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Realizing the Clinical Potential of Computational Psychiatry: Report From the Banbury Center Meeting, February 2019

Published:February 26, 2020DOI:https://doi.org/10.1016/j.biopsych.2019.12.026
      Computational psychiatry is an emerging field that examines phenomena in mental illness using formal techniques from computational neuroscience, mathematical psychology, and machine learning (
      • Huys Q.J.M.
      • Maia T.V.
      • Paulus M.P.
      Computational psychiatry: From mechanistic insights to the development of new treatments.
      ,
      • Montague P.R.
      • Dolan R.J.
      • Friston K.J.
      • Dayan P.
      Computational psychiatry.
      ,
      • Paulus M.P.
      • Huys Q.J.M.
      • Maia T.V.
      A roadmap for the development of applied computational psychiatry.
      ,
      • Stephan K.E.
      • Binder E.B.
      • Breakspear M.
      • Dayan P.
      • Johnstone E.C.
      • Meyer-Lindenberg A.
      • et al.
      Charting the landscape of priority problems in psychiatry, part 2: Pathogenesis and aetiology.
      ,
      • Wang X.J.
      • Krystal J.H.
      Computational psychiatry.
      ,
      • Kishida K.T.
      • King-Casas B.
      • Montague P.R.
      Neuroeconomic approaches to mental disorders.
      ). These techniques can be used in a theory-driven manner to gain insight into neural or cognitive processes and in a data-driven way to identify predictive and explanatory relationships in complex datasets. The approaches complement each other: theory-driven models can be used to infer mechanisms, and the resulting measurements can be used in data-driven approaches for prediction. Recent computational studies have successfully described and measured novel mechanisms in a range of disorders (
      • Browning M.
      • Behrens T.E.
      • Jocham G.
      • O’Reilly J.X.
      • Bishop S.J.
      Anxious individuals have difficulty learning the causal statistics of aversive environments.
      ,
      • Collins A.G.E.
      • Albrecht M.A.
      • Waltz J.A.
      • Gold J.M.
      • Frank M.J.
      Interactions among working memory, reinforcement learning, and effort in value-based choice: A new paradigm and selective deficits in schizophrenia.
      ,
      • Huys Q.J.M.
      • Pizzagalli D.A.
      • Bogdan R.
      • Dayan P.
      Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis.
      ,
      • Powers A.R.
      • Mathys C.
      • Corlett P.R.
      Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors.
      ,
      • Lawson R.P.
      • Mathys C.
      • Rees G.
      Adults with autism overestimate the volatility of the sensory environment.
      ), have framed disorders in new and informative ways (
      • Braver T.S.
      • Barch D.M.
      • Cohen J.D.
      Cognition and control in schizophrenia: A computational model of dopamine and prefrontal function.
      ), and have identified predictors of treatment response (
      • Chekroud A.M.
      • Zotti R.J.
      • Shehzad Z.
      • Gueorguieva R.
      • Johnson M.K.
      • Trivedi M.H.
      • et al.
      Cross-trial prediction of treatment outcome in depression: A machine learning approach.
      ,
      • Harlé K.M.
      • Stewart J.L.
      • Zhang S.
      • Tapert S.F.
      • Yu A.J.
      • Paulus M.P.
      Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use.
      ). These methods hold the potential to improve identification of relevant clinical variables and could be superior to classification based on traditional behavioral or neural data alone (
      • Wiecki T.V.
      • Poland J.
      • Frank M.J.
      Model-based cognitive neuroscience approaches to computational psychiatry clustering and classification.
      ,
      • Huys Q.J.M.
      • Maia T.V.
      • Frank M.J.
      Computational psychiatry as a bridge from neuroscience to clinical applications.
      ,
      • Wiecki T.V.
      • Antoniades C.A.
      • Stevenson A.
      • Kennard C.
      • Borowsky B.
      • Owen G.
      • et al.
      A computational cognitive biomarker for early-stage Huntington’s disease.
      ,
      • Brodersen K.H.
      • Schofield T.M.
      • Leff A.P.
      • Ong C.S.
      • Lomakina E.I.
      • Buhmann J.M.
      • Stephan K.E.
      Generative embedding for model-based classification of fMRI data.
      ). However, these promising results have been slow to influence clinical practice or to improve patient outcomes.
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      References

        • Huys Q.J.M.
        • Maia T.V.
        • Paulus M.P.
        Computational psychiatry: From mechanistic insights to the development of new treatments.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1: 382-385
        • Montague P.R.
        • Dolan R.J.
        • Friston K.J.
        • Dayan P.
        Computational psychiatry.
        Trends Cogn Sci. 2012; 16: 72-80
        • Paulus M.P.
        • Huys Q.J.M.
        • Maia T.V.
        A roadmap for the development of applied computational psychiatry.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1: 386-392
        • Stephan K.E.
        • Binder E.B.
        • Breakspear M.
        • Dayan P.
        • Johnstone E.C.
        • Meyer-Lindenberg A.
        • et al.
        Charting the landscape of priority problems in psychiatry, part 2: Pathogenesis and aetiology.
        Lancet Psychiatry. 2016; 3: 84-90
        • Wang X.J.
        • Krystal J.H.
        Computational psychiatry.
        Neuron. 2014; 84: 638-654
        • Kishida K.T.
        • King-Casas B.
        • Montague P.R.
        Neuroeconomic approaches to mental disorders.
        Neuron. 2010; 67: 543-554
        • Browning M.
        • Behrens T.E.
        • Jocham G.
        • O’Reilly J.X.
        • Bishop S.J.
        Anxious individuals have difficulty learning the causal statistics of aversive environments.
        Nat Neurosci. 2015; 18: 590-596
        • Collins A.G.E.
        • Albrecht M.A.
        • Waltz J.A.
        • Gold J.M.
        • Frank M.J.
        Interactions among working memory, reinforcement learning, and effort in value-based choice: A new paradigm and selective deficits in schizophrenia.
        Biol Psychiatry. 2017; 82: 431-439
        • Huys Q.J.M.
        • Pizzagalli D.A.
        • Bogdan R.
        • Dayan P.
        Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis.
        Biol Mood Anxiety Disord. 2013; 3: 12
        • Powers A.R.
        • Mathys C.
        • Corlett P.R.
        Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors.
        Science. 2017; 357: 596-600
        • Lawson R.P.
        • Mathys C.
        • Rees G.
        Adults with autism overestimate the volatility of the sensory environment.
        Nat Neurosci. 2017; 20: 1293-1299
        • Braver T.S.
        • Barch D.M.
        • Cohen J.D.
        Cognition and control in schizophrenia: A computational model of dopamine and prefrontal function.
        Biol Psychiatry. 1999; 46: 312-328
        • Chekroud A.M.
        • Zotti R.J.
        • Shehzad Z.
        • Gueorguieva R.
        • Johnson M.K.
        • Trivedi M.H.
        • et al.
        Cross-trial prediction of treatment outcome in depression: A machine learning approach.
        Lancet Psychiatry. 2016; 3: 243-250
        • Harlé K.M.
        • Stewart J.L.
        • Zhang S.
        • Tapert S.F.
        • Yu A.J.
        • Paulus M.P.
        Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use.
        Brain. 2015; 138: 3413-3426
        • Wiecki T.V.
        • Poland J.
        • Frank M.J.
        Model-based cognitive neuroscience approaches to computational psychiatry clustering and classification.
        Clin Psychol Sci. 2015; 3: 378-399
        • Huys Q.J.M.
        • Maia T.V.
        • Frank M.J.
        Computational psychiatry as a bridge from neuroscience to clinical applications.
        Nat Neurosci. 2016; 19: 404-413
        • Wiecki T.V.
        • Antoniades C.A.
        • Stevenson A.
        • Kennard C.
        • Borowsky B.
        • Owen G.
        • et al.
        A computational cognitive biomarker for early-stage Huntington’s disease.
        PLoS One. 2016; 11e0148409
        • Brodersen K.H.
        • Schofield T.M.
        • Leff A.P.
        • Ong C.S.
        • Lomakina E.I.
        • Buhmann J.M.
        • Stephan K.E.
        Generative embedding for model-based classification of fMRI data.
        PLoS Comput Biol. 2011; 7e1002079
        • Behrens T.E.J.
        • Woolrich M.W.
        • Walton M.E.
        • Rushworth M.F.S.
        Learning the value of information in an uncertain world.
        Nat Neurosci. 2007; 10: 1214-1221
        • Pulcu E.
        • Browning M.
        Affective bias as a rational response to the statistics of rewards and punishments [published correction appears in Elife 2017; 6:e32902.
        Elife. 2017; 6e27879
        • Insel T.
        • Cuthbert B.
        • Garvey M.
        • Heinssen R.
        • Pine D.S.
        • Quinn K.
        • et al.
        Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders.
        Am J Psychiatry. 2010; 167: 748-751
        • Hedge C.
        • Powell G.
        • Bompas A.
        • Vivian-Griffiths S.
        • Sumner P.
        Low and variable correlation between reaction time costs and accuracy costs explained by accumulation models: Meta-analysis and simulations.
        Psychol Bull. 2018; 144: 1200-1227
        • Price R.B.
        • Brown V.
        • Siegle G.J.
        Computational modeling applied to the dot-probe task yields improved reliability and mechanistic insights.
        Biol Psychiatry. 2019; 85: 606-612
        • Kessels R.P.C.
        Improving precision in neuropsychological assessment: Bridging the gap between classic paper-and-pencil tests and paradigms from cognitive neuroscience.
        Clin Neuropsychol. 2019; 33: 357-368
        • Moutoussis M.
        • Bullmore E.T.
        • Goodyer I.M.
        • Fonagy P.
        • Jones P.B.
        • Dolan R.J.
        • et al.
        Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood.
        PLoS Comput Biol. 2018; 14e1006679
        • Shahar N.
        • Hauser T.U.
        • Moutoussis M.
        • Moran R.
        • Keramati M.
        • consortium N.S.P.N.
        • Dolan R.J.
        Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling.
        PLoS Comput Biol. 2019; 15e1006803
        • Hedge C.
        • Powell G.
        • Sumner P.
        The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences.
        Behav Res Methods. 2018; 50: 1166-1186
        • Enkavi A.Z.
        • Eisenberg I.W.
        • Bissett P.G.
        • Mazza G.L.
        • MacKinnon D.P.
        • Marsch L.A.
        • Poldrack R.A.
        Large-scale analysis of test-retest reliabilities of self-regulation measures.
        Proc Natl Acad Sci U S A. 2019; 116: 5472-5477
        • Maia T.V.
        • Huys Q.J.M.
        • Frank M.J.
        Theory-based computational psychiatry.
        Biol Psychiatry. 2017; 82: 382-384
        • Kurth-Nelson Z.
        • O’Doherty J.P.
        • Barch D.M.
        • Denève S.
        • Durstewitz D.
        • Frank M.J.
        • et al.
        Computational approaches for studying mechanisms of psychiatric disorders.
        in: Redish D. Gordon J.A. Computational Psychiatry. MIT Press, Cambridge, MA2016
        • Gillan C.M.
        • Daw N.D.
        Taking psychiatry research online.
        Neuron. 2016; 91: 19-23
        • Poldrack R.A.
        • Yarkoni T.
        From brain maps to cognitive ontologies: Informatics and the search for mental structure.
        Annu Rev Psychol. 2016; 67: 587-612
        • Clarke P.
        • MacLeod C.M.
        • Shirazee N.
        Prepared for the worst: Readiness to acquire threat bias and susceptibility to elevate trait anxiety.
        Emotion. 2008; 8: 47-57
        • Calhoun V.D.
        • Lawrie S.M.
        • Mourao-Miranda J.
        • Stephan K.E.
        Prediction of individual differences from neuroimaging data.
        Neuroimage. 2017; 145: 135-136
        • Marinescu I.E.
        • Lawlor P.N.
        • Kording K.P.
        Quasi-experimental causality in neuroscience and behavioural research.
        Nat Hum Behav. 2018; 2: 891-898
        • Etkin A.
        • Patenaude B.
        • Song Y.J.C.
        • Usherwood T.
        • Rekshan W.
        • Schatzberg A.F.
        • et al.
        A cognitive-emotional biomarker for predicting remission with antidepressant medications: A report from the iSPOT-D trial.
        Neuropsychopharmacology. 2015; 40: 1332-1342
        • Trivedi M.H.
        • McGrath P.J.
        • Fava M.
        • Parsey R.V.
        • Kurian B.T.
        • Phillips M.L.
        • et al.
        Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design.
        J Psychiatr Res. 2016; 78: 11-23
        • Auchter A.M.
        • Hernandez Mejia M.
        • Heyser C.J.
        • Shilling P.D.
        • Jernigan T.L.
        • Brown S.A.
        • et al.
        A description of the ABCD organizational structure and communication framework.
        Dev Cogn Neurosci. 2018; 32: 8-15
        • Gorgolewski K.J.
        • Alfaro-Almagro F.
        • Auer T.
        • Bellec P.
        • Capotă M.
        • Chakravarty M.M.
        • et al.
        BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.
        PLoS Comput Biol. 2017; 13e1005209
        • Smucny J.
        • Barch D.M.
        • Gold J.M.
        • Strauss M.E.
        • MacDonald A.W.
        • Boudewyn M.A.
        • et al.
        Cross-diagnostic analysis of cognitive control in mental illness: Insights from the CNTRACS consortium.
        Schizophr Res. 2019; 208: 377-383
        • Wilson R.C.
        • Collins A.
        Ten simple rules for the computational modeling of behavioral data.
        Elife. 2019; 8e49547