Anomalies of Imagination, Self-Disorders, and Schizophrenia Spectrum Psychopathology: A Network Analysis

BackgroundAnomalies of imagination encompass disturbances of the basic experiential structure of fantasies and imagery that can be explored in a semi-structured way with the Examination of Anomalous Fantasy and Imagination (EAFI). We aimed (1) to examine the distribution of anomalies of imagination among different diagnostic groups and a group of healthy controls, and (2) to examine their relation with disorders of basic self, perceptual disturbances and canonical state psychopathology of the schizophrenia-spectrum (positive, negative and general symptoms).MethodsThe 81 participants included patients with schizophrenia or other non-affective psychosis (N = 32), schizotypal personality disorder (N = 15) or other mental illness (N = 16) and healthy controls (N = 18). The assessment encompassed EAFI, Examination of Anomalous Self-Experience (EASE), parts of Bonn Scale for the Assessment of Basic Symptoms (BSABS) and Positive and Negative Syndrome Scale (PANSS). For network analysis, the associations of EAFI with the other psychopathological variables were tested by Pearson's correlation coefficient and graphically represented using multidimensional clustering. Comparisons between correlations in the network were tested with Steiger's test.ResultsAnomalies of imagination aggregated significantly in schizophrenia-spectrum disorders compared to other mental illness and healthy controls with no difference between schizophrenia and schizotypal disorder. In the network analysis, anomalies of imagination were closely inter-connected with self-disorders. Although, the anomalies of imagination correlated moderately with perceptual disturbance and positive, negative and general state symptomatology, these dimensions aggregated separately and relatively distant in the network.ConclusionsThe results support that anomalies of imagination are highly characteristic of schizophrenia-spectrum disorders and closely related to self-disorders.

Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.

COVID-19-Related Mortality Risk in People With Severe Mental Illness: A Systematic and Critical Review

Background: Increasing clinical evidence suggests that people with severe mental illness (SMI), including schizophrenia spectrum disorders, bipolar disorder (BD), and major depressive disorder (MDD), are at higher risk of dying from COVID-19. Several systematic reviews examining the association between psychiatric disorders and COVID-19-related mortality have recently been published. Although these reviews have been conducted thoroughly, certain methodological limitations may hinder the accuracy of their research findings.Methods: A systematic literature search, using the PubMed, Embase, Web of Science, and Scopus databases (from inception to July 23, 2021), was conducted for observational studies assessing the risk of death associated with COVID-19 infection in adult patients with pre-existing schizophrenia spectrum disorders, BD, or MDD. Methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS).Results: Of 1,446 records screened, 13 articles investigating the rates of death in patients with pre-existing SMI were included in this systematic review. Quality assessment scores of the included studies ranged from moderate to high. Most results seem to indicate that patients with SMI, particularly patients with schizophrenia spectrum disorders, are at significantly higher risk of COVID-19-related mortality, as compared to patients without SMI. However, the extent of the variation in COVID-19-related mortality rates between studies including people with schizophrenia spectrum disorders was large because of a low level of precision of the estimated mortality outcome(s) in certain studies. Most studies on MDD and BD did not include specific information on the mood state or disease severity of patients. Due to a lack of data, it remains unknown to what extent patients with BD are at increased risk of COVID-19-related mortality. A variety of factors are likely to contribute to the increased mortality risk of COVID-19 in these patients. These include male sex, older age, somatic comorbidities (particularly cardiovascular diseases), as well as disease-specific characteristics.Conclusion: Methodological limitations hamper the accuracy of COVID-19-related mortality estimates for the main categories of SMIs. Nevertheless, evidence suggests that SMI is associated with excess COVID-19 mortality. Policy makers therefore must consider these vulnerable individuals as a high-risk group that should be given particular attention. This means that targeted interventions to maximize vaccination uptake among these patients are required to address the higher burden of COVID-19 infection in this already disadvantaged group.

Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders

A large body of research attributes learning deficits in schizophrenia (SZ) to the systems involved in value representation (prefrontal cortex, PFC) and reinforcement learning (basal ganglia, BG) as well as to the compromised connectivity of these regions. In this study, we employed learning tasks hypothesized to probe the function and interaction of the PFC and BG in patients with SZ-spectrum disorders in comparison to healthy control (HC) subjects. In the Instructed Probabilistic Selection task (IPST), participants received false instruction about one of the stimuli used in the course of probabilistic learning which creates confirmation bias, whereby the instructed stimulus is overvalued in comparison to its real experienced value. The IPST was administered to 102 patients with SZ and 120 HC subjects. We have shown that SZ patients and HC subjects were equally influenced by false instruction in reinforcement learning (RL) probabilistic task (IPST) (p-value = 0.441); however, HC subjects had significantly higher learning rates associated with the process of overcoming cognitive bias in comparison to SZ patients (p-value = 0.018). The behavioral results of our study could be hypothesized to provide further evidence for impairments in the SZ-BG circuitry; however, this should be verified by neurofunctional imaging studies.

Validation and recalibration of OxMIV in predicting violent behaviour in patients with schizophrenia spectrum disorders

Oxford Mental Illness and Violence (OxMIV) addresses the need in mental health services for a scalable, transparent and valid tool to predict violent behaviour in patients with severe mental illness. However, external validations are lacking. Therefore, we have used a Dutch sample of general psychiatric patients with schizophrenia spectrum disorders (N = 637) to evaluate the performance of OxMIV in predicting interpersonal violence over 3 years. The predictors and outcome were measured with standardized instruments and multiple sources of information. Patients were mostly male (n = 493, 77%) and, on average, 27 (SD = 7) years old. The outcome rate was 9% (n = 59). Discrimination, as measured by the area under the curve, was moderate at 0.67 (95% confidence interval 0.61–0.73). Calibration-in-the-large was adequate, with a ratio between predicted and observed events of 1.2 and a Brier score of 0.09. At the individual level, risks were systematically underestimated in the original model, which was remedied by recalibrating the intercept and slope of the model. Probability scores generated by the recalibrated model can be used as an adjunct to clinical decision-making in Dutch mental health services.