Cognitive remediation in schizophrenia-now it is really getting personal

Cognitive problems are consistently documented in people with schizophrenia. They negatively influence functioning and contribute to the long term consequences of the illness. Cognitive remediation (CR) is a psychological intervention developed to target these cognitive difficulties. There is evidence that CR is beneficial but there is still a limited understanding of how the putative active therapy ingredients contribute to changes in the brain and translate into improved functioning. This paper reviews recent research focused on topics that, in our view, will drive future developments such as the identification of translational mechanisms, the personalisation of CR, the best implementation methods and potential augmenting strategies to improve treatment effectiveness

Detecting non-Gaussian gravitational wave backgrounds: a unified framework

We describe a novel approach to the detection and parameter estimation of a non\textendash Gaussian stochastic background of gravitational waves. The method is based on the determination of relevant statistical parameters using importance sampling. We show that it is possible to improve the Gaussian detection statistics, by simulating realizations of the expected signal for a given model. While computationally expensive, our method improves the detection performance, leveraging the prior knowledge on the expected signal, and can be used in a natural way to extract physical information about the background. We present the basic principles of our approach, characterize the detection statistic performances in a simplified context and discuss possible applications to the detection of some astrophysical foregrounds. We argue that the proposed approach, complementarily to the ones available in literature might be used to detect suitable astrophysical foregrounds by currently operating and future gravitational wave detectors.Comment: 12 Pages, 4 Figures, Supplemental material (published on 24 March 2023

Improved detection statistics for non Gaussian gravitational wave stochastic backgrounds

In a recent paper we described a novel approach to the detection and parameter estimation of a non-Gaussian stochastic background of gravitational waves. In this work we propose an improved version of the detection procedure, preserving robustness against imperfect noise knowledge at no cost of detection performance: in the previous approach, the solution proposed to ensure robustness reduced the performances of the detection statistics, which in some cases (namely, mild non-Gaussianity) could be outperformed by Gaussian ones established in literature. We show, through a simple toy model, that the new detection statistic performs better than the previous one (and than the Gaussian statistic) everywhere in the parameter space. It approaches the optimal Neyman-Pearson statistics monotonically with increasing non-Gaussianity and/or number of detectors. In this study we discuss in detail its efficiency. This is a second, important step towards the implementation of a nearly--optimal detection procedure for a realistic non-Gaussian stochastic background. We discuss the relevance of results obtained in the context of the toy model used, and their importance for understanding a more realistic scenario.Comment: 12 pages, 5 figures (published on 23 June 2023

Using wearable technology to detect the autonomic signature of illness severity in schizophrenia

IntroductionResearch suggests that people with schizophrenia have autonomic dysfunctions. These have been linked to functioning problems, symptoms and considered a risk factor for illness chronicity. The aim of this study is to introduce a new Mobile Health (mHealth) method using wearable technology to assessing autonomic activity in people's everyday life. We aim to evaluate the new method acceptability and characterise the association between schizophrenia illness features and autonomic abnormalities.MethodThirty participants with schizophrenia and 25 controls were asked to wear a mHealth device measuring autonomic activity and movements during their normal everyday life. Measures of device use acceptability were collected from all participants. Participants with schizophrenia were also assessed for symptoms and functioning levels. Measures of heart rate variability (HRV), electrodermal activity (EDA) and movement were collected by the device and groups were compared. Correlation between physiological measures, functioning, symptoms and medication levels were assessed in people with schizophrenia.ResultsThe mHealth device method proved to be acceptable and produced reliable measures of autonomic activity and behaviour. Compared to controls, people with schizophrenia showed lower levels of HRV, movement and functioning. In people with schizophrenia illness severity, particularly positive symptoms, was associated with parasympathetic deregulation.ConclusionsAutonomic abnormalities can be detected using wearable technology from people's everyday life. These are in line with previous research and support the notion that autonomic deregulation are relevant illness features for mental and physical health in schizophrenia. This method may be developed as a monitoring system for well-being and relapse prevention

Blending Active and Passive Digital Technology Methods to Improve Symptom Monitoring in Early Psychosis

Aims: Psychotic symptoms fluctuate over time and effective and regular monitoring may contribute to relapse prevention and improve long-term outcomes. In this proof-of-concept study we test the feasibility, acceptability and potential usefulness of a novel digital method assessing the association between physiological signals and psychotic symptom distress.Methods: Fifteen participants with first episode psychosis were asked to use a self-assessment mobile phone application for psychotic symptom monitoring for 10 days while using a wrist worn device continuously recording heart rate variability (HRV) and electrodermal activity (EDA). We compared physiological activity when participants reported experiencing distressing and non-distressing psychotic symptoms.Results: Participants completed on average 76% of the mobile phone symptom assessments. When reporting distressing hallucinations and delusions participants had significantly higher EDA levels and non-significant lower HRV values compared to when these symptoms were non-distressing.Conclusions: This study provides further evidence linking psychotic symptom's distress, as experienced in everyday life, and autonomic deregulation. This proof-of-concept study may lead to further longer-term efforts to identify relapse biosignatures using automated methods based on passive monitoring. This method may allow for earlier interventions, contribute to improverelapse prevention and reduce symptoms interfering with recovery