Thursday, November 21, 2024
11/21/2024
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.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
