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 and treatment response. Toward these goals, the program also supports translational research focused on novel approaches to classification of mental disorders and the use of computational models for validating RDoC constructs in the clinic.
Areas of Emphasis
- Development and validation of models for optimizing the precision of treatments for reducing the severity and incidence of psychopathology
- Translational efforts to adapt computational models developed via basic research to address clinical issues by informing the models with data from clinical populations
- Models to predict treatment response, relapse, and/or vulnerability to side effects of therapeutic interventions (including psychotropic medications, neuromodulation, and psychosocial interventions) on the level of the individual
- Biologically realistic and integrative models incorporating molecular, cellular, neural, behavioral and other data to identify psychopathology phenotypes that transcend heterogeneous diagnostic categories
- Applying innovations in data science and machine learning to understand how neural activity in widely distributed brain systems encodes mental states in psychiatric disorders across development
Abera Wouhib, Ph.D.
Interim Program Officer