The goal of my proposed K01 application is to receive training and conduct research that will lead to the development of a new methodology for the early identification of posttraumatic stress outcomes. This proposal capitalizes on my previous research to identify empirically derived outcomes of posttraumatic stress and resilience that are agnostic to DSM-based diagnostic criteria. This work has identified a limited number of common patterns of response including resilience, slow recovery, and chronic posttraumatic stress. Further, the proposal capitalizes on training I have received in the neurobiology of posttraumatic stress disorder. My training throughout the K01 will focus on learning a new set of modeling techniques (Machine Learning) that will allow me to predict an individual's likely course following trauma exposure based on neuroendocrine abnormalities and cognitive and emotional characteristics in the acute phase, genetic polymorphisms, and demographic characteristics. Further, my training will focus on greatly increasing my theoretical understanding of the neurobiological underpinnings of symptom responses, as well as alterations in cognition and emotion in response to stress and traumatic stress, providing the theoretical basis for the predictive models. I have selected mentors, sponsors, and advisors who have the specific backgrounds and knowledge to aid me in achieving these goals and with whom I am currently collaborating on preliminary research in this area. The ultimate goal of this proposal is to develop new methods for the early identification of individuals in need of therapeutic intervention following trauma exposure. A second but equally important goal is the identification of new treatment targets based on abnormal characteristics that present early on in individuals who will later develop long-term posttraumatic stress outcomes. This K01 proposal is ambitious, but it is also feasible thanks to the intellectual support of my proposed mentors, sponsors, and advisors along with considerable financial and resource support from the NYU Department of Psychiatry. The combined research and training plan will provide me with skills, theoretical knowledge, and data to launch a focused independent career that fills an important public health gap. Currently, there are not suitable methods to identify individuals in need of treatment early on following trauma or methods for identifying individuals specific treatment needs. While this K01 proposal will not achieve these aims directly, it will provide me with the necessary skills to make a significant contribution in this area over the long term.
The purpose of the proposed research and training plan is to develop innovative methods for the early identification of posttraumatic stress responses and resilience based on putative cognitive, emotional, and biological characteristics that differentiat healthy from maladaptive responses to trauma. This effort can lead to the early identification of individuals in need of clinical services and the identification of underlying dimensions of dysfunction that can become new treatment targets for alleviating the disease burden of PTSD. This effort has significant public health implications, as it can lead to new treatment targets and result in the accurate allocation of treatment resources to those most in need.
|Galatzer-Levy, Isaac R; Ruggles, Kelly; Chen, Zhe (2018) Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience. Chronic Stress (Thousand Oaks) 2:|
|Galatzer-Levy, I R; Ma, S; Statnikov, A et al. (2017) Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Transl Psychiatry 7:e0|
|Malgaroli, Matteo; Galatzer-Levy, Isaac R; Bonanno, George A (2017) Heterogeneity in Trajectories of Depression in Response to Divorce is Associated with Differential Risk for Mortality. Clin Psychol Sci 5:843-850|
|Stevens, Jennifer S; Kim, Ye Ji; Galatzer-Levy, Isaac R et al. (2017) Amygdala Reactivity and Anterior Cingulate Habituation Predict Posttraumatic Stress Disorder Symptom Maintenance After Acute Civilian Trauma. Biol Psychiatry 81:1023-1029|
|Galatzer-Levy, Isaac R; Andero, Raül; Sawamura, Takehito et al. (2017) A cross species study of heterogeneity in fear extinction learning in relation to FKBP5 variation and expression: Implications for the acute treatment of posttraumatic stress disorder. Neuropharmacology 116:188-195|
|Maccallum, Fiona; Galatzer-Levy, Isaac R; Bonanno, George A (2015) Trajectories of depression following spousal and child bereavement: A comparison of the heterogeneity in outcomes. J Psychiatr Res 69:72-9|
|Galatzer-Levy, Isaac R (2015) Applications of Latent Growth Mixture Modeling and allied methods to posttraumatic stress response data. Eur J Psychotraumatol 6:27515|
|Karstoft, Karen-Inge; Galatzer-Levy, Isaac R; Statnikov, Alexander et al. (2015) Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry 15:30|
|Galatzer-Levy, Isaac R; Bonanno, George A (2014) Optimism and death: predicting the course and consequences of depression trajectories in response to heart attack. Psychol Sci 25:2177-88|
|Galatzer-Levy, Isaac R; Karstoft, Karen-Inge; Statnikov, Alexander et al. (2014) Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application. J Psychiatr Res 59:68-76|
Showing the most recent 10 out of 13 publications