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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.

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 aberrant prediction errors lead to a brittle model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for a more 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 shifts in perceived 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.

Circadian rhythms are imprinted in all organisms and influence virtually all aspects of physiology and behaviour in adaptation to the 24-hour day-night cycle. This recognition of a circadian timekeeping system permeating essentially all healthy functioning of body and mind quickly leads to the realisation that, in turn, human ailments should be probed for the degree to which they are rooted in or marked by disruptions and dysregulations of circadian clock functions in the human body. In this review, we will focus on psychosis as a key mental illness and foremost one of its cardinal symptoms: auditory hallucinations. We will discuss recent empirical evidence and conceptual advances probing the potential role of circadian disruption in auditory hallucinations. Moreover, a dysbalance in excitation and inhibition within cortical networks, which in turn drive a disinhibition of dopaminergic signalling will be highlighted as central physiological mechanism. Finally, we will propose two avenues for experimentally intervening on the circadian influences to potentially alleviate hallucinations in psychotic disorders.

Previous research showed that dysfunctions of fronto-striatal neural networks are implicated in the pathophysiology of obsessive–compulsive disorder (OCD). Accordingly, patients with OCD showed altered performances during decision-making tasks. As P300, evoked by oddball paradigms, is suggested to be related to attentional and cognitive processes and generated in the medial temporal lobe and orbitofrontal and cingulate cortices, it is of special interest in OCD research. Therefore, this study aimed to investigate P300 in OCD and its associations with brain activity during decision-making: P300, evoked by an auditory oddball paradigm, was analysed in 19 OCD patients and 19 healthy controls regarding peak latency, amplitude and source density power in parietal cortex areas by sLORETA. Afterwards, using a fMRI paradigm, Blood–oxygen-level-dependent (BOLD) contrast imaging was conducted during a delay-discounting paradigm. We hypothesised differences between groups regarding P300 characteristics and associations with frontal activity during delay-discounting. The P300 did not differ between groups, however, the P300 latency over the P4 electrode correlated negatively with the NEO-FFI score openness to experience in patients with OCD. In healthy controls, P300 source density power correlated with activity in frontal regions when processing rewards, a finding which was absent in OCD patients. To conclude, associations of P300 with frontal brain activation during delay-discounting were found, suggesting a contribution of attentional or context updating processes. Since this association was absent in patients with OCD, the findings could be interpreted as being indeed related to dysfunctions of fronto-striatal neural networks in patients with OCD.

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

Background Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.

Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk patients with no later transition to psychosis (CHR_NT), 15 clinical high-risk patients with later transition (CHR_T) and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject’s clinical status was predicted by importing separately a) the difference of entropy feature map and b) the contrast feature map, resulting in classification accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.

Patients with psychotic disorders present alterations in thalamocortical intrinsic functional connectivity as measured by resting-state functional magnetic resonance imaging. Specifically, thalamic intrinsic functional connectivity is increased with sensorimotor cortices (hyperconnectivity) and decreased with prefrontal limbic cortices (hypoconnectivity). Psychedelics such as lysergic acid diethlyamide (LSD) elicit similar thalamocortical hyperconnectivity with sensorimotor areas in healthy volunteers. It is unclear whether LSD also induces thalamocortical hypoconnectivity with prefrontal limbic cortices, because current findings are equivocal. Thalamocortical hyperconnectivity was associated with psychotic symptoms in patients and substance-induced altered states of consciousness in healthy volunteers. Thalamocortical dysconnectivity is likely evoked by altered neurotransmission, e.g., via dopaminergic excess in psychotic disorders and serotonergic agonism in psychedelic-induced states. It is unclear whether thalamocortical dysconnectivity is also elicited by amphetamine-type substances, broadly releasing monoamines (i.e., dopamine, norepinephrine) but producing fewer perceptual effects than psychedelics.We administrated LSD, d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers and investigated their effects on thalamic intrinsic functional connectivity with 2 brain networks (auditory-sensorimotor and salience networks, corresponding to sensorimotor and prefrontal limbic cortices, respectively), using a double-blind, placebo-controlled, crossover design.All active substances elicited auditory-sensorimotor-thalamic hyperconnectivity compared with placebo, despite predominantly distinct pharmacological actions and subjective effects. LSD-induced effects correlated with subjective changes in perception, indicating a link between hyperconnectivity and psychedelic-type perceptual alterations. Unlike d-amphetamine and MDMA, which induced hypoconnectivity with the salience network, LSD elicited hyperconnectivity. D-amphetamine and MDMA evoked similar thalamocortical dysconnectivity patterns.Psychedelics, empathogens, and psychostimulants evoke thalamocortical hyperconnectivity with sensorimotor areas, akin to findings in patients with psychotic disorders.

BACKGROUND Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. METHODS HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). RESULTS The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. CONCLUSIONS We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Cancer is a complex disease rooted in heterogeneity, which is the phenomenon of individual cells, tissues, or patients having distinct phenotypic and/or genetic characteristics. Observed divergent disease etiology is likely rooted, at least in part, in tumor heterogeneity and the classification of distinct and important subpopulations of cells within the tumor and its associated microenvironment has remained a technical challenge. Standard next-generation sequencing of bulk tumor tissue provides an overall average genetic profile of the sample, and masks contributions from individual cells and minor populations of cells, particularly in heterogeneous samples. Only with the advent of single-cell analysis and sequencing technologies has it become possible to characterize key contributions of cellular subpopulations in order to more comprehensively characterize disease. This chapter describes a method to generate linked phenotypic and genotypic data at single-cell resolution using a real-time single-cell resolved platform. Specifically, the example method provided here is used to link cellular growth kinetics and expression of a prognostic marker protein, CA-125, in cells derived from ovarian cancer patients with their single-cell genomic profiles, but the method is translatable to other cell types and phenotypes of interest.

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis in the identification of people with psychosis. Structural MRI were acquired from 73 CHR, 77 FEP and 44 healthy controls (HC). Chaos analysis of the grey matter distribution was performed: first, the distances of each voxel from the center of mass in the grey matter image was calculated. Next, the distances multiplied by the voxel intensity was represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts how the grey matter topology changes. The classification of a subject’s clinical status was finally predicted by a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and b) matching the lambda series with the Morlet wavelet, which resulted in 100% accuracy in distinguishing between FEP and CHR. The proposed framework using spatial-series extraction enhances the classification decision for FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

Psychiatry has a well-established tradition of comparing drug-induced experiences to psychotic symptoms, based on shared phenomena such as altered perceptions. The present review focuses on experiences induced by classic psychedelics, which are substances capable of eliciting powerful psychoactive effects, characterized by distortions/alterations of several neurocognitive processes (e.g., hallucinations). Herein we refer to such experiences as psychedelic states. Psychosis is a clinical syndrome defined by impaired reality testing, also characterized by impaired neurocognitive processes (e.g., hallucinations and delusions). In this review we refer to acute phases of psychotic disorders as psychotic states. Neuropharmacological investigations have begun to characterize the neurobiological mechanisms underpinning the shared and distinct neurophysiological changes observed in psychedelic and psychotic states. Mounting evidence indicates changes in thalamic filtering, along with disturbances in cortico-striato-pallido-thalamo-cortical (CSPTC)-circuitry, in both altered states. Notably, alterations in thalamocortical functional connectivity were reported by functional magnetic resonance imaging (fMRI) studies. Thalamocortical dysconnectivity and its clinical relevance are well-characterized in psychotic states, particularly in schizophrenia research. Specifically, studies report hyperconnectivity between the thalamus and sensorimotor cortices and hypoconnectivity between the thalamus and prefrontal cortices, associated with patients' psychotic symptoms and cognitive disturbances, respectively. Intriguingly, studies also report hyperconnectivity between the thalamus and sensorimotor cortices in psychedelic states, correlating with altered visual and auditory perceptions. Taken together, the two altered states appear to share clinically and functionally relevant dysconnectivity patterns. In this review we discuss recent findings of thalamocortical dysconnectivity, its putative extension to CSPTC circuitry, along with its clinical implications and future directions.

Background Research on patients at clinical high risk (CHR) for psychosis has so far mainly focused on those with transition to frank psychosis (CHR-T patients). However, the majority of CHR patients do not transition (CHR-NT patients) and relatively little information is available on their clinical and functional outcome. Methods We conducted a systematic review on clinical and functional long-term outcome of CHR-NT patients. Studies were included if they had an average follow-up period of at least 24 months and reported on long-term outcome of CHR-NT patients in one or more of the following domains: (non-)remission from CHR, prevalence of clinical symptoms and/or clinical diagnoses (axis I and II), and psychosocial functioning. Results Ten publications from seven different single or multicenter studies with average follow-up durations of 2–7.5 years could be included. At the last follow-up assessment 28–71% of CHR-NT patients were not remitted from their CHR and 22–82% still had at least one clinical diagnosis. Approximately half of CHR-NT patients presented with poor psychosocial outcome at 2-year and 6-year follow-up. Conclusions The results suggest that, in the long-term, the majority of CHR-NT patients are not in full clinical remission and seem to suffer from one or more clinical disorders and psychosocial impairments. Since relatively few studies could be identified, further research is required to better understand the trajectories and clinical needs of CHR-NT patients.

SUMMARYEarly detection and specialised early intervention for people at high risk for psychotic disorders have received growing attention in the past few decades, with the aim of delaying or preventing the outbreak of explicit psychotic symptoms and improving functional outcomes. This article summarises criteria for a diagnosis of high psychosis risk, the implications for such a diagnosis and recommendations for treatment.LEARNING OBJECTIVESAfter reading this article you will be able to: •recognise signs and symptoms indicating increased psychosis risk•understand uses and limitations of screening for high psychosis risk, and interpretation of results•recognise evidence-based treatment options for patients at clinical high risk for psychosis.DECLARATION OF INTERESTC.A. has received non-financial support from Sunovion and Lundbeck in the past 36 months.

Abnormal gamma-band oscillations (GBO) have been frequently associated with the pathophysiology of schizophrenia. GBO are modulated by glutamate, a neurotransmitter, which is continuously discussed to shape the complex symptom spectrum in schizophrenia. The current study examined the effects of ketamine, a glutamate N-methyl-d-aspartate receptor (NMDAR) antagonist, on the auditory-evoked gamma-band response (aeGBR) and psychopathological outcomes in healthy volunteers to investigate neuronal mechanisms of psychotic behavior. In a placebo-controlled, randomized crossover design, the aeGBR power, phase-locking factor (PLF) during a choice reaction task, the Positive and Negative Syndrome Scale (PANSS) and the Altered State of Consciousness (5D-ASC) Rating Scale were assessed in 25 healthy subjects. Ketamine was applied in a subanaesthetic dose. Low-resolution brain electromagnetic tomography was used for EEG source localization. Significant reductions of the aeGBR power and PLF were identified under ketamine administration compared to placebo (p

HIV-1 causes a chronic infection that affects more than 37 million people worldwide. People living with human immunodeficiency virus (HIV) experience comorbidity related to chronic inflammation despite antiretroviral therapy. However, these inflammatory signaling has not been fully characterized. The role of early entry events on the activation of cellular signaling events and downstream gene expression has not been captured at the single-cell level. Here the authors describe a method that applies principles of live-cell fluorescence microscopy to an automated single-cell platform that cultures and images cells over user-customized time courses, allowing for high-throughput analysis of dynamic cellular processes. This assay can track single-cell live fluorescence microscopy of early events that immediately follow HIV-1 infection, notably the influx of calcium that accompanies exposure to the virus and the development of productive infection using a fluorescent reporter virus. MT-4 cells are loaded with a calcium-sensitive dye and cultured in isolated pens on a nanofluidic device. The cultured cells are infected with an HIV-1 reporter virus (HIV-1 NLCI). A fluorescence microscope positioned above the nanofluidic device measures calcium influx over an 8-min time course following acute HIV-1 exposure. HIV-1 productive infection is measured in those same cells over a 4-day interval. Imaging data from these time courses are analyzed to define virus-host receptor interactions and signaling pathway dynamics. The authors present an integrated, scalable alternative to traditional imaging methods using a novel optofluidic platform capable of single-cell sorting, culturing, imaging, and software automation. This assay can measure the kinetics of events under various conditions, including cell type, agonist, or antagonist effect, while measuring an array of parameters. This is the first established method for nanofluidic high-throughput longitudinal single-cell culture and imaging: This technique can be broadly adapted to study cellular signaling kinetics and dynamic molecular interactions.

This prognostic study evaluates whether psychosis transition can be predicted in patients with clinical high-risk syndromes or recent-onset depression by multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging, and polygenic risk scores for schizophrenia., Key Points Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians’ prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians’ estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians’ lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression., Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models’ geographic generalizability; to test and integrate clinicians’ predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and Relevance These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms’ and clinicians’ risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.

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.

Objectives Reward system dysfunctions are considered to be a pathophysiological mechanism in schizophrenia. Electrophysiological studies of reward system functions have identified frequency-specific brain networks for the processing of positive (high-beta frequency) and negative (theta frequency) events. Remarkably, midbrain dopaminergic signalling also includes theta and high-beta frequency modes, which have been assumed to reflect tonic and phasic dopamine responses, respectively. The aim of the present study was to identify alterations of oscillatory responses to reward feedback in patients with schizophrenia. Methods Seventeen patients with schizophrenia and 18 healthy controls performed a gambling task during recording of 64-channel electroencephalography. The theta and high-beta band total power were investigated in response to feedback events depending on feedback valence (loss or gain) and magnitude (5 vs. 25 points). Results Both the increase of theta oscillatory activity in response to loss feedback (compared to gain feedback) and the increase of high-beta oscillatory activity in response to gain feedback (compared to loss feedback) were reduced in patients. The difference in high-beta responses to gain versus loss feedback in patients was associated with the severity of negative symptoms. Conclusions Our findings are consistent with current models of reward system dysfunction in schizophrenia, and indicate deficits in both cortical tonic and subcortical phasic dopamine activity, consistent with the complex dopaminergic abnormalities in schizophrenia.

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.

Reports linking a 'jumping-to-conclusions' bias to delusions have led to growing interest in the neurobiological correlates of probabilistic reasoning. Several brain areas have been implicated in probabilistic reasoning; however, findings are difficult to integrate into a coherent account. The present study aimed to provide additional evidence by investigating, for the first time, effective connectivity among brain areas involved in different stages of evidence gathering. We investigated evidence gathering in 25 healthy individuals using fMRI and a new paradigm (Box Task) designed such as to minimize the effects of cognitive effort and reward processing. Decisions to collect more evidence ('draws') were contrasted to decisions to reach a final choice ('conclusions') with respect to BOLD activity. Psychophysiological interaction analysis was used to investigate effective connectivity. Conclusion events were associated with extensive brain activations in widely distributed brain areas associated with the task-positive network. In contrast, draw events were characterized by higher activation in areas assumed to be part of the task-negative network. Effective connectivity between the two networks decreased during draws and increased during conclusion events. Our findings indicate that probabilistic reasoning may depend on the balance between the task-positive and task-negative network, and that shifts in connectivity between the two may be crucial for evidence gathering. Thus, abnormal connectivity between the two systems may significantly contribute to the jumping-to-conclusions bias.

Patients diagnosed with schizophrenia display deficits in Theory of Mind (ToM) and Emotion Perception (EP) even before the appearance of full-blown symptomatology.We evaluated ToM and EP in a male cohort consisting of 25 First Episode Psychosis (FEP) and 16 relapsed schizophrenic patients (CHRON) compared to 12 subjects in Ultra-high Risk (UHR) and 23 healthy controls (CTR). Furthermore, we measured the levels of Cortisol, Insulin like Growth Factor (IGF-1), TNF-a, TNF-b and several interleukins as potential biomarkers.Deficits in EP and ToM were found in FEP, CHRON patients and UHR subjects compared to CTR. The impairments in these two domains seem to follow different patterns in the course of psychosis. EP was more impaired in subjects with a longer history of symptomatology whereas there was no statistically significant difference regarding ToM. On the other hand IL-4 was the only biomarker correlated to ToM and EP scores in two different samples of our study.Social Cognition (SC) domains are impaired in patients with psychosis as well as in UHR subjects compared to healthy controls. There are differences in the progress of ToM and EP deficits in the course of psychosis. Interleukins as IL-4 could correlate to SC.

AIM Gender differences in the current symptomatology of patients with psychotic disorders have previously been described in the literature. However, it has not yet been investigated whether gender differences exist in the very first self-perceived signs or symptoms of illness onset. The aim of this study was to investigate this aspect in at-risk mental state (ARMS) and first-episode psychosis (FEP) patients. METHODS ARMS and FEP were recruited via the early detection of psychosis (FePsy) clinic Basel, Switzerland. The Basel Interview for Psychosis (BIP) was used to retrospectively assess the first 3 self-perceived signs and symptoms at illness onset. Differences between gender and patient groups on single item and symptom cluster levels were analysed using logistic regression models. RESULTS One-hundred-thirty six ARMS (91 men, 45 women) and 89 FEP patients (63 men, 26 women) could be recruited for this study. On a single item level, women more frequently reported "unusual anxiety, fears" and men (at a trend level) "social withdrawal" as being among their 3 first self-perceived symptoms, independent of diagnostic group. On the symptom cluster level, women more frequently reported "increased worrying/anxiety" and (sub-threshold) "hallucinations", independent of diagnostic group. Problems with "thinking, concentration" were reported more frequently by men in the ARMS group only. CONCLUSION Our results suggest that only few and relatively small gender differences exist in the first self-perceived signs and symptoms. While men initially mainly notice negative/cognitive symptoms, women first notice (sub-threshold) positive and affective symptoms.

Schizophrenia is a complex psychiatric disorder with unknown and presumably heterogeneous etiology. While the disorder can have various outcomes, research is predominantly “deficit-oriented” emphasizing the hardship that the disorder inflicts on sufferers as well as their families and society. Beyond symptom reduction, imparting patients with hope and meaning in life is increasingly considered an important treatment target, which may raise self-esteem, and reduce self-stigma and suicidal ideation. The present study compared a psychotherapeutic treatment aimed at improving cognitive insight, individualized metacognitive intervention (MCT+), with an active control in order to elucidate if personal meaning-making and hope can be improved in patients with psychosis across time. A total of 92 patients were randomized to either individualized metacognitive therapy (MCT+) or CogPack (neuropsychological training) and followed up for up to 6 months. The “Subjective Sense in Psychosis Questionnaire” (SUSE) was administered which covers different salutogenetic vs pathogenetic views of the disorder, valence of symptom experiences and the consequences of psychosis. Patients in the MCT+ group showed a significant positive shift in attitudes towards the consequences of their illness over time relative to patients in the active control condition. There was some evidence that MCT+ also enhanced meaning-making. The perceived negative consequences of psychosis were highly correlated with depression and low self-esteem, as well as suicidality. The study shows that a cognitive insight training can improve meaning-making in patients and help them come to terms with their diagnosis.

Background: Targeting the N-methyl-d-aspartate receptor (NMDAR) is a major translational approach for treating negative symptoms of schizophrenia. Ketamine comprehensively produces schizophrenia-like symptoms, such as positive, cognitive and negative symptoms in healthy volunteers. The amplitude of the mismatch negativity (MMN) is known to be significantly reduced not only in patients with schizophrenia, but also in healthy controls receiving ketamine. Accordingly, it was the aim of the present study to investigate whether changes of MMN amplitudes during ketamine administration are associated with the emergence of schizophrenia-like negative symptoms in healthy volunteers. Methods: We examined the impact of ketamine during an MMN paradigm with 64-channel electroencephalography (EEG) and assessed the psychopathological status using the Positive and Negative Syndrome Scale (PANSS) in healthy male volunteers using a single-blind, randomized, placebo-controlled crossover design. Low-resolution brain electromagnetic tomography was used for source localization. Results: Twenty-four men were included in our analysis. Significant reductions of MMN amplitudes and an increase in all PANSS scores were identified under the ketamine condition. Smaller MMN amplitudes were specifically associated with more pronounced negative symptoms. Source analysis of MMN generators indicated a significantly reduced current source density (CSD) under the ketamine condition in the primary auditory cortex, the posterior cingulate and the middle frontal gyrus. Limitations: The sample included only men within a tight age range of 20–32 years. Conclusion: The MMN might represent a biomarker for negative symptoms in schizophrenia related to an insufficient NMDAR system and could be used to identify patients with schizophrenia with negative symptoms due to NMDAR dysfunction.

Background and objectives Individualized Metacognitive Training (MCT+) is a manualized intervention designed to improve delusional severity by reducing delusion-associated cognitive biases such as jumping-to-conclusions. Increased interest in personalized medicine stipulates the identification of patients who are more likely to benefit from specialized interventions. The present study aimed to explore baseline moderators of MCT+ efficacy on delusions and overall positive symptoms in psychosis. Methods We analyzed data from a randomized rater-blind controlled trial, in which 92 patients with psychotic disorders and current or past delusions were randomly assigned to either MCT+ or CogPack®, a cognitive remediation software. Baseline moderator variables consisted of jumping-to-conclusions, cognitive insight, quality of life, self-esteem, selective attention, and patients’ attitudes towards their symptoms. Linear mixed-effects models were applied to investigate specific moderators of MCT+ efficacy. Results In MCT+ relative to CogPack, presence of a jumping-to-conclusions bias, a lowered decision threshold, and low self-esteem were associated with larger improvements in delusional severity and/or overall positive symptoms over time. Subjective reasoning style and insight, as well as subjective attitudes towards psychosis, did not moderate the treatment efficacy of MCT+ relative to CogPack. Limitations Participation of both treatment groups in group MCT as a part of standard care, possibly leading to additional effects on delusional severity. Conclusions Patients with low self-esteem and those who are prone to jumping-to-conclusions seem to particularly benefit from MCT+. Our results can help inform clinical practice as they provide specific criteria for selecting patients for whom MCT+ is most appropriate.

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. Data from 200 patients of the prospective Fruherkennung 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. 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. 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.

Background:Few studies have followed up patients with a clinical high risk (CHR) for psychosis for more than 2–3 years. We aimed to investigate the rates and baseline predictors for remission from CHR and transition to psychosis over a follow-up period of up to 16 years. Additionally, we examined the clinical and functional long-term outcome of CHR patients who did not transition.Methods:We analyzed the long-term course of CHR patients that had been included in the longitudinal studies “Früherkennung von Psychosen” (FePsy) or “Bruderholz” (BHS). Those patients who had not transitioned to psychosis during the initial follow-up periods (2/5 years), were invited for additional follow-ups.Results:Originally, 255 CHR patients had been included. Of these, 47 had transitioned to psychosis during the initial follow-ups. Thus, 208 were contacted for the long-term follow-up, of which 72 (34.6%) participated. From the original sample of 255, 26%, 31%, 35%, and 38% were estimated to have transitioned after 3, 5, 10, and 16 years, respectively, and 51% had remitted from their high risk status at the latest follow-up. Better psychosocial functioning at baseline was associated with a higher rate of remission. Of the 72 CHR patients re-assessed at long-term follow-up, 60 had not transitioned, but only 28% of those were fully recovered clinically and functionally.Conclusions:Our study shows the need for follow-ups and clinical attention longer than the usual 2–3 years as there are several CHR patients with later transitions and only a minority of CHR those without transition fully recovers.

Dieser Artikel zeigt auf, wie Fachpersonen, die im Erstkontakt mit psychosegefahrdeten Menschen zu tun haben, den klinischen Verlauf und die Prognose konkret verbessern konnen.

Cet article montre comment les professionnels qui ont le premier contact avec les personnes a risque de psychose peuvent ameliorer concretement l’evolution clinique et le pronostic.

Objectives: Brain-derived neurotrophic factor (BDNF) is involved in numerous cognitive processes. Since cognitive deficits are a core feature of psychotic disorders, the investigation of BDNF levels in psychosis and their correlation with cognition has received increased attention. However, there are no studies investigating BDNF levels in individuals with an at-risk mental state (ARMS) for psychosis. Hence, the aims of the present study were: (1) assessing peripheral BDNF levels across different (potential) stages of psychosis; (2) investigating their association with cognition. Methods: Plasma and serum BDNF levels and neuropsychological performance were assessed in 16 ARMS, six first-episode psychosis (FEP), and 11 chronic schizophrenia (CS) patients. Neuropsychological assessment covered intelligence, verbal memory, working memory, attention and executive functioning. Results: Both plasma and serum BDNF levels were highest in CS, intermediate in FEP and lowest in ARMS. Multiple regression analysis revealed a significant positive association of plasma BDNF levels with planning ability across all groups. Conclusions: The lower peripheral BDNF levels in ARMS compared to FEP and CS might point towards an important drop of this neurotrophin prior to the onset of frank psychosis. The associations of peripheral BDNF with planning-abilities match previous findings.

Association Splitting, a novel cognitive intervention, was tested in patients with alcohol dependence as an add-on intervention in an initial randomized controlled trial. Preliminary support for Association Splitting has been found in patients with obsessive-compulsive disorder, as well as in an online pilot study of patients with alcohol use disorders. The present variant sought to reduce craving by strengthening neutral associations with alcohol-related stimuli, thus, altering cognitive networks. Eighty-four inpatients with verified diagnoses of alcohol dependence, who were currently undergoing inpatient treatment, were randomly assigned to Association Splitting or Exercise Therapy. Craving was measured at baseline, 4-week follow-up, and six months later with the Obsessive-Compulsive Drinking Scale (primary outcome) and the Alcohol Craving Questionnaire. There was no advantage for Association Splitting after three treatment sessions relative to Exercise Therapy. Among Association Splitting participants, 51.9% endorsed a subjective decline in craving and 88.9% indicated that they would use Association Splitting in the future. Despite high acceptance, an additional benefit of Association Splitting beyond standard inpatient treatment was not found. Given that participants were concurrently undergoing inpatient treatment and Association Splitting has previously shown moderate effects, modification of the study design may improve the potential to detect significant effects in future trials.

Childhood trauma (CT) is a comprehensive concept encompassing experiences of sexual, physical, and emotional abuse, and neglect during childhood and adolescence. Patients with schizophrenia-spectrum psychosis (SSP) display higher rates of CT than healthy controls. Among the potential mediators of this association, insecure attachment has gained attention and empirical validation. The present study aimed to extend existing knowledge on this field by exploring the role of the two attachment dimensions, attachment anxiety and attachment avoidance, in the CT-SSP association. A clinical sample of 63 SSP inpatients was compared to a healthy control group on CT and attachment style measures. Correlations between CT, attachment dimensions and psychopathology were sought. Mediation analyses were also performed to examine whether attachment anxiety and/or attachment avoidance mediated the CT-SSP association. Patients displayed higher rates of CT and insecure attachment than controls. Attachment anxiety and severity of Mother Antipathy were linked to severity of hallucinations. Attachment anxiety was recognized as the sole mediator of the CT-SSP association. Our findings suggest that individuals with severe CT and increased attachment anxiety represent a risk population warranting early clinical attention, regular monitoring and tailored therapeutic interventions aimed at reducing the psychological impact of trauma.

Several studies using electroencephalography (EEG) demonstrate that the processing of feedback in patients suffering from borderline personality disorder (BPD) is altered in comparison to healthy controls. Differences occur in the theta (ca. 5 Hz) and high-beta frequency-ranges (ca. 20 Hz) of oscillations in response to negative and positive feedback, respectively. However, alpha (ca. 10 Hz) and low-beta (ca. 15 Hz) oscillations have also been shown to be involved in feedback processing. We hypothesized that additional alterations might occur in these frequency ranges in BPD. Eighteen patients with BPD and twenty-two healthy controls performed a gambling task while 64-channel-EEG was recorded. Induced oscillatory responses to positive (i.e. gain) and negative (i.e. loss) feedback in the alpha and low-beta frequency range were investigated. No significant differences were found in the alpha frequency range. Regarding the low-beta frequency range a significant Group (i.e. BPD vs. healthy controls) × Valence (i.e. gain vs. loss) interaction in the time frame between 600 and 700 milliseconds after feedback was found. This effect showed a significant correlation with symptom severity (assessed with the BSL-23). The results indicate that feedback processing in BPD could be more heavily altered than previously expected, with more severe symptomatology being linked to stronger alterations in oscillatory responses to feedback in the low-beta range.

In the past few decades, sex differences have been identified in a number of clinical, cognitive and functional outcomes in patients with schizophrenia spectrum disorders. However, to date, sex differences in higher-order cognitive biases have not been systematically studied. The present study aimed to examine sex differences in jumping-to-conclusions and evidence integration impairment based on data collected in two previous studies in patients with schizophrenia spectrum disorders and healthy controls. For this purpose, data from n = 58 patients and n = 60 healthy controls on the Fish Task (as a measure of jumping to conclusions) and bias against disconfirmatory evidence (BADE; as a measure of evidence integration) task were analyzed. Results indicated a lack of sex differences in jumping-to-conclusions and evidence integration impairment both in patients with schizophrenia spectrum disorders and healthy controls. Although the present study was adequately powered to detect sex differences of a low medium effect size, larger studies are warranted to exclude differences of a smaller magnitude between men and women regarding delusion-associated cognitive biases.

Grey matter (GM) volume alterations have been repeatedly demonstrated in patients with first episode psychosis (FEP). Some of these neuroanatomical abnormalities are already evident in the at-risk mental state (ARMS) for psychosis. Not only GM alterations but also neurocognitive impairments predate the onset of frank psychosis with verbal learning and memory (VLM) being among the most impaired domains. Yet, their interconnection with alterations in GM volumes remains ambiguous. Thus, we evaluated associations of different subcortical GM volumes in the medial temporal lobe with VLM performance in antipsychotic-naive ARMS and FEP patients. Data from 59 ARMS and 31 FEP patients, collected within the prospective Fruherkennung von Psychosen study, were analysed. Structural T1-weighted images were acquired using a 3 Tesla magnetic resonance imaging scanner. VLM was assessed using the California Verbal Learning Test and its factors Attention Span, Learning Efficiency, Delayed Memory and Inaccurate Memory. FEP patients showed significantly enlarged volumes of hippocampus, pallidum, putamen and thalamus compared to ARMS patients. A significant negative association between amygdala and pallidum volume and Attention Span was found in ARMS and FEP patients combined, which however did not withstand correction for multiple testing. Although we found significant between-group differences in subcortical volumes and VLM is among the most impaired cognitive domains in emerging psychosis, we could not demonstrate an association between low performance and subcortical GM volumes alterations in antipsychotic-naive patients. Hence, deficits in this domain do not appear to stem from alterations in subcortical structures.

Ongoing research is shifting towards a dimensional understanding of borderline personality disorder (BPD). Aim of this study was to identify personality profiles in BPD that are predictive of self-destructive behaviors. Personality traits were assessed (n = 130) according to the five-factor model of personality (i.e., Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) and an additional factor called Risk Preference. Self-destructive behavior parameters such as non-suicidal self-injury (NSSI) and other borderline typical dyscontrolled behaviors (e.g., drug abuse) were assessed by self-report measures. Canonical correlation analyses demonstrated that Neuroticism, Extraversion, and Conscientiousness are predictors of NSSI. Further, Neuroticism, Agreeableness, and Risk Preference were associated with dyscontrolled behaviors. Our results add further support on personality-relevant self-destructive behaviors in BPD. A combined diagnostic assessment could offer clinically meaningful insights about the causes of self-destruction in BPD to expand current therapeutic repertoires.

Negative symptoms and neurocognitive performance have been reported to be negatively associated in patients with emerging psychosis. However, most previous studies focused on patients with frank psychosis and did not differentiate between subdomains of negative symptoms. Hence, we aimed to elucidate the specific relationship between negative symptoms and cognitive functioning in patients at clinical high risk (CHR) for psychosis. Data from 154 CHR patients collected within the prospective Fruherkennung von Psychosen (FePsy) study were analyzed. Negative symptoms were assessed with the Scale for the Assessment of Negative Symptoms (SANS) and cognitive functioning with an extensive neuropsychological test battery. Regression analyses revealed significant negative associations between negative symptoms and cognitive functioning, particularly in the domains of nonverbal intelligence and verbal fluency. When analyzing each negative symptom domain separately, alogia and asociality/anhedonia were significantly negatively associated with nonverbal intelligence and alogia additionally with verbal fluency. Overall, our results in CHR patients are similar to those reported in patients with frank psychosis. The strong negative association between verbal fluency and negative symptoms may be indicative of an overlap between these constructs. Verbal fluency might have a strong influence on the clinical impression of negative symptoms (particularly alogia) and vice versa.

Background Verbal learning and memory are impaired not only in patients with a first episode of psychosis (FEP) but also–to a lower extent–in those with an at-risk mental state for psychosis (ARMS). However, little is known about the specific nature of these impairments. Hence, we aimed to study learning and memory processes in ARMS and FEP patients by making use of structural equation modelling. Methods Verbal learning was assessed with the California Verbal Learning Test (CVLT) in 98 FEP patients, 126 ARMS patients and 68 healthy controls (HC) as part of the Basel early detection of psychosis (FePsy) study. The four-factorial CFA model of Donders was used to estimate test performance on latent variables of the CVLT and growth curve analysis was used to model the learning curve. The latter allows disentangling initial recall, which is strongly determined by attentional processes, from the learning rate. Results The CFA model revealed that ARMS and FEP patients were impaired in Attention Span, Learning Efficiency and Delayed Memory and that FEP patients were additionally impaired in Inaccurate Memory. Additionally, ARMS-NT, but not ARMS-T, performed significantly worse than HC on Learning Efficiency. The growth curve model indicated that FEP patients were impaired in both initial recall and learning rate and that ARMS patients were only impaired in the learning rate. Conclusions Since impairments were more pronounced in the learning rate than the initial recall, our results suggest that the lower scores in the CVLT reported in previous studies are more strongly driven by impairments in the rate of learning than by attentional processes.

Disturbances in evidence gathering and disconfirmatory evidence integration have been associated with the presence of or propensity for delusions. Previous evidence suggests that these 2 types of reasoning bias might be differentially affected by antipsychotic medication. We aimed to investigate the effects of a dopaminergic agonist (L-dopa) and a dopaminergic antagonist (haloperidol) on evidence gathering and disconfirmatory evidence integration after single-dose administration in healthy individuals.The study used a randomized, double-blind, placebo-controlled, 3-way crossover design. Participants were healthy individuals aged 18-40 years. We administered a new data-gathering task designed to increase sensitivity to change compared with traditional tasks. The Bias Against Disconfirmatory Evidence (BADE) task was used as a measure of disconfirmatory evidence integration.We included 30 individuals in our study. In the data-gathering task, dopaminergic modulation had no significant effect on the amount of evidence gathered before reaching a decision. In contrast, the ability of participants to integrate disconfirmatory evidence showed a significant linear dopaminergic modulation pattern (highest with haloperidol, intermediate with placebo, lowest with L-dopa), with the difference between haloperidol and L-dopa marginally reaching significance.Although the doses used for haloperidol and L-dopa were similar to those used in previous studies, drug plasma level measurements would have added to the validity of findings.Evidence gathering and disconfirmatory evidence integration might be differentially influenced by dopaminergic agents. Our findings are in support of a dual-disturbance account of delusions and provide a plausible neurobiological basis for the use of interventions targeted at improving reasoning biases as an adjunctive treatment in patients with psychotic disorders.

The aim of the present study was to investigate the effects of comorbid obsessive-compulsive symptoms (OCS) on the perception of insincere speech (e.g., sarcasm and white lies) in patients with first-episode psychosis.Participants were 65 patients with nonaffective first-episode psychosis (FEP) and 47 healthy controls. The patient group was divided into two subgroups, those with (FEP+; n= 38) and those without OCS (FEP-; n = 27). The ability to process sarcasm and lie was assessed with the Perception of Social Inference Test (PESIT). Severity of psychotic symptoms and OCS was assessed with the Positive and Negative Syndrome Scale (PANSS) and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), respectively.Deficits in the perception of sarcasm and lie were confirmed in patients with nonaffective first-episode psychosis compared to healthy controls. In patients, comorbidity with OCS was associated with worse performance on certain aspects of insincere speech (i.e., lie) compared to FEP- patients. Y-BOCS scores correlated significantly with the perception of lying. The cognitive factor of the PANSS predicted accuracy on the perception of sincere speech, paradoxical sarcasm, and white lies, while the presence of OCS predicted accuracy on the perception of sincere speech and white lies.Our finding of impaired counterfactual information processing in patients with first-episode psychosis suggests that these deficits are already present at illness onset. Presence of OCS appears to have additional deleterious effects on the successful interpretation of intentional lying, further supporting that these patients are characterized by more extensive cognitive impairment.

Many patients with schizophrenia display neuropsychological deficits in concert with cognitive biases, particularly the tendency to jump to conclusions (JTC). The present study examined the effects of a generic psychoeducational cognitive bias correction (CBC) program. We hypothesized that demonstrating the fallibility of human cognition to patients would diminish their susceptibility to the JTC bias. A total of 70 participants with schizophrenia were recruited online. At baseline, patients were asked to fill out a JTC task (primary outcome) and the Paranoia Checklist before being randomized to either the CBC or a waitlist control condition. The CBC group received six successive pdf-converted PowerPoint presentations teaching them about cognitive biases; we neither placed any emphasis on psychosis-related cognitive distortions nor addressed psychosis. Six weeks after inclusion, subjects were re-administered the JTC task and the Paranoia Checklist. At a medium-to-large effect size the JTC bias was significantly improved under the CBC condition in comparison to controls for both the per protocol and the intention to treat analysis. The Paranoia Checklist remained essentially unchanged over time. No effects were observed for depression. Psychoeducational and cognitive programs are urgently needed as many patients are still deprived of any psychological treatment despite recommendations of most guidelines. Self-help may bridge the large treatment gap in schizophrenia and motivate patients to seek help. The study asserts both the feasibility and effectiveness of self-help programs in schizophrenia.

Patients with delusions exhibit an increased tendency to arrive at decisions based on very limited evidence (jumping-to-conclusions; JTC), making this reasoning bias relevant for the treatment of delusions. Neurocognitive deficits contribute to JTC, but it is not known whether this has any bearing on the clinical syndrome of delusions. We addressed this question by reanalyzing data from an efficacy study of non-pharmacological interventions as adjunctive treatments in schizophrenia. We investigated the longitudinal associations of cognitive functioning, JTC and delusions in patients with psychotic disorders receiving either a metacognitive intervention addressing reasoning biases (n=59), or cognitive remediation (n=58). Both interventions improved JTC; in the cognitive remediation group, tentative evidence suggested that better neurocognitive performance contributed to this improvement. However, JTC gains were associated with delusion improvement only in the metacognitive intervention group, suggesting a content-specific mechanism of action.