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.
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| Rizk, Mina M; Rubin-Falcone, Harry; Lin, Xuejing et al. (2018) Gray matter volumetric study of major depression and suicidal behavior. Psychiatry Res Neuroimaging 283:16-23 |
| Daray, Federico M; Mann, J John; Sublette, M Elizabeth (2018) How lipids may affect risk for suicidal behavior. J Psychiatr Res 104:16-23 |
| Rizk, Mina M; Galfalvy, Hanga; Singh, Tanya et al. (2018) Toward subtyping of suicidality: Brief suicidal ideation is associated with greater stress response. J Affect Disord 230:87-92 |
| Rubin-Falcone, Harry; Zanderigo, Francesca; Thapa-Chhetry, Binod et al. (2018) Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder. J Affect Disord 227:498-505 |
| Boldrini, Maura; Fulmore, Camille A; Tartt, Alexandria N et al. (2018) Human Hippocampal Neurogenesis Persists throughout Aging. Cell Stem Cell 22:589-599.e5 |
| Youssef, Mariam M; Underwood, Mark D; Huang, Yung-Yu et al. (2018) Association of BDNF Val66Met Polymorphism and Brain BDNF Levels with Major Depression and Suicide. Int J Neuropsychopharmacol 21:528-538 |
| 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 |
| 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 |
| Coleman, Daniel; Lawrence, Ryan; Parekh, Amrita et al. (2017) Narcissistic Personality Disorder and suicidal behavior in mood disorders. J Psychiatr Res 85:24-28 |
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