What makes the psychosis 'clinical high risk' state risky: psychosis itself or the co-presence of a non-psychotic disorder?

AIMS: Although attenuated psychotic symptoms in the psychosis clinical high-risk state (CHR-P) almost always occur in the context of a non-psychotic disorder (NPD), NPD is considered an undesired 'comorbidity' epiphenomenon rather than an integral part of CHR-P itself. Prospective work, however, indicates that much more of the clinical psychosis incidence is attributable to prior mood and drug use disorders than to psychosis clinical high-risk states per se. In order to examine this conundrum, we analysed to what degree the 'risk' in CHR-P is indexed by co-present NPD rather than attenuated psychosis per se. METHODS: We examined the incidence of early psychotic experiences (PE) with and without NPD (mood disorders, anxiety disorders, alcohol/drug use disorders), in a prospective general population cohort (n = 6123 at risk of incident PE at baseline). Four interview waves were conducted between 2007 and 2018 (NEMESIS-2). The incidence of PE, alone (PE-only) or with NPD (PE + NPD) was calculated, as were differential associations with schizophrenia polygenic risk score (PRS-Sz), environmental, demographical, clinical and cognitive factors. RESULTS: The incidence of PE + NPD (0.37%) was lower than the incidence of PE-only (1.04%), representing around a third of the total yearly incidence of PE. Incident PE + NPD was, in comparison with PE-only, differentially characterised by poor functioning, environmental risks, PRS-Sz, positive family history, prescription of antipsychotic medication and (mental) health service use. CONCLUSIONS: The risk in 'clinical high risk' states is mediated not by attenuated psychosis per se but specifically the combination of attenuated psychosis and NPD. CHR-P/APS research should be reconceptualised from a focus on attenuated psychotic symptoms with exclusion of non-psychotic DSM-disorders, as the 'pure' representation of a supposedly homotypic psychosis risk state, towards a focus on poor-outcome NPDs, characterised by a degree of psychosis admixture, on the pathway to psychotic disorder outcomes.

Genetic and Environmental Influences on the Affective Regulation Network: A Prospective Experience Sampling Analysis.

Background: The study of networks of affective mental states that play a role in psychopathology may help model the influence of genetic and environmental risks. The aim of the present paper was to examine networks of affective mental states (AMS: "cheerful," "insecure," "relaxed," "anxious," "irritated," and "down") over time, stratified by genetic liability for psychopathology and exposure to environmental risk, using momentary assessment technology. Methods: Momentary AMS, collected using the experience sampling method (ESM) as well as childhood trauma and genetic liability (based on the level of shared genes and psychopathology in the co-twin) were collected in a population-based sample of female-female twin pairs and sisters (585 individuals). Networks were generated using multilevel time-lagged regression analysis, and regression coefficients were compared across three strata of childhood trauma severity and three strata of genetic liability using permutation testing. Regression coefficients were presented as network connections. Results: Visual inspection of network graphs revealed some suggestive changes in the networks with more exposure to either childhood trauma or genetic liability (i.e., stronger reinforcing loops between the three negative AMS anxious, insecure, and down both under higher early environmental, and under higher genetic liability exposure, stronger negative association between AMS of different valences: i.e., between "anxious" at t-1 and "relaxed" at t, "relaxed" at t-1 and "down" at t, under intermediate genetic liability exposure when compared to both networks under low and high genetic liability). Yet, statistical evaluation of differences across exposure strata was inconclusive. Conclusions: Although suggestive of a difference in the emotional dynamic, there was no conclusive evidence that genetic and environmental factors may impact ESM network models of individual AMS.

Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis.

Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions "cheerful," "insecure," "relaxed," "anxious," "irritated," and "down"-collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p < 0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing. Conclusions: The present findings demonstrate that the network approach may have some value in understanding the relation between established risk factors for mental disorders (particularly GL) and the dynamic interplay between emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence.

Correction: An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis.

[This corrects the article DOI: 10.1371/journal.pone.0162811.].

An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis.

INTRODUCTION: Dynamic relationships between the symptoms of psychosis can be shown in individual networks of psychopathology. In a single patient, data collected with the Experience Sampling Method (ESM-a method to construct intensive time series of experience and context) can be used to study lagged associations between symptoms in relation to illness severity and pharmacological treatment. METHOD: The patient completed, over the course of 1 year, for 4 days per week, 10 daily assessments scheduled randomly between 10 minutes and 3 hours apart. Five a priori selected symptoms were analysed: 'hearing voices', 'down', 'relaxed', 'paranoia' and 'loss of control'. Regression analysis was performed including current level of one symptom as the dependent variable and all symptoms at the previous assessment (lag) as the independent variables. Resulting regression coefficients were printed in graphs representing a network of symptoms. Network graphs were generated for different levels of severity: stable, impending relapse and full relapse. RESULTS: ESM data showed that symptoms varied intensely from moment to moment. Network representations showed meaningful relations between symptoms, e.g. 'down' and 'paranoia' fuelling each other, and 'paranoia' negatively impacting 'relaxed'. During relapse, symptom levels as well as the level of clustering between symptoms markedly increased, indicating qualitative changes in the network. While 'hearing voices' was the most prominent symptom subjectively, the data suggested that a strategic focus on 'paranoia', as the most central symptom, had the potential to bring about changes affecting the whole network. CONCLUSION: Construction of intensive ESM time series in a single patient is feasible and informative, particularly if represented as a network, showing both quantitative and qualitative changes as a function of relapse.

Use of the experience sampling method in the context of clinical trials.

OBJECTIVE: The experience sampling method (ESM) is a structured diary technique to appraise subjective experiences in daily life. It is applied in psychiatric patients, as well as in patients with somatic illness. Despite the potential of ESM assessment, the improved logistics and its increased administration in research, its use in clinical trials remains limited. This paper introduces ESM for clinical trials in psychiatry and beyond. METHODS: ESM is an ecologically valid method that yields a comprehensive view of an individual's daily life. It allows the assessment of various constructs (eg, quality of life, psychopathology) and psychological mechanisms (eg, stress-sensitivity, coping). These constructs are difficult to assess using cross-sectional questionnaires. ESM can be applied in treatment monitoring, as an ecological momentary intervention, in clinical trials, or in single case clinical trials. Technological advances (eg, smartphone applications) make its implementation easier. RESULTS: Advantages of ESM are highlighted and disadvantages are discussed. Furthermore, the ecological nature of ESM data and its consequences are explored, including the potential pitfalls of ambiguously formulated research questions and the specificities of ESM in statistical analyses. The last section focuses on ESM in relation to clinical trials and discusses its future use in optimising clinical decision-making. CONCLUSIONS: ESM can be a valuable asset in clinical trial research and should be used more often to study the benefits of treatment in psychiatry and somatic health.