title: Computational approaches to schizophrenia: A perspective on negative symptoms.
abstract: Schizophrenia is a heterogeneous spectrum disorder often associated with detrimental negative symptoms. In recent years, computational approaches to psychiatry have attracted growing attention. Negative symptoms have shown some overlap with general cognitive impairments and were also linked to impaired motivational processing in brain circuits implementing reward prediction. In this review, we outline how computational approaches may help to provide a better understanding of negative symptoms in terms of the potentially underlying behavioural and biological mechanisms. First, we describe the idea that negative symptoms could arise from a failure to represent reward expectations to enable flexible behavioural adaptation. It has been proposed that these impairments arise from a failure to use prediction errors to update expectations. Important previous studies focused on processing of so-called model-free prediction errors where learning is determined by past rewards only. However, learning and decision-making arise from multiple cognitive mechanisms functioning simultaneously, and dissecting them via well-designed tasks in conjunction with computational modelling is a promising avenue. Second, we move on to a proof-of-concept example on how generative models of functional imaging data from a cognitive task enable the identification of subgroups of patients mapping on different levels of negative symptoms. Combining the latter approach with behavioural studies regarding learning and decision-making may allow the identification of key behavioural and biological parameters distinctive for different dimensions of negative symptoms versus a general cognitive impairment. We conclude with an outlook on how this computational framework could, at some point, enrich future clinical studies.
authors.affiliation: Max Planck Fellow Group 'Cognitive and Affective Control of Behavioral Adaptation', Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany; Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University of Leipzig, Leipzig, Germany. Electronic address: deserno@cbs.mpg.de.
authors.forename: Lorenz
authors.initials: L
authors.lastname: Deserno
authors.affiliation: Max Planck Fellow Group 'Cognitive and Affective Control of Behavioral Adaptation', Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Heinz
authors.affiliation: Max Planck Fellow Group 'Cognitive and Affective Control of Behavioral Adaptation', Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Schlagenhauf
mesh_terms: D003072:Cognition Disorders / Q000209:etiology*; D003198:Computer Simulation; D006801:Humans; D001523:Mental Disorders / Q000209:etiology; D008959:Models, Neurological; D009042:Motivation; D012559:Schizophrenia / Q000150:complications / Q000175:diagnosis / Q000628:therapy; D012565:Schizophrenic Psychology
keywords: Computational modelling; Computational psychiatry; Decision-making; Negative symptoms; Reinforcement learning; Schizophrenia