Biomarker based classification and clustering of Veterans with PTSD

基于生物标志物的 PTSD 退伍军人分类和聚类

• 批准号: 10579692
• 负责人: ALAN N SIMMONS
• 金额: --
• 依托单位: VA SAN DIEGO HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579692
• 关键词: Acceleration; Address; Algorithms; Amygdaloid structure; Behavioral; Biological; Biological Markers; Brain; Caring; Chromosome Mapping; Classification; Clinical; Cognition; Collection; Complex; DNA analysis; Data; Data Set; Development; Diagnosis; Diagnostic and Statistical Manual of Mental Disorders; Dimensions; Disease; Evaluation; Formulation; Foundations; Gene Combinations; Genetic; Grant; Graph; Grouping; Heterogeneity; Individual; Lead; Link; Literature; Machine Learning; Maps; Measurement; Measures; Methodology; Methods; Modeling; Neurobiology; Persons; Population; Post-Traumatic Stress Disorders; Reporting; Research; Sampling; Subgroup; Symptoms; Techniques; Testing; Training; Trees; Validation; Veterans; Work; advanced analytics; analytical tool; biological adaptation to stress; clinical care; comorbidity; cost; data repository; deep learning model; density; experience; feature selection; genetic analysis; genetic predictors; genome wide association study; improved; individualized medicine; military veteran; neuroimaging; neuroimaging marker; novel; personalized medicine; polygenic risk score; programs; repository; research and development; response; self organization; success; therapy development; traumatic event; traumatic stress

Posttraumatic Stress Disorder (PTSD) is both impactful on a societal and individual basis. Better treatments are needed for our Veterans, and methodological advancements can facilitate progress in the understanding and treatment of PTSD. With the dramatically reduced cost of collection and analysis of DNA there is now many large repositories that contain relatively comprehensive genetic data. Furthermore, on the individual level, many Veterans have genetic data available through commercial (e.g., 23 and Me) or research (e.g., the Million Veteran Program) entities. It is possible that access to their own genetic data will allow people to leverage the latest research developments in the aims of receiving state-of-the-art personalized medicine. New algorithmic advances can allow this data to be utilized for improving our understanding of conditions, such as PTSD. Machine learning provides a way to better identify those with PTSD through methods such as boosted trees or deep learning models. Clustering techniques can provide a way to clarify homogenous subgroups within Veteran samples. This project looks to take a supervised (aka, classification) and unsupervised (aka, clustering) analytical approach to better understand PTSD using genetic and neuroimaging data from publicly available data repositories. For this work, features are selected based on the latest advancements in the literature and models are selected through rigorous empirical evaluation. Treatment for PTSD, and other disorders, is potentially limited by classifications that are based on symptom reports such as used in DSM. This traditional approach creates biologically heterogeneous samples. We propose applying advanced analytical tools to empirically derived biomarkers to create homogeneous biologically-based groupings.

创伤后应激障碍（PTSD）对一个社会和 个人基础。我们的退伍军人需要更好的治疗方法和方法论 进步可以促进理解和治疗PTSD的进步。 随着收集成本和DNA分析的大幅降低， 现在，许多大型存储库中包含相对全面的遗传数据。 此外，在个人层面上，许多退伍军人都有可用的遗传数据 通过商业（例如23和我）或研究（例如，《百万退伍军人计划》） 实体。访问自己的遗传数据可能会使人们能够利用 最新的研究发展是为了获得最新的个性化 药品。新的算法进步可以允许将这些数据用于改进 我们对诸如PTSD之类的条件的理解。机器学习提供了一种方法 更好地通过改造树或深度学习等方法来识别具有PTSD的人 型号。聚类技术可以提供澄清同质亚组的方法 在退伍军人样品中。 该项目看起来需要进行监督（又称分类）和无监督的 （又称聚类）分析方法，可以通过遗传和 来自公开可用数据存储库的神经影像学数据。对于这项工作，功能是 根据文献和模型的最新进步选择选择 通过严格的经验评估。 PTSD和其他疾病的治疗是 可能受到基于症状报告的分类的限制 DSM。这种传统的方法创建了生物学上异质的样品。我们 建议将高级分析工具应用于经验得出的生物标志物以创建 基于生物学的均质分组。