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      The dysconnection hypothesis (2016)

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          Abstract

          Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed – and how it has been enriched by recent advances in clinical neuroscience. In particular, we examine the dysconnection hypothesis in the context of (i) theoretical neurobiology and computational psychiatry; (ii) the empirical insights afforded by neuroimaging and associated connectomics – and (iii) how bottom-up (molecular biology and genetics) and top-down (systems biology) perspectives are converging on the mechanisms and nature of dysconnections in schizophrenia.

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          Most cited references 85

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          Abnormal neural oscillations and synchrony in schizophrenia.

          Converging evidence from electrophysiological, physiological and anatomical studies suggests that abnormalities in the synchronized oscillatory activity of neurons may have a central role in the pathophysiology of schizophrenia. Neural oscillations are a fundamental mechanism for the establishment of precise temporal relationships between neuronal responses that are in turn relevant for memory, perception and consciousness. In patients with schizophrenia, the synchronization of beta- and gamma-band activity is abnormal, suggesting a crucial role for dysfunctional oscillations in the generation of the cognitive deficits and other symptoms of the disorder. Dysfunctional oscillations may arise owing to anomalies in the brain's rhythm-generating networks of GABA (gamma-aminobutyric acid) interneurons and in cortico-cortical connections.
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            Psychosis as a state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia.

             Shitij Kapur (2002)
            The clinical hallmark of schizophrenia is psychosis. The objective of this overview is to link the neurobiology (brain), the phenomenological experience (mind), and pharmacological aspects of psychosis-in-schizophrenia into a unitary framework. Current ideas regarding the neurobiology and phenomenology of psychosis and schizophrenia, the role of dopamine, and the mechanism of action of antipsychotic medication were integrated to develop this framework. A central role of dopamine is to mediate the "salience" of environmental events and internal representations. It is proposed that a dysregulated, hyperdopaminergic state, at a "brain" level of description and analysis, leads to an aberrant assignment of salience to the elements of one's experience, at a "mind" level. Delusions are a cognitive effort by the patient to make sense of these aberrantly salient experiences, whereas hallucinations reflect a direct experience of the aberrant salience of internal representations. Antipsychotics "dampen the salience" of these abnormal experiences and by doing so permit the resolution of symptoms. The antipsychotics do not erase the symptoms but provide the platform for a process of psychological resolution. However, if antipsychotic treatment is stopped, the dysregulated neurochemistry returns, the dormant ideas and experiences become reinvested with aberrant salience, and a relapse occurs. The article provides a heuristic framework for linking the psychological and biological in psychosis. Predictions of this hypothesis, particularly regarding the possibility of synergy between psychological and pharmacological therapies, are presented. The author describes how the hypothesis is complementary to other ideas about psychosis and also discusses its limitations.
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              The Helmholtz machine.

              Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.
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                Author and article information

                Contributors
                Journal
                Schizophr Res
                Schizophr. Res
                Schizophrenia Research
                Elsevier Science Publisher B. V
                0920-9964
                1573-2509
                1 October 2016
                October 2016
                : 176
                : 2-3
                : 83-94
                Affiliations
                [a ]Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
                [b ]Oxford Centre for Human Brain Activity, University of Oxford, UK
                [c ]Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland
                [d ]Department of Psychiatry, Psychotherapy and Psychosomatics, Zurich, Switzerland
                Author notes
                [* ]Corresponding author at: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, London WC1N 3BG, UK.Wellcome Trust Centre for NeuroimagingInstitute of NeurologyUCLLondonWC1N 3BGUK k.friston@ 123456ucl.ac.uk
                Article
                S0920-9964(16)30331-0
                10.1016/j.schres.2016.07.014
                5147460
                27450778
                © 2016 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Neurology

                neurogenetics, predictive coding, bayesian, neuromodulation, dysconnection, schizophrenia

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