Statistical and Machine Learning Methods for Integrating Clinical and Multimodal Imaging Data to Select Optimal Antidepressant Treatment

整合临床和多模态成像数据以选择最佳抗抑郁治疗的统计和机器学习方法

• 批准号: 10241351
• 负责人: Adam Ciarleglio
• 金额: $ 16.1万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241351
• 关键词: Address; Algorithms; Antidepressive Agents; Area; Automobile Driving; Award; Biological Markers; Brain; Characteristics; Clinical; Clinical Data; Clinical Research; Clinical Trials; Complex; Computer software; Data; Data Analyses; Data Set; Development; Disease; Disease remission; Funding; Goals; Guidelines; Heterogeneity; Image; Instruction; Investigation; Knowledge; Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Measures; Mental Depression; Mental Health; Mental disorders; Mentored Research Scientist Development Award; Mentors; Methodology; Methods; Modality; Modeling; Modernization; Multimodal Imaging; National Institute of Mental Health; Neurosciences; Outcome; Patients; Performance; Placebos; Play; Positron-Emission Tomography; Process; Psychiatric therapeutic procedure; Psychopathology; Public Health; Quality of Care; Research; Research Personnel; Research Project Grants; Research Training; Resources; Risk; Role; Selection for Treatments; Sertraline; Source; Statistical Methods; Statistical Models; Structure; Time; Training; Training Programs; Work; Writing; big biomedical data; biosignature; burden of illness; clinical care; clinical imaging; data reduction; diverse data; experience; flexibility; functional magnetic resonance imaging/electroencephalography; health data; high dimensionality; imaging modality; individual patient; individualized medicine; innovation; insight; interest; large scale data; machine learning algorithm; machine learning method; multimodality; neuroimaging; neuroimaging marker; novel; personalized medicine; precision medicine; public health relevance; relating to nervous system; response; skills; statistical and machine learning; success; therapy development; tool; treatment effect; treatment response; user friendly software

Summary: The public health burden of major depressive disorder (MDD) is immense and current approaches for selecting antidepressant treatment have had limited success. By some estimates, fewer than one in three MDD patients will respond to their prescribed antidepressant and the quest for a treatment that will work is typically characterized by a lengthy course of trial-and-error. The need to identify patient characteristics (biomarkers) that can be used to objectively select personalized antidepressant treatment is clear. Accordingly, large clinical studies like the NIMH-funded Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) study have collected massive amounts of baseline measures including those from various neuroimaging sources in the hope that some can be used to guide antidepressant treatment selection. These data bring with them many statistical challenges that have yet to be effectively addressed. These challenges include (1) dealing with high-dimensionality, (2) handling data missingness, and (3) determining how best to simultaneously model relationships between measures from multiple imaging modalities and the response of interest. The goal of this project is to acquire the essential training and experience to make significant progress in this area by addressing each of these challenges. Aim 1 of this project will employ state-of-the-art ensemble machine learning algorithms and targeted estimation to identify moderators of antidepressant treatment effect using scalar clinical, demographic, and summary neuroimaging data from clinical trials of antidepressant treatments, including EMBARC. Strategies for handling missing data in this context will also be investigated and guidelines on best practices will be proposed. Aim 2 will extend the methods used in Aim 1 and develop user-friendly software to directly incorporate high- dimensional multimodal neuroimaging data into treatment decision rules. Included in this aim will be an investigation into best practices for handling missing high-dimensional imaging data in the context of estimating treatment decision rules. Aim 3 will employ the novel methods developed in Aim 2 and the estimated treatment decision rules will be evaluated and compared with those developed in Aim 1. I have put together a training program that directly supports the completion of these research aims. It includes instruction, mentoring, and hands-on-experience (1) in psychopathology and the neural basis for psychiatric disorders and treatment for those disorders; (2) in the use of neuroimaging data to understand depression and response to antidepressant treatment; (3) in the use of modern algorithms to store, process, manipulate, and analyze big biomedical data like those arising in multimodal neuroimaging studies. This K01 Mentored Research Scientist Development Award will provide the training, time, and resources to be able to make substantial progress in addressing this important problem and will provide the skills and experience that will be crucial in my transition to an independent investigator.

摘要：重性抑郁症（MDD）的公共卫生负担是巨大的，目前的方法 选择抗抑郁药治疗的成功率有限。据估计，不到三分之一的人 MDD患者会对他们的处方抗抑郁药产生反应，寻求有效的治疗方法是 其典型特征是漫长的试错过程。需要识别患者特征 （生物标志物）可以用来客观地选择个性化的抗抑郁治疗是明确的。 因此，大型临床研究，如NIMH资助的建立调节剂和生物特征， 临床护理的抗抑郁反应（EMBARC）研究收集了大量的基线 包括来自各种神经影像学来源的测量，希望其中一些可以用来指导 抗抑郁治疗选择这些数据带来了许多统计上的挑战， 有效解决。这些挑战包括（1）处理高维数据，（2）处理数据 缺失，以及（3）确定如何最好地同时对来自 多种成像模式和感兴趣的响应。该项目的目标是获得必要的 培训和经验，通过应对这些挑战，在这一领域取得重大进展。目的 该项目的1将采用最先进的集成机器学习算法和有针对性的估计， 使用临床、人口统计学和汇总量表确定抗抑郁治疗效果调节因子 来自抗抑郁治疗临床试验的神经影像学数据，包括EMBARC。处理策略 还将调查这方面的缺失数据，并提出最佳做法准则。目的2 将扩展目标1中使用的方法，并开发用户友好的软件，直接将高- 将多维多模态神经成像数据转化为治疗决策规则。这一目标将包括一个 在评估背景下处理缺失的高维成像数据的最佳实践调查 治疗决策规则。目标3将采用目标2中开发的新方法， 将对治疗决策规则进行评估，并与目标1中制定的规则进行比较。我整理了一份 培训计划，直接支持这些研究目标的完成。它包括指导， 指导和实践经验（1）精神病理学和精神疾病的神经基础， 治疗这些疾病;（2）使用神经影像学数据来了解抑郁症和对 抗抑郁治疗;（3）使用现代算法来存储，处理，操作和分析大数据 生物医学数据，如多模态神经成像研究中产生的数据。K 01指导的研究科学家 发展奖将提供培训，时间和资源，以便能够取得实质性进展， 解决这个重要的问题，并将提供技能和经验，这将是至关重要的，在我的过渡 交给独立调查员