Computational Psychiatry & Decision-making

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  • doi pdf Computational psychiatry: from mechanistic insights to the development of new treatments
  • Huys QJM, Maia T and Paulus MP
  • Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2016) 1(5):382-385
  • Computational psychiatry is a young field that aims to further our understanding of mental illness and its treatment with the use of novel computational techniques. The present issue provides an overview over the breadth of the field. On the one hand, computational techniques can be used to provide mechanistic insight into illnesses. This is exemplified with contributions using Bayesian and reinforcement-learning techniques into schizophrenia, methamphetamine and alcohol use disorders. On the other hand, mechanistically agnostic techniques can directly infer information relevant to treatment. Examples in the issue include prediction of depression treatment responses with EEG and response prediction with fMRI. The issue concludes with a novel way to address heterogeneity, and finally with a proposal to adopt a developmental pathway akin to that in drug development to ensure computational psychiatry fulfills its promise to improve patient outcomes.