Developing Computational Nosologies of Posttraumatic Stress Disorder (PTSD)

开发创伤后应激障碍 (PTSD) 的计算疾病分类学

• 批准号: 10426259
• 负责人: Amin Zand Vakili
• 金额: --
• 依托单位: PROVIDENCE VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10426259
• 关键词: Affect; Arousal; Award; Awareness; Base of the Brain; Basic Science; Biological; Biological Markers; Brain; Caring; Chronic; Clinical; Clinical Research; Clinical Sciences; Cognition; Complex; DSM-V; Data; Data Science; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Disease; Epidemic; Foundations; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Future; Goals; Health; Heterogeneity; Individual; Intervention; Lead; Learning; Link; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Mental Depression; Mental Health; Mental disorders; Mentors; Mentorship; Methodology; Modeling; Modernization; Moods; Network-based; Neurobiology; Patients; Pharmacotherapy; Phenotype; Physicians; Population; Positioning Attribute; Post-Traumatic Stress Disorders; Psychiatric Diagnosis; Psychiatrist; Psychiatry; Psychotherapy; Quality of life; Research; Research Training; Rest; Scientist; Services; Severities; Suicide; Symptoms; System; Testing; Time; Training; Veterans; Vietnam; Work; advanced analytics; analytical method; analytical tool; base; career; clinical care; clinical heterogeneity; comorbidity; computerized tools; design; diagnostic strategy; diagnostic tool; disease classification; experience; improved; individualized medicine; innovation; machine learning algorithm; neural network; neuroimaging; neuroregulation; novel; novel strategies; phenomenological models; physical conditioning; programs; psychosocial; service member; skills; social stigma; substance use; symptom cluster; symptomatology; tool; tractography; translational study

Posttraumatic Stress Disorder (PTSD) is a highly prevalent and chronic psychiatric disorder in Veterans and the broader US population. It is often associated with significant stigma, diminished psychosocial functioning, poor physical health, and lessened quality of life. Despite the impact of PTSD, a precise diagnosis is often difficult. PTSD presents as a multi-faceted illness with variable clinical presentation. It is highly comorbid with other psychiatric disorders, and Veterans and patients often express a myriad of distinct symptoms. A better neurobiological and network-level understanding of PTSD can lead to diagnostic clarity and more advanced, targeted, and individualized treatments. Despite this, the biological mechanisms of PTSD are not fully understood. Also, finding a unitary biomarker of PTSD has proven difficult. This is likely because of the diversity of presentation, and the potential that different biological subtypes exist within the clinical symptom profile. Recently, advanced computational tools have emerged that can parse this high level of complexity and thus hold significant promise to develop individualized and neurobiologically-based and objective biomarkers of PTSD. The primary research objective of this CDA-2 is to develop an objective brain- based identification that can be used to individualize diagnosis and treatment for Veterans suffering from PTSD. The first specific aim will evaluate whether a machine learning algorithm can link PTSD symptoms with the information in individuals’ neuroimaging data. The second specific aim will test whether a machine learning algorithm can be used to link brain networks to DSM-5 PTSD symptom clusters in order to enable mapping of network abnormalities to commonly recognized DSM-5 domains of PTSD. The candidate’s exploratory research objective will investigate the relationship between individual PTSD symptoms and connectivity-based networks to determine if symptoms can be grouped differently to make a modified and data-driven PTSD diagnostic tool. The findings from this study will provide foundational data for future Merit-funded work and lay the foundation for a programmatic, independent VA career bringing data science to research and clinical care. The protected time funded by this CDA award will allow the candidate to participate in activities imparting a unique combination of skills and perspectives that will allow him to bridge basic and clinical science in the service of finding better treatment options for Veterans with PTSD. This CDA-2 will allow for the time to gain the critical skills needed to integrate neuroimaging, machine learning, and advanced analytic methods. The candidate is well-established within the VA system and currently holds a staff physician (psychiatrist) position. He is also actively involved in clinical research with a successful track record of conducting clinical and translational studies. The candidate’s mentorship team is comprised of VA clinicians and scientists actively working with veterans, specifically those with PTSD. They also have expertise in data science, computational psychiatry, and advanced analytics. They are well- qualified to mentor the candidate toward the career goal of becoming an independent VA based physician-scientist with an active research program, leveraging data science tools to understand mental illness neurobiology better and develop new and individualized treatments.

创伤后应激障碍（PTSD）是退伍军人中一种高度流行的慢性精神疾病 更广泛的美国人口。它往往与严重的耻辱感，减少心理社会 功能障碍、身体健康状况不佳和生活质量下降。尽管创伤后应激障碍的影响，一个精确的 诊断往往很困难。创伤后应激障碍是一种多方面的疾病，临床表现各异。它 与其他精神疾病高度共病，退伍军人和患者经常表达无数的 明显的症状对PTSD的神经生物学和网络水平的更好理解可以导致 诊断清晰度和更先进的，有针对性的和个性化的治疗。尽管如此， PTSD的生物学机制尚未完全了解。此外，发现创伤后应激障碍的单一生物标志物 证明困难。这可能是因为呈现的多样性，以及不同的潜在性。 生物亚型存在于临床症状谱中。 最近，出现了先进的计算工具，可以解析这种高水平的复杂性， 因此，具有重要的承诺，发展个性化和神经生物学为基础的和客观的， 创伤后应激障碍的生物标志物本CDA-2的主要研究目标是开发一个客观的大脑- 基于身份的识别，可用于个性化诊断和治疗退伍军人遭受 创伤后应激障碍第一个具体目标将评估机器学习算法是否可以将PTSD与 症状与个体神经影像学数据中的信息进行比较。第二个具体目标将测试 机器学习算法是否可用于将大脑网络与DSM-5 PTSD症状联系起来 集群，以便能够将网络异常映射到公认的DSM-5域 创伤后应激障碍候选人的探索性研究目标将调查 个体PTSD症状和基于连通性的网络，以确定症状是否可以分组 以不同的方式来制作一个修改的和数据驱动的PTSD诊断工具。 这项研究的结果将为未来的Merit资助工作提供基础数据，并奠定 为程序化的独立VA职业生涯奠定基础，将数据科学带入研究和临床 在乎该CDA奖资助的受保护时间将允许候选人参加活动 传授技能和观点的独特组合，使他能够在基础和临床之间架起桥梁 科学服务于为患有PTSD的退伍军人找到更好的治疗方案。CDA-2将允许 获得整合神经成像、机器学习和高级 分析方法候选人在VA系统内建立良好，目前拥有一名工作人员 医生（精神病医生）职位。他还积极参与临床研究，并取得了成功 进行临床和转化研究的记录。候选人的导师团队由以下人员组成： 退伍军人管理局的临床医生和科学家积极与退伍军人，特别是那些与创伤后应激障碍。他们还 拥有数据科学、计算精神病学和高级分析方面的专业知识。他们很好- 有资格指导候选人走向成为一个独立的VA为基础的职业目标 拥有活跃研究计划的医生科学家，利用数据科学工具来了解心理 疾病神经生物学更好，并开发新的和个性化的治疗。