Assessing cognitive insight in nonpsychiatric individuals and outpatients with schizophrenia in Taiwan: an investigation using the Beck Cognitive Insight Scale

<p>Abstract</p> <p>Background</p> <p>The Beck Cognitive Insight Scale (BCIS) was designed for the assessment of the cognitive processes involved in self-reflection and the ability to modify erroneous beliefs and misinterpretations. Studies investigating the factor structure of the BCIS have indicated a two-factor model in the psychotic population. The factor structure of the BCIS, however, has not received much consideration in the nonpsychiatric population. The present study examined the factor structure and validity of the BCIS and compared its scores between nonpsychiatric individuals and outpatients with psychosis.</p> <p>Method</p> <p>The Taiwanese version of the BCIS was administered to 507 nonpsychiatric individuals and 118 outpatients with schizophrenia. The psychometric properties of the BCIS were examined through the following analyses: exploratory and confirmatory factor analyses, reliability, correlation analyses, and discriminative validity.</p> <p>Results</p> <p>The BCIS showed adequate internal consistency and stability over time. Exploratory and confirmatory factor analyses on the 15-item measure indicated a two-factor solution that supported the two dimensions of the Taiwanese BCIS, which was also observed with the original BCIS. Following the construct validation, we obtained a composite index (self-reflectiveness minus self-certainty) of the Taiwanese BCIS that reflected cognitive insight. Consistent with previous studies, our results indicated that psychosis is associated with low self-reflectiveness and high self-certainty, which possibly reflect lower cognitive insight. Our results also showed that better cognitive insight is related to worse depression in patients with schizophrenia spectrum disorders, but not in nonpsychiatric individuals. The receiver operating characteristic (ROC) analyses revealed that the area under the curve (AUC) was 0.731. A composite index of 3 was a good limit, with a sensitivity of 87% and a specificity of 51%.</p> <p>Conclusion</p> <p>The BCIS proved to be useful for measuring cognitive insight in Taiwanese nonpsychiatric and psychotic populations.</p

Nowhere Home: The Waiting of Vulnerable Child Refugees

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Repetitive Behavior in Rubinstein–Taybi Syndrome:Parallels with Autism Spectrum Phenomenology

Syndrome specific repetitive behavior profiles have been described previously. A detailed profile is absent for Rubinstein–Taybi syndrome (RTS). The Repetitive Behaviour Questionnaire and Social Communication Questionnaire were completed for children and adults with RTS (N = 87), Fragile-X (N = 196) and Down (N = 132) syndromes, and individuals reaching cut-off for autism spectrum disorder (N = 228). Total and matched group analyses were conducted. A phenotypic profile of repetitive behavior was found in RTS. The majority of behaviors in RTS were not associated with social-communication deficits or degree of disability. Repetitive behavior should be studied at a fine-grained level. A dissociation of the triad of impairments might be evident in RTS

DSM-5: a collection of psychiatrist views on the changes, controversies, and future directions

The recent release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) by the American Psychiatric Association has led to much debate. For this forum article, we asked BMC Medicine Editorial Board members who are experts in the field of psychiatry to discuss their personal views on how the changes in DSM-5 might affect clinical practice in their specific areas of psychiatric medicine. This article discusses the influence the DSM-5 may have on the diagnosis and treatment of autism, trauma-related and stressor-related disorders, obsessive-compulsive and related disorders, mood disorders (including major depression and bipolar disorders), and schizophrenia spectrum disorders

Comprehensive review:Computational modelling of Schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence.Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated