From Computation to Clinic

Sarah W. Yip, Deanna M. Barch, Henry W. Chase, Shelly Flagel, Quentin J.M. Huys, Anna B. Konova, Read Montague, Martin Paulus

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations

Abstract

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation—and on the best strategies to overcome these barriers—is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Original languageEnglish
Pages (from-to)319-328
Number of pages10
JournalBiological Psychiatry Global Open Science
Volume3
Issue number3
DOIs
StatePublished - Jul 2023

Keywords

  • Cognitive neuroscience
  • Computational psychiatry
  • Machine learning
  • Neuroimaging
  • Reinforcement learning

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