Translational Digital and Computational Psychiatry Program

Overview

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 methods with behavioral, biological, and/or clinical data to decipher complex mechanisms involved in mental disorders and to conduct initial tests of novel tools 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 Research Domain Criteria (RDoC) constructs in clinical populations. The program encourages the development and testing of novel tools that modulate mental health treatment targets or enhance the precision of mental health assessments through the use of passive and active sensing devices. It also focuses on research and development of novel computational methods used in the diagnosis, treatment, and prevention of various forms of psychopathology. Consistent with the focus of the branch on adult psychopathology applications assigned to this program address anxiety and mood disorders, personality pathology, and psychosis in adults 18 years of age and older. Computational methods in this program typically involve research translating basic science knowledge to discover phenotypic characterization, etiology, pathophysiology, trajectory, risk and resilience, and develop efficacious interventions for mental health conditions. These translational research studies include software-based tools being tested for early-stage efficacy, typically phase I/II Clinical Trials.

Areas of Emphasis

• Translational efforts to use computational models of fundamental 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.
• Development of novel translational tools for digital phenotyping, including those that use device-based data collection methods, information harvested from multiple data sources, and multi- modal approaches.
• Early-stage development and initial tests of precision psychiatry approaches that employ novel 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.
• Early-stage development and testing of novel tools to perform functions such as clinical decision support, computer-assisted detection/diagnosis, companion diagnostics, digital therapeutics, and remote patient monitoring.
• Application leveraging novel computational/mHealth/digital health technology to study etiology, pathophysiology, course and trajectories of mental health problems.

Please note that inquiries regarding grant applications focused on the use of computational methods in real-world practice settings and communities, aiming to improve public health through research related to the late-stage efficacy and effectiveness of existing interventions and innovations for improving clinical practice (e.g., phase III/IV Clinical Trials) should be directed to the Division of Services and Interventions (DSIR).

Contact

Michele Ferrante, Ph.D.
Program Officer
Michele.Ferrante@nih.gov