
开发新型半透明且坚固的纳米结构牙科氧化锆
  • 批准号:
    10273470
  • 财政年份:
    2020
  • 资助金额:
    $ 72.08万
  • 项目类别:
Chromatin looping directed RAG targeting during V(D)J recombination
V(D)J 重组过程中染色质环化引导 RAG 靶向
  • 批准号:
    10524028
  • 财政年份:
    2020
  • 资助金额:
    $ 72.08万
  • 项目类别:
Chromatin looping directed RAG targeting during V(D)J recombination
V(D)J 重组过程中染色质环化引导 RAG 靶向
  • 批准号:
    10597767
  • 财政年份:
    2020
  • 资助金额:
    $ 72.08万
  • 项目类别:
A 2D segmentation method for jointly characterizing epigenetic dynamics in multiple cell lines
联合表征多个细胞系表观遗传动态的二维分割方法
  • 批准号:
    9382058
  • 财政年份:
    2017
  • 资助金额:
    $ 72.08万
  • 项目类别:
Toward novel translucent and strong nanostructured dental zirconia
开发新型半透明且坚固的纳米结构牙科氧化锆
  • 批准号:
    9904609
  • 财政年份:
    2017
  • 资助金额:
    $ 72.08万
  • 项目类别:
Graded Zirconia Structures for Resistance to Chipping, Delamination, and Fatigue
分级氧化锆结构可抵抗碎裂、分层和疲劳
  • 批准号:
    8595174
  • 财政年份:
    2012
  • 资助金额:
    $ 72.08万
  • 项目类别:
Graded Zirconia Structures for Resistance to Chipping, Delamination, and Fatigue
分级氧化锆结构可抵抗碎裂、分层和疲劳
  • 批准号:
    8788784
  • 财政年份:
    2012
  • 资助金额:
    $ 72.08万
  • 项目类别:
Graded Zirconia Structures for Resistance to Chipping, Delamination, and Fatigue
分级氧化锆结构可抵抗碎裂、分层和疲劳
  • 批准号:
    8238242
  • 财政年份:
    2012
  • 资助金额:
    $ 72.08万
  • 项目类别:
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