Computational Psychiatry Program
The overarching goal of this program is to foster innovative computational approaches to identify and validate novel mechanisms, biomarkers, and treatment targets relevant to the prevention and treatment of psychiatric disorders. The program supports research projects that use advanced computational tools with behavioral, biological, and/or clinical data to decipher complex mechanisms involved in mental disorders and to predict risk, clinical trajectories, and treatment response. Toward these goals, the program also supports translational research focused on novel computational approaches to dissecting the heterogeneity of mental disorders and the use of computational models for validating RDoC constructs in the clinic.
Areas of Emphasis
- Translational efforts to use computational models of basic neurobehavioral, cognitive, and affective processes to understand mechanisms underlying mental disorders
- Biologically realistic and integrative models incorporating genetic, molecular, cellular, neural, behavioral, and other data to identify psychopathology phenotypes that transcend heterogeneous diagnostic categories
- Novel tools for digital phenotyping, including those that use device-based data collection methods, information harvested from health records, and multi-modal approaches
- Precision psychiatry approaches using state-of-the-art data science tools, including artificial intelligence and machine learning, to improve the accuracy of individual-level predictions of treatment response and clinical trajectories, including efforts to reduce bias in such approaches
Abera Wouhib, Ph.D.
Interim Program Officer