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
- 来源:
- 关键词:AdoptedAntidepressive AgentsAttentionBiological MarkersBrainBrain regionCaringClinicClinical TrialsCommunitiesConflict (Psychology)CoupledDataData SetDerivation procedureDevelopmentDiseaseDisease remissionElectroencephalographyEmotionalExhibitsFunctional Magnetic Resonance ImagingHealthHeterogeneityIndividualMachine LearningMajor Depressive DisorderMedicineMental DepressionMental disordersMethodsModalityModelingNational Institute of Mental HealthNeurobiologyOutcomePatientsPharmaceutical PreparationsPhenotypePlacebo EffectPlacebosPrediction of Response to TherapyPrevalenceProceduresProtocols documentationPsychiatryPublishingRegulationReproducibilityResearchResearch PersonnelRestSamplingSelection for TreatmentsSelective Serotonin Reuptake InhibitorSertralineSoftware ToolsSpace ModelsSymptomsTechniquesTestingTreatment outcomeValidationWorld Health Organizationanalytical toolbiomarker identificationbiomarker validationbiosignatureclinical careclinical diagnosisclinical effectclinical heterogeneityclinical outcome assessmentclinical practiceclinical predictorsclinically relevantcohortdata archivedata-driven modeldepressed patientdesigndisabilityeffective interventionfunctional magnetic resonance imaging/electroencephalographyindividual responsemachine learning modelmultimodal datamultimodal neuroimagingmultimodalityneural circuitneurobiological mechanismneuroimagingnoveloutcome predictionpersonalized medicinepredictive markerpredictive modelingpredictive signaturerandomized placebo-controlled clinical trialrandomized, clinical trialsrecruitresponsesoftware developmenttooltranslational impacttreatment effecttreatment 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
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
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
Yu Zhang;Zhijun Rong - 通讯作者:
Zhijun Rong
Yu Zhang的其他文献
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{{ truncateString('Yu Zhang', 18)}}的其他基金
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- 资助金额:
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- 资助金额:
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- 资助金额:
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A 2D segmentation method for jointly characterizing epigenetic dynamics in multiple cell lines
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- 资助金额:
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Graded Zirconia Structures for Resistance to Chipping, Delamination, and Fatigue
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- 批准号:
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- 资助金额:
$ 72.08万 - 项目类别:
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分级氧化锆结构可抵抗碎裂、分层和疲劳
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- 资助金额:
$ 72.08万 - 项目类别: