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

使用数据驱动方法识别 PTSD 的独特多模式生物型：多站点大数据研究

• 批准号: 10521278
• 负责人: Xi Zhu
• 金额: $ 17.59万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-10 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10521278
• 关键词: Address; Advanced Development; Anxiety; Anxiety Disorders; Authorization documentation; Behavioral; Big Data; Biological; Biological Markers; Brain; Brain imaging; Categories; Classification; Clinical; Clinical Treatment; Clinical Trials; Cluster Analysis; Complex; DSM-IV; Data; Data Set; Dependence; Development; Diagnosis; Diagnostic; Dimensions; Disease; Ensure; Environment; Fostering; Fright; Functional disorder; Future; Goals; Grant; Heterogeneity; Individual; Knowledge; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Measures; Mental Depression; Mentored Research Scientist Development Award; Mentorship; Methods; Modeling; Multimodal Imaging; National Institute of Mental Health; Neurobiology; Patients; Pattern; Pattern Recognition; Pharmaceutical Preparations; Post-Traumatic Stress Disorders; Prediction of Response to Therapy; Prevalence; Psychiatry; Psychoses; Psychotherapy; Public Health; Reproducibility; Research; Research Personnel; Research Project Grants; Research Support; Rest; Sample Size; Selection for Treatments; Site; Subgroup; Symptoms; Techniques; Training; Translational Research; Trauma; Treatment outcome; Work; advanced analytics; anxiety-related disorders; associated symptom; authority; biomarker identification; biotypes; clinical heterogeneity; clinical phenotype; comorbidity; data fusion; design; experience; gray matter; individualized medicine; large datasets; machine learning model; memory process; multimodal data; multimodal neuroimaging; multimodality; neural; neurobiological mechanism; patient oriented; predicting response; programs; reward processing; skills; supervised learning; therapy development; trauma exposure; treatment comparison; treatment research; treatment response; treatment strategy; unsupervised learning; working group

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.

创伤后应激障碍（PTSD）是一种非常普遍且使人衰弱的疾病。尽管努力