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: 10709860
• 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/10709860
• 关键词: Affect; Arousal; Award; Awareness; 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 Imaging; Maps; Mental Depression; Mental Health; Mental disorders; Mentors; Mentorship; Methodology; Modeling; Modernization; Moods; Neurobiology; Patients; Pharmacotherapy; Phenotype; Physicians; Population; Positioning Attribute; Post-Traumatic Stress Disorders; Psychiatric Diagnosis; Psychiatrist; Psychiatry; Psychotherapy; Qualifying; Quality of life; Research; Rest; Scientist; Services; Severities; Suicide; Symptoms; System; Testing; Time; Training; Veterans; Vietnam; Work; advanced analytics; analytical method; analytical tool; brain based; career; clinical care; clinical heterogeneity; clinical training; 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)是退伍军人中一种非常普遍的慢性精神障碍 以及更广泛的美国人口。它通常与严重的耻辱、心理社会的削弱有关 身体机能不佳，身体健康差，生活质量下降。尽管有创伤后应激障碍的影响，但准确的 诊断往往很困难。创伤后应激障碍是一种多方面的疾病，临床表现多种多样。它 与其他精神障碍高度共存，退伍军人和患者经常表现出无数的 明显的症状。对创伤后应激障碍的更好的神经生物学和网络层面的理解可以导致 明确的诊断和更先进、更有针对性和个性化的治疗。尽管如此， 创伤后应激障碍的生物学机制尚不完全清楚。此外，寻找创伤后应激障碍的单一生物标记物 事实证明这很难。这很可能是因为呈现的多样性，以及不同的潜力 生物学亚型存在于临床症状谱中。 最近，出现了先进的计算工具，它们可以解析这种高度的复杂性和 因此，很有希望发展个性化的、基于神经生物学的和客观的 创伤后应激障碍的生物标志物。CDA-2的主要研究目标是开发一个客观的大脑- 基于身份识别，可用于对退伍军人痛苦的个性化诊断和治疗 来自创伤后应激障碍。第一个具体目标是评估机器学习算法是否可以将PTSD 症状与个人神经成像数据中的信息一致。第二个具体目标将测试 是否可以使用机器学习算法将大脑网络与DSM-5创伤后应激障碍症状联系起来 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两者之间的关系 个人创伤后应激障碍症状和基于连接的网络，以确定是否可以将症状分组 不同于制造经修改的、数据驱动的创伤后应激障碍诊断工具。 这项研究的发现将为未来的功勋资助工作提供基础数据，并为 为将数据科学带入研究和临床的计划性、独立的退伍军人管理局职业生涯奠定基础 关心。由CDA奖励资助的受保护时间将允许候选人参加活动 传授独特的技能和观点的结合，使他能够在基础和临床之间架起桥梁 科学服务于为患有创伤后应激障碍的退伍军人寻找更好的治疗选择。这个CDA-2将允许 为获得将神经成像、机器学习和高级 分析方法。候选人在退伍军人制度内地位稳固，目前拥有一名员工 医生(精神病学家)的职位。他还积极参与临床研究，取得了成功 进行临床和转化性研究的记录。候选人的指导团队由以下人员组成 退伍军人管理局临床医生和科学家积极与退伍军人合作，特别是那些患有创伤后应激障碍的人。他们也 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- 有资格指导候选人实现成为独立退伍军人的职业目标 医生-科学家，拥有积极的研究计划，利用数据科学工具理解心理 更好的疾病神经生物学，开发新的个性化治疗方法。