Identification of Distinct Multimodal Biotypes of PTSD Using Data Driven Approach: A Multisite Big Data Study

创伤后应激障碍(PTSD)是一种非常普遍的、使人衰弱的疾病。尽管做出了努力 创伤后应激障碍的病理生理特征及其异质性，尚未建立客观的生物标志物 以帮助诊断和预测治疗反应。本K01介绍了一项研究和培训计划 这将支持申请人走上成为独立调查员的道路，专注于利用 识别多峰神经生物标志物的数据驱动计算方法和机器学习技术 创伤后应激障碍(有监督)和多模式创伤后应激障碍的生物型(无监督)，并探索这些生物型是否可以 用于预测对长期暴露(PE)的反应，这是创伤后应激障碍的一线治疗方法。培训计划 以应聘者先前的培训和经验为基础，并利用指导团队和研究 培养候选人在1)创伤后应激障碍的神经和行为基础方面的专业知识的环境 数据驱动下的多通道数据融合分析和潜在维度解释 计算方法和数据再现性；3)以患者为中心的焦虑症翻译研究 精神错乱。该研究项目将监督和非监督机器学习技术应用于 来自现有最大的创伤后应激障碍数据集的多模式MRI数据(来自Enigma-PTSD工作组的N~3000)。 从这个大型数据集中确定的生物类型随后将扩展到临床治疗数据。评选结果 拟议的研究将有助于寻找创伤后应激障碍的神经生物标志物，并更好地预测不同的治疗方法 通过不同的生物型靶点的结果，并将导致未来的R01拨款检查大脑症状 焦虑和创伤相关障碍之间的联系，并使用新发现的创伤后应激障碍生物型来告知 在随后的R61/33中不同的治疗结果。总而言之，研究和培训经验和 通过该K01奖项开发的专业知识将支持申请人过渡到研究独立和 确保申请者成为在以下领域应用数据驱动计算方法的领先权威 精神病学研究，并为未来NIMH拨款探索多模式大脑的生物型提供基础 在焦虑相关障碍中使用数据驱动的计算方法进行成像。

Posttraumatic stress disorder (PTSD) is a highly prevalent and debilitating disorder. Despite efforts to characterize the pathophysiology of PTSD and its heterogenity, no objective biomarker have been established to aid in diagnosis, and prediction of treatment response. This K01 presents a program for research and training that will support the applicant on a path towards becoming an independent investigator, focused on utilizing a data-driven computational approach and machine learning techniques to identify multimodal neural biomarkers of PTSD (supervised) and multimodal biotypes of PTSD (unsupervised) and explore whether such biotypes could be used to predict response to prolonged exposure (PE), the first line treatment for PTSD. The training plan builds on the candidate’s prior training and experience and capitalizes on a mentorship team and a research environment to foster development of the candidate’s expertise in 1) the neural and behavioral basis of PTSD and anxiety disorders; 2) multimodal data fusion analysis and latent dimension interpretation with data-driven computational approaches and data reproducibility; and 3) patient-oriented translational research in anxiety disorders. This research project will apply both supervised and unsupervised machine learning techniques on multimodal MRI data from the largest existing PTSD dataset (N~3000 from the ENIGMA-PTSD working group). Biotypes identified from this large dataset will then be extended to clinical treatment data. The results of the proposed research will be vital to aid in finding neural biomarkers of PTSD and better predict different treatment outcomes through different biotype targets and will lead to a future R01 grant examining brain-symptoms association across anxiety and trauma-related disorders, and to use the newly identified PTSD biotypes to inform different treatment outcomes in a following R61/33. Together, the research and training experiences and expertise developed through this K01 award will support the applicant’s transition to research independence and ensure the applicant becomes a leading authority in the application of data-driven computational approaches in psychiatry research, and provide the basis for future NIMH grants to explore biotypes from multimodal brain imaging using data-driven computational approaches across anxiety-related disorders.