Developing Computational Nosologies of Posttraumatic Stress Disorder (PTSD)

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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.