Individual Multimodal Pathway Statistics for Predicting Treatment Response in Late-life Depression

用于预测晚年抑郁症治疗反应的个体多模式通路统计

基本信息

批准号：
10722921
负责人：
Andrew Robert Gerlach
金额：
$ 17.58万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
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：
2023-08-03 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10722921
关键词：
Affect Age Algorithms Antidepressive Agents Anxiety Disorders Biological Markers Brain Clinical Cognitive Data Data Collection Depressed mood Development Diffusion Magnetic Resonance Imaging Disease remission Doctor of Philosophy Elderly Engineering Functional Magnetic Resonance Imaging Functional disorder Goals Health Image Impairment Individual K-Series Research Career Programs Knowledge Magnetic Resonance Imaging Measurement Measures Mediating Mediator Mental Depression Methods Modality Modeling Montgomery and Asberg depression rating scale Mood Disorders National Institute of Mental Health Nature Neurobiology Nuclear Outcome Participant Pathway interactions Performance Physics Play Positioning Attribute Prediction of Response to Therapy Prognosis Psychiatry Radiology Specialty Reproducibility Research Research Design Research Personnel Resolution Rest Risk Role Scanning Scheme Science Scientist Severities Specificity Structure Testing Training Treatment Protocols Treatment outcome Universities career clinical predictors clinical translation collaborative environment computational neuroscience computer framework depressive symptoms executive function geriatric depression gray matter high dimensionality human subject image processing image registration improved interest late-life anxiety machine learning model multimodal data multimodal neuroimaging multimodality neural circuit neurobiological mechanism overtreatment predictive modeling primary outcome recruit response statistics tool treatment response white matter white matter change

项目摘要

Modest response rates to first-line antidepressant treatment for late-life depression (LLD) expose individuals to prolonged depressive symptoms that worsen their prognosis and associated health risks. Biomarkers of treatment response can alleviate this burden by identifying individuals most likely to benefit from antidepressant treatment. MRI measures of brain structure and function are a promising tool to identify such biomarkers, though the performance required for clinical translation has remained elusive. The goal of this proposal is to integrate complementary network measures from structural and functional MRI with clinical measures to generate biologically relevant features that can improve prediction of treatment outcome in LLD. The anticipated impact of this research will provide improved personalization of LLD treatment (NIMH Strategic Objective 3.2), while elucidating the neural circuitry indicative of treatment outcome (Objective 1.3). To achieve this goal, structural, resting state, and diffusion-weighted MRI will be collected from 75 participants with LLD before commencing an algorithmic antidepressant treatment protocol. The role of resting state functional connectivity as a mediator of the relationship between structural connectivity and clinical measures (baseline depression severity and change in depression severity over treatment) will be investigated within key neural circuitry at the group level. Individual Multimodal Pathway Statistics (IMPathS) will be derived to quantify the personalized importance of functional connectivity to the relationship between structural connectivity and depression severity for prediction of treatment outcome at the individual level. Utility of IMPathS will be assessed by their ability to improve performance beyond unimodal MRI and clinical predictors. Dr. Gerlach has a PhD in nuclear engineering and radiological sciences and is completing a transition from computational physics to computational neuroscience. He will require additional training in 1) the neurobiology, clinical manifestations, and treatment of LLD, 2) diffusion-weighted imaging processing and analysis, 3) advanced statistical training for development and testing of IMPathS, 4) human subjects, study design, and data collection. Completion of the training and research plan in this career development award will enable Dr. Gerlach to progress to an independent investigator focused on investigating the neurobiology of late life anxiety and mood disorders through improved integration of multimodal neuroimaging measures. Dr. Gerlach will execute this training and research with the full support of the Department of Psychiatry at the University of Pittsburgh, which is a highly collaborative environment focused on the development of early career scientists.

对晚期抑郁症 (LLD) 一线抗抑郁治疗的反应率适中，使个人面临长期的抑郁症状会恶化他们的预后和相关的健康风险。生物标志物治疗反应可以通过确定最有可能受益的个人来减轻这种负担抗抑郁治疗。大脑结构和功能的 MRI 测量是识别此类疾病的有前途的工具尽管临床转化所需的性能仍然难以捉摸。此举的目标建议将结构和功能 MRI 的补充网络测量与产生生物学相关特征的临床措施，可以改善治疗的预测 LLD 的结果。这项研究的预期影响将改善 LLD 的个性化治疗（NIMH 战略目标 3.2），同时阐明指示治疗结果的神经回路（目标 1.3）。为了实现这一目标，将收集结构、静息状态和扩散加权 MRI 75 名患有 LLD 的参与者在开始算法抗抑郁治疗方案之前。的作用静息态功能连接作为结构连接和结构连接之间关系的中介临床测量（基线抑郁严重程度和治疗期间抑郁严重程度的变化）将在群体层面的关键神经回路中进行研究。个体多模式路径统计 (IMPathS) 将得出量化功能连接对之间关系的个性化重要性结构连通性和抑郁严重程度用于预测个体水平的治疗结果。公用事业 IMPathS 的评估将通过其提高单模态 MRI 和临床表现的能力来评估预测因子。 Gerlach 博士拥有核工程和放射科学博士学位，并正在完成一项从计算物理学到计算神经科学的转变。他将需要以下方面的额外培训：1) LLD 的神经生物学、临床表现和治疗，2) 弥散加权成像处理和分析，3) 用于 IMPathS 开发和测试的高级统计培训，4) 人类受试者、研究设计、数据收集。完成本职业发展奖的培训和研究计划将使 Gerlach 博士能够晋升为一名独立研究者，专注于研究最近的神经生物学通过改进多模式神经影像测量的整合来治疗生活焦虑和情绪障碍。博士。格拉赫将在精神病学系的全力支持下执行这项培训和研究匹兹堡大学是一个高度协作的环境，专注于早期发展职业科学家。