Bioinformatics for post-traumatic stress

项目总结/摘要 创伤后的适应不良并发症，包括创伤后应激（PTS），在这两个国家都非常普遍。 退伍军人和平民，并一直难以准确诊断，管理和治疗。 诊断标准和需要代表有助于 精神病理学已经催生了研究领域标准（RDoC）的国家发展 心理健康研究所（NIMH）。RDoC是一个开发中的框架，用于帮助指导发现和验证 心理健康障碍的新维度及其与潜在生物机制的关系。 NIMH现在有一个丰富的联邦数据库，目前包含来自RDoC赞助的临床试验的原始数据。 国家临床试验数据库（NDCT）中的研究和临床试验数据， 有助于解开PTS症状和潜在的 生物标志物，以促进创伤康复的诊断和治疗框架的改进。的 拟议的项目将应用生物信息学和机器学习分析工具，以这些大型，异构 识别和验证创伤相关精神病理学和治疗的新研究维度的数据集 反应轨迹及其预测因子。目标1将通过以下方式开发计算机模拟创伤患者人群： 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 存储在可用的数据库中，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， 影像学和其他相关研究和元数据。目标2将确定PTS诊断的多个维度 标准，使用内部和外部无监督降维统计方法的组合 交叉验证和预测模型的监督假设检验，以了解 PTS的亚型。AIM 3将部署无监督机器学习方法，如拓扑数据分析 和层次聚类，以确定基于肿瘤学的独特患者群， 基于疾病严重程度的PTS患者精确映射的聚类方法。目标4将使用监督 机器学习技术用于有针对性的预测分析，重点是识别治疗反应者， NDCT和预测治疗反应的潜在变量的识别。建议的结果 研究项目将大大丰富计算精神病学研究领域，以确定保守的 与精神病理学和精确治疗计划的复杂关系相关的维度 在经历了创伤性事件之后

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.