The effects of the COVID-19 outbreak and measures in patients with a pre-existing psychiatric diagnosis: A cross-sectional study

Background COVID-19 has seriously affected physical and mental health world-wide,both due to spreading of the virus and due to the socially restrictive measures most governments have enforced. Increased anxiety, stress and depressive symptoms have been widely reported in the general population. The current study investigated the effects of COVID and the restrictive measures in the Netherlands on stress, anxiety and loneliness in patients with a pre-existing psychiatric disorder. Methods 189 patients with a pre-existing psychiatric disorder treated at the University Medical Center Utrecht (UMCU) provided consent to participate in an electronically provided survey. Questionnaires on anxiety, depressive symptoms, worry, stress and general health were completed by 148 participants. Results All patients reported heightened distress as well as the presence of depressive symptoms and loneliness during the initial phase of the restrictive measures. Patients could be divided into two major subgroups with either psychotic disorder (n = 71) and affective disorder (n = 86). Patients with affective disorders were more affected by the outbreak and accompanying socially restrictive measures than patients with psychotic disorders. Conclusions Our findings indicate negative mental health effects of the global COVID-19 pandemic and the restrictive measures in a particularly vulnerable population, with differential effects on diagnostic groups

Revealing the impact of psychiatric comorbidities on treatment outcome in early psychosis using counterfactual model explanation

INTRODUCTION: Psychiatric comorbidities have a significant impact on the course of illness in patients with schizophrenia spectrum disorders. To accurately predict outcomes for individual patients using computerized prognostic models, it is essential to consider these comorbidities and their influence. METHODS: In our study, we utilized a multi-modal deep learning architecture to forecast symptomatic remission, focusing on a multicenter sample of patients with first-episode psychosis from the OPTiMiSE study. Additionally, we introduced a counterfactual model explanation technique to examine how scores on the Mini International Neuropsychiatric Interview (MINI) affected the likelihood of remission, both at the group level and for individual patients. RESULTS: Our findings at the group level revealed that most comorbidities had a negative association with remission. Among them, current and recurrent depressive disorders consistently exerted the greatest negative impact on the probability of remission across patients. However, we made an interesting observation: current suicidality within the past month and substance abuse within the past 12 months were associated with an increased chance of remission in patients. We found a high degree of variability among patients at the individual level. Through hierarchical clustering analysis, we identified two subgroups: one in which comorbidities had a relatively limited effect on remission (approximately 45% of patients), and another in which comorbidities more strongly influenced remission. By incorporating comorbidities into individualized prognostic prediction models, we determined which specific comorbidities had the greatest impact on remission at both the group level and for individual patients. DISCUSSION: These results highlight the importance of identifying and including relevant comorbidities in prediction models, providing valuable insights for improving the treatment and prognosis of patients with psychotic disorders. Furthermore, they open avenues for further research into the efficacy of treating these comorbidities to enhance overall patient outcomes

The role of depression in the prediction of a "late" remission in first-episode psychosis:An analysis of the OPTiMiSE study

Objective: The identification of predictors of psychosis remission could guide early clinical decision-making for treatment of first-episode schizophrenia (FES). Methods: We analyzed two non-independent subsamples of patients with FES ages 18-40 years from the OPTiMiSE study dataset to investigate the demographic and clinical factors that might help to differentiate "late" re-mitters (i.e., not in remission at week 2 or 4, but achieving remission within a 10-week follow-up period) from non-remitters within the same period. Results: Subsample 1 included 216 individuals (55 females, mean age 25.9 years) treated with amisulpride in an open-label design who were not in remission at week 2. Early symptomatic response between baseline and week 2 (odds ratio (OR)- 4.186, 95% confidence interval (CI)- 2.082-8.416, p < 0.001) and older age (OR- 1.081, 95% = CI 1.026-1.138, p- 0.003) were the only variables significantly associated with a higher probability of psychosis remission at week 4. Subsample 2 was composed of the 72 participants (19 females, mean age 25.1 years) who were not in remission at week 4 and completed a 6-week double-blind randomized trial comparing continuation of amisulpride with switch to olanzapine. Depression at baseline (as measured with the Calgary Depression Scale for Schizophrenia) was significantly associated with a nearly 3-fold lower likelihood of psychosis remission during the 10-week follow-up (hazard ratio = 2.865, 95% CI = 1.187-6.916, p = 0.019). Conclusion: Our results reinforce the importance of assessing depressive symptoms in people with FES and support the relevance of an early response (as early as 2 weeks) as a predictor of clinical outcome in this population. (C) 2021 Elsevier B.V. All rights reserved

Digital behavioural signatures reveal trans-diagnostic clusters of schizophrenia and Alzheimer's disease patients

The current neuropsychiatric nosological categories underlie pragmatic treatment choice, regulation and clinical research but does not encompass biological rationale. However, subgroups of patients suffering from schizophrenia or Alzheimer's disease have more in common than the neuropsychiatric nature of their condition, such as the expression of social dysfunction. The PRISM project presents here initial quantitative biological insights allowing the first steps toward a novel trans-diagnostic classification of psychiatric and neurological symptomatology intended to reinvigorate drug discovery in this area. In this study, we applied spectral clustering on digital behavioural endpoints derived from passive smartphone monitoring data in a subgroup of Schizophrenia and Alzheimer's disease patients, as well as age matched healthy controls, as part of the PRISM clinical study. This analysis provided an objective social functioning characterization with three differential clusters that transcended initial diagnostic classification and was shown to be linked to quantitative neurobiological parameters assessed. This emerging quantitative framework will both offer new ways to classify individuals in biologically homogenous clusters irrespective of their initial diagnosis, and also offer insights into the pathophysiological mechanisms underlying these clusters.</p

Effect of disease related biases on the subjective assessment of social functioning in Alzheimer's disease and schizophrenia patients

Background: Questionnaires are the current hallmark for quantifying social functioning in human clinical research. In this study, we compared self- and proxy-rated (caregiver and researcher) assessments of social functioning in Schizophrenia (SZ) and Alzheimer's disease (AD) patients and evaluated if the discrepancy between the two assessments is mediated by disease-related factors such as symptom severity. Methods: We selected five items from the WHO Disability Assessment Schedule 2.0 (WHODAS) to assess social functioning in 53 AD and 61 SZ patients. Caregiver- and researcher-rated assessments of social functioning were used to calculate the discrepancies between self-rated and proxy-rated assessments. Furthermore, we used the number of communication events via smartphones to compare the questionnaire outcomes with an objective measure of social behaviour. Results: WHODAS results revealed that both AD (p &lt; 0.001) and SZ (p &lt; 0.004) patients significantly overestimate their social functioning relative to the assessment of their caregivers and/or researchers. This overestimation is mediated by the severity of cognitive impairments (MMSE; p = 0.019) in AD, and negative symptoms (PANSS; p = 0.028) in SZ. Subsequently, we showed that the proxy scores correlated more strongly with the smartphone communication events of the patient when compared to the patient-rated questionnaire scores (self; p = 0.076, caregiver; p &lt; 0.001, researcher-rated; p = 0.046). Conclusion: Here we show that the observed overestimation of WHODAS social functioning scores in AD and SZ patients is partly driven by disease-related biases such as cognitive impairments and negative symptoms, respectively. Therefore, we postulate the development and implementation of objective measures of social functioning that may be less susceptible to such biases.The PRISM project (www.prism-project.eu) leading to this application has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115916. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This publication reflects only the authors’ views neither IMI JU nor EFPIA nor the European Commission are liable for any use that may be made of the information contained therein. Dr. Arango has also received funding support by the Spanish Ministry of Science and Innovation. Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds. Fundación Familia Alonso and Fundación Alicia Koplowit

The aperiodic exponent of neural activity varies with vigilance state in mice and men

Recently the 1/f signal of human electroencephalography has attracted attention, as it could potentially reveal a quantitative measure of neural excitation and inhibition in the brain, that may be relevant in a clinical setting. The purpose of this short article is to show that the 1/f signal depends on the vigilance state of the brain in both humans and mice. Therefore, proper labelling of the EEG signal is important as improper labelling may obscure diseaserelated changes in the 1/f signal. We demonstrate this by comparing EEG results from a longitudinal study in a genetic mouse model for synaptic dysfunction in schizophrenia and autism spectrum disorders to results from a large European cohort study with schizophrenia and mild Alzheimer's disease patients. The comparison shows when the 1/f is corrected for vigilance state there is a difference between groups, and this effect disappears when vigilance state is not corrected for. In conclusion, more attention should be paid to the vigilance state during analysis of EEG signals regardless of the species

Towards precision medicine in psychosis: Benefits and challenges of multimodal multicenter studies - PSYSCAN: translating neuroimaging findings from research into clinical practice

In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.The PSYSCAN Project is supported by grant agreement no. 603196 under the European Union’s Seventh Framework Programme. We would like to thank all participants who took part in the study. Conflict of Interest: S.G. received honoraria, advisory board, or consulting fees from the following companies: Gedeon-Richter, Janssen Pharmaceuticals, Janssen-Cilag Polska Sp. z o.o, Otsuka, Pierre Fabre and Sunovion Pharmarmaceuticals. B.G. is the leader of a Lundbeck Foundation Centre of Excellence for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), which is partially financed by an independent grant from the Lundbeck Foundation based on international review and partially financed by the Mental Health Services in the Capital Region of Denmark, the University of Copenhagen, and other foundations. Her group has also received a research grant from Lundbeck A/S for another independent investigator initiated study. All grants are the property of the Mental Health Services in the Capital Region of Denmark and administrated by them. G.S. is president of the Austrian Society of Neuropsychopharmacology and Biological Psychiatry, which is partially financed by the support from pharmaceutical companies. G.S. received consulting fees and/or honoraria for speeches within the last 3 years from Angelini, AOP Orphan, Alkermes, Janssen, Lundbeck, Pfizer. PFP received advisory board fees and research funds from Lundbeck

Cross-disorder and disorder-specific deficits in social functioning among schizophrenia and Alzheimer's disease patients

BACKGROUND: Social functioning is often impaired in schizophrenia (SZ) and Alzheimer's disease (AD). However, commonalities and differences in social dysfunction among these patient groups remain elusive.MATERIALS AND METHODS: Using data from the PRISM study, behavioral (all subscales and total score of the Social Functioning Scale) and affective (perceived social disability and loneliness) indicators of social functioning were measured in patients with SZ (N = 56), probable AD (N = 50) and age-matched healthy controls groups (HC, N = 29 and N = 28). We examined to what extent social functioning differed between disease and age-matched HC groups, as well as between patient groups. Furthermore, we examined how severity of disease and mood were correlated with social functioning, irrespective of diagnosis.RESULTS: As compared to HC, both behavioral and affective social functioning seemed impaired in SZ patients (Cohen's d's 0.81-1.69), whereas AD patients mainly showed impaired behavioral social function (Cohen's d's 0.65-1.14). While behavioral indices of social functioning were similar across patient groups, SZ patients reported more perceived social disability than AD patients (Cohen's d's 0.65). Across patient groups, positive mood, lower depression and anxiety levels were strong determinants of better social functioning (p's &lt;0.001), even more so than severity of disease.CONCLUSIONS: AD and SZ patients both exhibit poor social functioning in comparison to age- and sex matched HC participants. Social dysfunction in SZ patients may be more severe than in AD patients, though this may be due to underreporting by AD patients. Across patients, social functioning appeared as more influenced by mood states than by severity of disease.</p

Multivariable prediction of functional outcome after first-episode psychosis:a crossover validation approach in EUFEST and PSYSCAN

Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current &lt; 65) and good outcome (GAF current ≥ 65). Pooled non-linear support vector machine (SVM) classifiers trained on the separate cohorts identified patients with a poor outcome with cross-validated balanced accuracies (BAC) of 65-66%, but BAC dropped substantially when the models were applied to patients from a different FES cohort (BAC = 50-56%). A leave-site-out analysis on the merged sample yielded better performance (BAC = 72%), highlighting the effect of combining data from different study designs to overcome calibration issues and improve model transportability. In conclusion, our results indicate that validation of prediction models in an independent sample is essential in assessing the true value of the model. Future external validation studies, as well as attempts to harmonize data collection across studies, are recommended.</p

Relationships between social withdrawal and facial emotion recognition in neuropsychiatric disorders

Background: Emotion recognition constitutes a pivotal process of social cognition. It involves decoding social cues (e.g., facial expressions) to maximise social adjustment. Current theoretical models posit the relationship between social withdrawal factors (social disengagement, lack of social interactions and loneliness) and emotion decoding. Objective: To investigate the role of social withdrawal in patients with schizophrenia (SZ) or probable Alzheimer's disease (AD), neuropsychiatric conditions associated with social dysfunction. Methods: A sample of 156 participants was recruited: schizophrenia patients (SZ; n = 53), Alzheimer's disease patients (AD; n = 46), and two age-matched control groups (SZc, n = 29; ADc, n = 28). All participants provided self-report measures of loneliness and social functioning, and completed a facial emotion detection task. Results: Neuropsychiatric patients (both groups) showed poorer performance in detecting both positive and negative emotions compared with their healthy counterparts (p < .01). Social withdrawal was associated with higher accuracy in negative emotion detection, across all groups. Additionally, neuropsychiatric patients with higher social withdrawal showed lower positive emotion misclassification. Conclusions: Our findings help to detail the similarities and differences in social function and facial emotion recognition in two disorders rarely studied in parallel, AD and SZ. Transdiagnostic patterns in these results suggest that social withdrawal is associated with heightened sensitivity to negative emotion expressions, potentially reflecting hypervigilance to social threat. Across the neuropsychiatric groups specifically, this hypervigilance associated with social withdrawal extended to positive emotion expressions, an emotional-cognitive bias that may impact social functioning in people with severe mental illness