Literature DB >> 27137974

Cognitive and neural models of threat appraisal in psychosis: A theoretical integration.

Raphael Underwood1, Veena Kumari2, Emmanuelle Peters3.   

Abstract

Cognitive models of psychosis propose that maladaptive appraisals of anomalous experiences contribute to distress and disability in psychosis. Attentional, attributional and reasoning biases are hypothesised to drive these threat-based appraisals. Experimental and self-report data have provided support for the presence of these biases in psychosis populations, but recently there have been calls for neurobiological data to be integrated into these findings. Currently, little investigation has been conducted into the neural correlates of maladaptive appraisals. Experimental and neuroimaging research in social cognition employing threatening stimuli provide the closest equivalent of maladaptive appraisal in psychosis. Consequently, a rapprochement of these two literatures was attempted in order to identify neural networks relevant to threat appraisal in psychosis. This revealed overlapping models of aberrant emotion processing in anxiety and schizophrenia, encompassing the amygdala, insula, hippocampus, anterior cingulate cortex, and prefrontal cortex. These models posit that aberrant activity in these systems relates to altered emotional significance detection and affect regulation, providing a conceptual overlap with threat appraisal in psychosis, specifically attentional and attributional biases towards threat. It remains to be seen if direct examination of these biases using neuroimaging paradigms supports the theoretical integration of extant models of emotion processing and maladaptive appraisals in psychosis.
Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Entities:  

Keywords:  Cognition; Neuroimaging; Psychosis

Mesh:

Year:  2016        PMID: 27137974     DOI: 10.1016/j.psychres.2016.03.016

Source DB:  PubMed          Journal:  Psychiatry Res        ISSN: 0165-1781            Impact factor:   3.222


  8 in total

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  8 in total

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