Predictors of study drop-out and service disengagement in patients at clinical high risk for psychosis.

PURPOSE: Study drop-out during follow-up and service disengagement frequently occur in patients at clinical high risk for psychosis (CHR-P). However, little is known about their predictors. Therefore, we aimed to analyze the rate and reasons for drop-out and service disengagement in CHR-P patients and investigate their sociodemographic and clinical predictors. METHODS: Data from 200 patients of the prospective Früherkennung von Psychosen (FePsy) study were analyzed with competing risks survival models, considering drop-out and transition to psychosis as competing events. To investigate whether symptoms changed immediately before drop-out, t tests were applied. RESULTS: Thirty-six percent of patients dropped out within 5 years. Almost all drop-outs also disengaged from our service. Hence, study drop-out was used as a proxy for service disengagement. Patients with more severe baseline disorganized symptoms and a late inclusion into the study were significantly more likely to disengage. Immediately before disengagement, there was significant improvement in negative symptoms only. CONCLUSION: A considerable proportion of CHR-P patients disengaged from our clinical study and service. Patients who were included during a later study period with more assessments disengaged more often, which might have been due to more frequent invitations to follow-up assessments and thereby increasing participation burden. Hence, our study provides a cautionary note on high-frequency follow-up assessments. Larger-scale studies evaluating predictors on multiple domains would help to further elucidate drop-out and disengagement.

Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis.

Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that overly precise prediction errors lead to an unstable model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for such an unstable model of the world and investigate the computational mechanisms underlying emerging paranoia. We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task designed to probe learning about others' changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility. There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between perceiving increased volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically. Our results suggest that FEP are characterised by a different computational mechanism - perceiving the environment as increasingly volatile - in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.

EEG microstates as biomarker for psychosis in ultra-high-risk patients.

Resting-state EEG microstates are brief (50-100 ms) periods, in which the spatial configuration of scalp global field power remains quasi-stable before rapidly shifting to another configuration. Changes in microstate parameters have been described in patients with psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk, suggesting potential usefulness of EEG microstates as a biomarker for psychotic disorders. The present study aimed to investigate the potential of EEG microstates as biomarkers for psychotic disorders and future transition to psychosis in patients at ultra-high-risk (UHR). We used 19-channel clinical EEG recordings and orthogonal contrasts to compare temporal parameters of four normative microstate classes (A-D) between patients with first-episode psychosis (FEP; n = 29), UHR patients with (UHR-T; n = 20) and without (UHR-NT; n = 34) later transition to psychosis, and healthy controls (HC; n = 25). Microstate A was increased in patients (FEP & UHR-T & UHR-NT) compared to HC, suggesting an unspecific state biomarker of general psychopathology. Microstate B displayed a decrease in FEP compared to both UHR patient groups, and thus may represent a state biomarker specific to psychotic illness progression. Microstate D was significantly decreased in UHR-T compared to UHR-NT, suggesting its potential as a selective biomarker of future transition in UHR patients.

EEG Microstate Differences in Medicated vs. Medication-Naïve First-Episode Psychosis Patients.

There has been considerable interest in the role of synchronous brain activity abnormalities in the pathophysiology of psychotic disorders and their relevance for treatment; one index of such activity are EEG resting-state microstates. These reflect electric field configurations of the brain that persist over 60-120 ms time periods. A set of quasi-stable microstates classes A, B, C, and D have been repeatedly identified across healthy participants. Changes in microstate parameters coverage, duration and occurrence have been found in medication-naïve as well as medicated patients with psychotic disorders compared to healthy controls. However, to date, only two studies have directly compared antipsychotic medication effects on EEG microstates either pre- vs. post-treatment or between medicated and unmedicated chronic schizophrenia patients. The aim of this study was therefore to directly compare EEG resting-state microstates between medicated and medication-naïve (untreated) first-episode (FEP) psychosis patients (mFEP vs. uFEP). We used 19-channel clinical EEG recordings to compare temporal parameters of four prototypical microstate classes (A-D) within an overall sample of 47 patients (mFEP n = 17; uFEP n = 30). The results demonstrated significant decreases of microstate class A and significant increases of microstate class B in mFEP compared to uFEP. No significant differences between groups were found for microstate classes C and D. Further studies are needed to replicate these results in longitudinal designs that assess antipsychotic medication effects on neural networks at the onset of the disorder and over time during illness progression. As treatment response and compliance in FEP patients are relatively low, such studies could contribute to better understand treatment outcomes and ultimately improve treatment strategies.