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      Computational Psychiatry: towards a mathematically informed understanding of mental illness

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          Abstract

          Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency (‘helplessness’), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods.

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          Most cited references126

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          Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

          We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
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            Rethinking Rumination.

            The response styles theory (Nolen-Hoeksema, 1991) was proposed to explain the insidious relationship between rumination and depression. We review the aspects of the response styles theory that have been well-supported, including evidence that rumination exacerbates depression, enhances negative thinking, impairs problem solving, interferes with instrumental behavior, and erodes social support. Next, we address contradictory and new findings. Specifically, rumination appears to more consistently predict the onset of depression rather than the duration, but rumination interacts with negative cognitive styles to predict the duration of depressive symptoms. Contrary to original predictions, the use of positive distractions has not consistently been correlated with lower levels of depressive symptoms in correlational studies, although dozens of experimental studies show positive distractions relieve depressed mood. Further, evidence now suggests that rumination is associated with psychopathologies in addition to depression, including anxiety, binge eating, binge drinking, and self-harm. We discuss the relationships between rumination and worry and between rumination and other coping or emotion-regulation strategies. Finally, we highlight recent research on the distinction between rumination and more adaptive forms of self-reflection, on basic cognitive deficits or biases in rumination, on its neural and genetic correlates, and on possible interventions to combat rumination.
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              The need for a new medical model: a challenge for biomedicine.

              The dominant model of disease today is biomedical, and it leaves no room within tis framework for the social, psychological, and behavioral dimensions of illness. A biopsychosocial model is proposed that provides a blueprint for research, a framework for teaching, and a design for action in the real world of health care.
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                Author and article information

                Journal
                J Neurol Neurosurg Psychiatry
                J. Neurol. Neurosurg. Psychiatr
                jnnp
                jnnp
                Journal of Neurology, Neurosurgery, and Psychiatry
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                0022-3050
                1468-330X
                January 2016
                8 July 2015
                : 87
                : 1
                : 53-63
                Affiliations
                [1 ]Institute of Cognitive Neuroscience, University College London , London, UK
                [2 ]Division of Psychiatry, University College London , London, UK
                [3 ]Translational Neuromodeling Unit, University of Zürich and Swiss Federal Institute of Technology, Zürich , Zürich, Switzerland
                [4 ]Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zürich , Zürich, Switzerland
                Author notes
                [Correspondence to ] Dr Rick A Adams, Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3BG, UK; rick.adams@ 123456ucl.ac.uk
                Author information
                http://orcid.org/0000-0002-7661-8881
                Article
                jnnp-2015-310737
                10.1136/jnnp-2015-310737
                4717449
                26157034
                c17bb10a-c697-49de-8ef2-393fa9898102
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

                History
                : 2 March 2015
                : 29 May 2015
                : 19 June 2015
                Categories
                1506
                1272
                Neuropsychiatry
                Review
                Custom metadata
                unlocked
                patients-choice

                Surgery
                schizophrenia,depression,psychiatry,cognition,psychopharmacology
                Surgery
                schizophrenia, depression, psychiatry, cognition, psychopharmacology

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