Project 6 involves developing statistical methodology that will be applicable to many of the very high dimensional datasets that are being gathered as part of the Conte Center. In particular, we will focus on models with single outcome variables and very high-dimensional predictors, e.g., using gene expression data to discriminate between suicide attempters and depressed non attempters, or using brain imaging data to predict a patient's response to treatment for depression. This methodology will employ powerful newly developing statistical concepts and tools including functional data analytic methods, machine learning techniques, and prescreening algorithms. Emphasis will be on developing models that can both achieve accurate predictions and provide stable interpretable models, allowing for a deeper understanding of the biological basis of suicidal behavior and mental illness. The project involves development of appropriate methodology, application both to existing datasets and to those that will be gathered as part of the Conte Center, and comparison among the various modeling strategies using both simulation studies and real data validations.
In order to better understand the biological basis of suicidal behavior and mental illness, powerful methods for modeling data with very high dimensional data (e.g., brain imaging data, gene expression data) are required. This project is focused on developing appropriate statistical methodology that will allow for both accurate predictions and stable, interpretable models in situations arising as part of the Conte Center.
|Chaudhury, Sadia R; Galfalvy, Hanga; Biggs, Emily et al. (2017) Affect in response to stressors and coping strategies: an ecological momentary assessment study of borderline personality disorder. Borderline Personal Disord Emot Dysregul 4:8|
|Coleman, Daniel; Lawrence, Ryan; Parekh, Amrita et al. (2017) Narcissistic Personality Disorder and suicidal behavior in mood disorders. J Psychiatr Res 85:24-28|
|Bernanke, J A; Stanley, B H; Oquendo, M A (2017) Toward fine-grained phenotyping of suicidal behavior: the role of suicidal subtypes. Mol Psychiatry 22:1080-1081|
|Chesin, Megan S; Galfavy, Hanga; Sonmez, Cemile Ceren et al. (2017) Nonsuicidal Self-Injury Is Predictive of Suicide Attempts Among Individuals with Mood Disorders. Suicide Life Threat Behav 47:567-579|
|Ganança, Licinia; Galfalvy, Hanga C; Oquendo, Maria A et al. (2017) Lipid correlates of antidepressant response to omega-3 polyunsaturated fatty acid supplementation: A pilot study. Prostaglandins Leukot Essent Fatty Acids 119:38-44|
|Rubin-Falcone, Harry; Zanderigo, Francesca; Thapa-Chhetry, Binod et al. (2017) Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder. J Affect Disord 227:498-505|
|Fitzgerald, Megan L; Kassir, Suham A; Underwood, Mark D et al. (2017) Dysregulation of Striatal Dopamine Receptor Binding in Suicide. Neuropsychopharmacology 42:974-982|
|Ceñido, Joshua F; Itin, Boris; Stark, Ruth E et al. (2017) Characterization of lipid rafts in human platelets using nuclear magnetic resonance: A pilot study. Biochem Biophys Rep 10:132-136|
|Lawrence, Ryan E; Brent, David; Mann, J John et al. (2016) Religion as a Risk Factor for Suicide Attempt and Suicide Ideation Among Depressed Patients. J Nerv Ment Dis 204:845-850|
|Champagne, Frances A (2016) Epigenetic legacy of parental experiences: Dynamic and interactive pathways to inheritance. Dev Psychopathol 28:1219-1228|
Showing the most recent 10 out of 42 publications