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Transforming the understanding
and treatment of mental illnesses.

Computational Psychiatry Program


The overarching goal of this program is to foster a novel biologically-based computational framework to identify and validate biomarkers and novel treatment targets relevant to the prevention, treatment, and recovery of psychiatric disorders. The program supports translational research utilizing computational models for validating RDoC constructs in the clinic. The program is interested in analytical approaches for the prediction of risk and treatment response and the understanding of the pathophysiology underlying mental disorders. Research projects combining mathematical and computational tools with neurophysiological, neuroanatomical, neurochemical, and/or neuroimaging techniques are encouraged in order to decipher the function of biological mechanisms implicated in mental disorders.

Areas of Emphasis

  • Models testing personalized treatments for reducing the severity and incidence of psychopathology by using biological markers (e.g., genomic, proteomic, and imaging)
  • Translating basic computational models into clinical research by informing the models with experimental data from clinical population
  • Models predicting treatment response, relapse, and/or vulnerability to side effects of therapeutic interventions (including psychotropic medications, neuromodulation devices, and behavioral) to prevent or ameliorate treatment-emergent side effects
  • Biologically realistic models of brain activity from molecules, cells, systems, to behavior to identify psychopathologi­cal phenotypes as new targets for therapeutic approaches
  • Understanding how individual differences in neuronal activity contribute to the transmission and processing of pathophysiological information in the central nervous system
  • Applying novel analytical and statistical approaches to biological data sets to identify patterns and relationships among variables that contribute to dysfunctional behavior
  • Biologically valid computational models to understand the efficacy (and/or failure) of psychopharmacology, biologics, neuromodulation, cognitive, and/or psychosocial clinical interventions
  • 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


Michele Ferrante, Ph.D.
Program Chief
6001 Executive Boulevard, Room 7202, MSC 9637