Diagnosis of a clinical high-risk (CHR) state enables timely treatment of individuals at risk for a psychotic disorder, thereby contributing to improving illness outcomes. However, only a minority of patients diagnosed with CHR will make the transition to overt psychosis. To identify patients most likely to benefit from early intervention, several studies have investigated characteristics that distinguish CHR patients who will later develop a psychotic disorder from those who will not. We aimed to summarize evidence from systematic reviews and meta-analyses on predictors of transition to psychosis in CHR patients, among characteristics and biomarkers assessed at baseline. A systematic search was conducted in Pubmed, Scopus, PsychInfo and Cochrane databases to identify reviews and meta-analyses of studies that investigated specific baseline predictors or biomarkers for transition to psychosis in CHR patients using a cross-sectional or longitudinal design. Non-peer-reviewed publications, gray literature, narrative reviews and publications not written in English were excluded from analyses. We provide a narrative synthesis of results from all included reviews and meta-analyses. For each included publication, we indicate the number of studies cited in each domain and its quality rating. A total of 40 publications (21 systematic reviews and 19 meta-analyses) that reviewed a total of 272 original studies qualified for inclusion. Baseline predictors most consistently associated with later transition included clinical characteristics such as attenuated psychotic and negative symptoms and functioning, verbal memory deficits and the electrophysiological marker of mismatch negativity. Few predictors reached a level of evidence sufficient to inform clinical practice, reflecting generalizability issues in a field characterized by studies with small, heterogeneous samples and relatively few transition events. Sample pooling and harmonization of methods across sites and projects are necessary to overcome these limitations.

Introduction In the inherently noisy real world, we can rarely have full certainty about what we have just seen or heard. Thus, making a perceptual decision on sensory information, and simultaneously tracking our varying levels of certainty in these decisions (i.e., metacognitive abilities) are crucial components of everyday life. Hallucinations, such as confidently reporting a human voice or face when none was present, are a hallmark of psychotic disorders but also occur among the normal population. Particularly in patients with psychotic disorders, these misperceptions are linked to confident beliefs in their actual existence. However, whether patients’ confidence is only increased during such erroneous perceptions and whether perceptual and metacognitive decisions arise from supramodal mechanisms across sensory modalities remains unknown. Objectives In the laboratory, we tested perceptual and metacognitive decisions under varying levels of sensory certainty in healthy adults and patients with psychotic disorders admitted to a psychiatry ward (N con =32, N pat =12; age = 19-49; F2x.x diagnoses). Methods Specifically, participants had to detect human voices or faces against briefly presented noisy backdrops and subsequently rate their confidence in the accuracy of their perceptual decision (Fig 1A,B,C). We further hypothesised that probabilistic cues prior to blocks of trials can bias participants’ choices and hallucination probability (i.e., confident false alarms). Results Patients exhibited higher perceptual sensitivity in the auditory than the visual task, alongside a generally stronger decision bias towards fewer ‘voice/face’ choices (Fig 2A,B). This bias was more pronounced in the visual domain. Decision performance was overall higher on the auditory task but lower for patients (predicted minimum > 55%; Fig 2C). Strong correlations between auditory accuracy and PANSS hallucination scores of patients and LSHS scores of healthy participants suggest an effect of these hallucinatory experiences on accurate perception. Metacognitive abilities were reduced in patients across both modalities: They exhibited general overconfidence, which was stronger for incorrect trials (Fig 3A). Patients’ confidence ratings were inversely related to the probability of choosing ‘voice/face’. Combining both perceptual and confidence decisions, patients showed higher hallucinations probability in the auditory task, particularly in more difficult trials (i.e., with less informative sensory evidence; Fig 3B). Image: Image 2: Image 3: Conclusions In sum, patients with psychotic disorders exhibit increased decision bias accompanied by increased confidence, and thus a reduced fidelity in their metacognitive abilities. The modality differences are in line with phenomenology and reported hallucination rates. These results suggest stronger priors in psychotic disorders resulting in worse perceptual acuity and assessment of this perception. Disclosure of Interest None Declared

Background: Structural MRI studies in people with first-episode psychosis (FEP) and those in the clinical high-risk (CHR) state have consistently shown volumetric abnormalities that depict changes in the structural complexity of the cortical boundary. The aim of the present study was to employ chaos analysis in the identification of people with psychosis based on the structural complexity of the cortical boundary and subcortical areas. Methods: We performed chaos analysis of the grey matter distribution on structural MRIs. First, the outer boundary points for each slice in the axial, coronal and sagittal view were calculated for grey matter maps. Next, the distance of each boundary point from the centre of mass in the grey matter was calculated and stored as spatial series, which was further analyzed by extracting the Largest Lyapunov Exponent (lambda [λ]), a feature depicting the structural complexity of the cortical boundary. Results: Structural MRIs were acquired from 77 FEP, 73 CHR and 44 healthy controls. We compared λ brain maps between groups, which resulted in statistically significant differences in all comparisons. By matching the λ values extracted in axial view with the Morlet wavelet, differences on the surface relief are observed between groups. Limitations: Parameters were selected after experimentation on the examined sample. Investigation of the effectiveness of the method in a larger data set is needed. Conclusion: The proposed framework using spatial series verifies diagnosis-relevant features and may contribute to the identification of structural biomarkers for psychosis.

Prediction models of brain texture changes in recent-onset psychosis (ROP) and recent-onset depression (ROD) have lately been proposed. The validation of these models transdiagnostically at the individual level and the investigation of the variability in clinical profiles are still missing. Established prevention and treatment approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of patients. We aimed to investigate the utility of brain texture changes for a) identification of the psychopathological state (ROP and ROD) and b) the association of individualized brain texture maps with clinical symptom severity and outcome profiles. We developed transdiagnostic models based on structural MRI data on 116 patients with ROD, 122 patients with ROP, and 197 healthy controls (HC) from the Personalised pROgNostic tools for early psychosIs mAnagement (PRONIA) study by applying explainable artificial intelligence and clustering analysis. We investigated the contrast texture feature as the key feature for the identification of a general psychopathological state. The discrimination power of the trained prediction model was > 72% and validated in a second independent age and sex-matched sample of 137 ROP, 94 ROD, and 159 HC. Clustering analysis was implemented to map the texture brain changes produced from an explainable artificial intelligence algorithm, in a group fashion. The explained individualized brain contrast map grouped into 8 homogeneous clusters. In each group, we investigated the association between the explained brain contrast texture map and clinical symptom severity as well as outcome profiles. Different patterns in the explained brain contrast texture map showed unique associations of brain alterations with clinical symptom severity and clinical outcomes, i.e., age, positive, negative and depressive symptoms, and functionality. In some clusters, the mean explained brain contrast texture map values and/or brain contrast texture voxels significantly contribute to the classification decision significantly predicted PANSS scores, functionality and change in functionality over time. In conclusion, we created homogeneous clusters which statistically significant predict the clinical severity and outcome profile.

The combination of a replication crisis, the global COVID-19 pandemic in 2020, and recent technological advances, have accelerated the ongoing transition of research in cognitive psychology and neuroscience to the online realm. When participants cannot be tested in-person, data of acceptable quality can still be collected online. While online research offers many advantages, numerous pitfalls may hinder researchers in addressing their questions appropriately, potentially resulting in unusable data and misleading conclusions. Here, we present an overview of the costs and benefits of conducting online studies in cognitive psychology and neuroscience, coupled with detailed best practice suggestions that span the range from initial study design to the final interpretation of data. These suggestions offer a critical look at issues regarding recruitment of typical and (sub)clinical samples, their comparison, and the importance of context-dependency in each part of a study. We illustrate our suggestions by means of a fictional online study, applicable to traditional paradigms such as research on working memory with a control and treatment group.