Bioinformatics for post-traumatic stress

创伤后应激的生物信息学

• 批准号: 9760006
• 负责人: Jessica Leigh Nielson
• 金额: $ 50.2万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-10 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9760006
• 关键词: Bioinformatics; Biological; Biological Markers; Categories; Clinical; Clinical Data; Clinical Research; Clinical Trials; Clinical Trials Database; Complex; Computer Simulation; Data; Data Analyses; Data Set; Databases; Development; Diagnostic; Diagnostic tests; Diagnostics Research; Dimensions; Disease; Exposure to; Genomics; Growth; Image; Laboratories; Linear Models; Linear Regressions; Logistics; Machine Learning; Maps; Measures; Medical History; Mental Health; Mental disorders; Metadata; Methods; Modeling; National Institute of Mental Health; Nervous System Trauma; Neurocognitive; Observational Study; Outcome; Pathology; Patients; Pattern; Phenotype; Population; Precision therapeutics; Prediction of Response to Therapy; Predictive Analytics; Principal Component Analysis; Psychiatry; Psychopathology; Recovery; Reproducibility; Research; Research Domain Criteria; Research Project Grants; Severity of illness; Source; Statistical Methods; Supervision; Symptoms; Syndrome; Techniques; Testing; Therapeutic; Trauma; Trauma Research; Trauma patient; Trauma recovery; Traumatic Brain Injury; Validation; Veterans; Work; accurate diagnosis; analytical tool; base; biobehavior; combat; computational platform; data archive; data mining; data sharing; data warehouse; demographics; federated computing; guided inquiry; hands-on learning; indexing; innovation; insight; interest; learning strategy; multimodality; patient population; patient subsets; post-traumatic stress; precision medicine; predictive modeling; predictive test; psychologic; research and development; research study; response; statistics; stress related disorder; supervised learning; symptomatology; tool; trauma exposure; traumatic event; treatment planning; treatment responders; treatment response; unsupervised learning; vector

Project Summary/Abstract Maladaptive complications following trauma, including post-traumatic stress (PTS), are highly prevalent in both veterans and civilians, and have been difficult to accurately diagnose, manage and treat. Debate regarding diagnostic criteria and the need to represent the full spectrum of inter-connected features contributing to psychopathology has spawned the development of the Research Domain Criteria (RDoC) by the National Institute of Mental Health (NIMH). RDoC is a developing framework to help guide the discovery and validation of new dimensions of mental health disorders and their relationships to underlying biological mechanisms. NIMH now has a rich federated database that currently houses raw data from RDoC-sponsored clinical research, and clinical trial data from the National Database of Clinical Trials (NDCT) with information that may help to unlock the complex and overlapping relationships between symptoms of PTS and the underlying biomarkers to fuel improvements on diagnostic and therapeutic frameworks for trauma recovery. The proposed project will apply bioinformatics and machine learning analytical tools to these large, heterogeneous datasets to identify and validate new research dimensions of trauma-related psychopathology and treatment response trajectories and their predictors. Aim 1 will develop an in silico trauma patient population by integrating data from diverse sources, including cross-sectional and observational longitudinal clinical studies housed within available data repositories for trauma and other related mental health research. Data will include medical history, demographics, diagnostic tests, clinical outcomes, psychological assessments, genomics, imaging, and other relevant study and meta-data. Aim 2 will identify multiple dimensions of PTS diagnostic criteria, using a combination of unsupervised dimension-reduction statistical methods, internal and external cross-validation, and supervised hypothesis testing of predictive models to understand the heterogeneous subtypes of PTS. Aim 3 will deploy unsupervised machine learning methods, such as topological data analysis and hierarchical clustering, to identify unique clusters of patients based on symptomatology to develop clustering methods for precision mapping of PTS patients based on disease severity. Aim 4 will use supervised machine learning techniques for targeted predictive analytics focused on identifying treatment responders from the NDCT, and identification of latent variables that predict treatment response. The results of the proposed research project will greatly enrich the field of computational psychiatry research to identify conserved dimensions associated with the complex relationships of psychopathology and precision treatment planning following exposure to traumatic events.

项目摘要/摘要 创伤后的不良适应并发症，包括创伤后应激（PTS），两者都高度普遍存在 退伍军人和平民，很难准确诊断，管理和治疗。关于 诊断标准以及需要代表各种相互连接的特征的需求 心理病理学催生了研究领域标准（RDOC）的发展 心理健康研究所（NIMH）。 RDOC是一个开发框架，可帮助指导发现和验证 精神健康障碍的新维度及其与潜在生物学机制的关系。 NIMH现在拥有一个丰富的联邦数据库，目前包含来自RDOC赞助的临床的原始数据 研究，以及来自国家临床试验数据库（NDCT）的临床试验数据，其中可能 帮助解锁PT症状与基础症状之间的复杂和重叠关系 生物标志物可以为创伤恢复的诊断和治疗框架加油。这 拟议的项目将应用生物信息学和机器学习分析工具对这些大型，异质 数据集以识别和验证与创伤有关的心理病理学和治疗的新研究维度 响应轨迹及其预测因素。 AIM 1将通过 整合来自不同来源的数据，包括横断面和观察性纵向临床研究 内置在可用的数据存储库中，用于创伤和其他相关的心理健康研究。数据将包括 病史，人口统计学，诊断测试，临床结果，心理评估，基因组学， 成像以及其他相关研究和元数据。 AIM 2将确定PTS诊断的多个维度 标准，结合了无监督的尺寸统计方法，内部和外部 交叉验证和监督预测模型的假设检验，以了解异质 PTS的亚型。 AIM 3将部署无监督的机器学习方法，例如拓扑数据 和分层聚类，以根据症状发展来识别患者的独特簇 基于疾病严重程度的PTS患者精确映射的聚类方法。 AIM 4将使用监督 针对有针对性预测分析的机器学习技术着重于从 NDCT以及预测治疗反应的潜在变量的识别。提议的结果 研究项目将极大地丰富计算精神病学研究领域，以确定保守的 与精神病理学和精度治疗计划的复杂关系相关的维度 暴露于创伤事件之后。