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: 10260058
• 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/10260058
• 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; Structure; 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; 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）是退伍军人的高度流行和慢性精神病 以及更广泛的美国人口。它通常与重大污名相关，心理社会降低 运作，身体健康状况不佳，生活质量降低。尽管有PTSD的影响，精度 诊断通常很困难。 PTSD表现为具有可变临床表现的多面疾病。它 与其他精神疾病高度合并，退伍军人和患者经常表达无数 不同的符号。对PTSD的更好的神经生物学和网络级别的理解可以导致 诊断清晰度，更先进，有针对性和个性化治疗。尽管如此， PTSD的生物学机制尚不完全了解。此外，找到PTSD的统一生物标志物具有 被证明困难。这可能是因为演示的多样性以及不同的潜力 生物亚型存在于临床符号内部。 最近，出现了高级计算工具，可以解析这种高度的复杂性和 因此，具有开发个性化和神经生物学基础和客观的巨大希望 PTSD的生物标志物。该CDA-2的主要研究目标是开发客观的大脑 可用于个性化诊断和治疗退伍军人苦难的基于识别 来自PTSD。第一个特定目的将评估机器学习算法是否可以链接PTSD 症状带有个人神经影像数据中的信息。第二个特定目标将测试 是否可以使用机器学习算法将大脑网络与DSM-5 PTSD症状联系起来 群集为了使网络异常映射到普遍识别的DSM-5域 PTSD。候选人的探索性研究目标将调查 单个PTSD症状和基于连接性的网络，以确定是否可以分组症状 以不同的方式制作了修改和数据驱动的PTSD诊断工具。 这项研究的发现将为未来的绩效资助工作提供基本数据，并进行 编程，独立VA职业的基金会，将数据科学带入研究和临床 关心。由该CDA奖资助的受保护时间将允许候选人参加活动 赋予他独特的技能和观点组合，使他能够桥接基本和临床 科学为使用PTSD寻找退伍军人的更好的治疗选择。这个CDA-2将允许 为了获得整合神经成像，机器学习和高级所需的关键技能 分析方法。候选人在VA系统中建立了良好的成就，目前拥有员工 物理学（精神科医生）位置。他还积极参与成功的临床研究 进行临床和翻译研究的记录。候选人的心态团队已完成 VA临床医生和科学家与退伍军人，特别是患有PTSD的退伍军人一起工作。他们也是 在数据科学，计算精神病学和高级分析方面拥有专业知识。他们很好 有资格指导候选人以成为独立VA的职业目标 带有活跃研究计划的身体科学家，利用数据科学工具来了解心理 疾病神经生物学更好，并发展新的和个性化的治疗方法。