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Transforming the understanding
and treatment of mental illnesses.

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Computational Models for Validating Dimensional Approaches to Psychopathology


Michele Ferrante, Ph.D.
Division of Translational Research, and Division of Neuroscience and Basic Behavioral Science


The primary goal of this initiative is to apply sophisticated computational (theory- and data-driven) approaches to research on the etiology and pathophysiology of psychiatric disorders that utilizes a dimensional framework (e.g., Research Domain Criteria (RDoC)). A secondary goal is to promote collaborations between computational and clinical scientists to advance translational research that leads to more effective and timely interventions for serious mental illness. Computational models and experimental approaches would be used to validate constructs in the NIMH RDoC matrix (or similar constructs based on comparable criteria) and to better understand heterogeneity and differential treatment response across psychiatric disorders.


Over the last 30 years, computational approaches have been successfully applied to basic neuroscience and contributed to significant advances in understanding complex brain functions at the molecular, cellular, systems and behavioral level. This foundational knowledge now provides a platform for the integration of computational modeling into clinical research on psychiatric disorders. Computational approaches can advance translational research and treatment by providing better nosological classifications of heterogenous patient populations; mechanistic explanations for newly identified neuro-behavioral types (biotypes); more precise treatment outcome predictions; and better stratification using multiple levels of data collected on individual subjects. To better understand the physiological and psychological basis of psychopathology in terms of domains of function that do not solely rely on Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnoses, the initiative would focus on utilizing an RDoC framework and sophisticated computational approaches to understand pathophysiology in clinical populations. We are interested in:

  • Fostering formal collaborations between clinical researchers and computational neuroscientists
  • Including at least:
    • Two levels of analysis (e.g., high resolution measurements of neural circuits and behavior activity using ecological momentary assessment and active and passive methods of data collection)
    • Three converging behavioral paradigms probing each RDoC dimensional construct of interest to get convergent validity for the measures and to test whether dimensional constructs can be identified, refined, merged, subdivided, hierarchically organized, and/or depend on each other through convergent mechanisms (e.g., circuits involved in negative and positive valence)
  • Utilizing accelerated longitudinal studies and predictive algorithms (e.g., focusing on childhood and adolescence to predict optimal neurodevelopmental stage for interventions)
  • Appropriately including healthy subjects and patients from multiple diagnostic groups
  • Studies applying modeling approaches and secondary analysis to large-N datasets (and combining multiple datasets) would also be encouraged.