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SummaryPersonalised prediction of functional outcomes is a promising approach for targeted early intervention in psychiatry. However, generalisability and resource efficiency of such prognostic models represent challenges. In the PRONIA study (German Clinical Trials Register: DRKS00005042), we demonstrate excellent generalisability of prognostic models in individuals at clinical high-risk for psychosis or with recent-onset depression, and substantial contributions of detailed clinical phenotyping, particularly to the prediction of role functioning. These results indicate that it is possible that functioning prediction models based only on clinical data could be effectively applied in diverse healthcare settings, so that neuroimaging data may not be needed at early assessment stages.

Background Formal thought disorder (FTD) has been associated with more severe illness courses and functional deficits in patients with psychotic disorders. However, it remains unclear whether the presence of FTD characterises a specific subgroup of patients showing more prominent illness severity, neurocognitive and functional impairments. This study aimed to identify stable and generalizable FTD-subgroups of patients with recent-onset psychosis (ROP) by applying a comprehensive data-driven clustering approach and to test the validity of these subgroups by assessing associations between this FTD-related stratification, social and occupational functioning, and neurocognition. Methods 279 patients with ROP were recruited as part of the multi-site European PRONIA study (Personalised Prognostic Tools for Early Psychosis Management; www.pronia.eu). Five FTD-related symptoms (conceptual disorganization, poverty of content of speech, difficulty in abstract thinking, increased latency of response and poverty of speech) were assessed with Positive and Negative Symptom Scale (PANSS) and the Scale for the Assessment of Negative Symptoms (SANS). Results The results with two patient subgroups showing different levels of FTD were the most stable and generalizable clustering solution (predicted clustering strength value = 0.86). FTD-High subgroup had lower scores in social (pfdr < 0.001) and role (pfdr < 0.001) functioning, as well as worse neurocognitive performance in semantic (pfdr < 0.001) and phonological verbal fluency (pfdr < 0.001), short-term verbal memory (pfdr = 0.002) and abstract thinking (pfdr = 0.010), in comparison to FTD-Low group. Conclusions Clustering techniques allowed us to identify patients with more pronounced FTD showing more severe deficits in functioning and neurocognition, thus suggesting that FTD may be a relevant marker of illness severity in the early psychosis pathway.

BackgroundChildhood trauma (CT) is associated with an increased risk of mental health disorders; however, it is unknown whether this represents a diagnosis-specific risk factor for specific psychopathology mediated by structural brain changes. Our aim was to explore whether (i) a predictive CT pattern for transdiagnostic psychopathology exists, and whether (ii) CT can differentiate between distinct diagnosis-dependent psychopathology. Furthermore, we aimed to identify the association between CT, psychopathology and brain structure.MethodsWe used multivariate pattern analysis in data from 643 participants of the Personalised Prognostic Tools for Early Psychosis Management study (PRONIA), including healthy controls (HC), recent onset psychosis (ROP), recent onset depression (ROD), and patients clinically at high-risk for psychosis (CHR). Participants completed structured interviews and self-report measures including the Childhood Trauma Questionnaire, SCID diagnostic interview, BDI-II, PANSS, Schizophrenia Proneness Instrument, Structured Interview for Prodromal Symptoms and structural MRI, analyzed by voxel-based morphometry.Results(i) Patients and HC could be distinguished by their CT pattern with a reasonable precision [balanced accuracy of 71.2% (sensitivity = 72.1%, specificity = 70.4%, p ≤ 0.001]. (ii) Subdomains ‘emotional neglect’ and ‘emotional abuse’ were most predictive for CHR and ROP, while in ROD ‘physical abuse’ and ‘sexual abuse’ were most important. The CT pattern was significantly associated with the severity of depressive symptoms in ROD, ROP, and CHR, as well as with the PANSS total and negative domain scores in the CHR patients. No associations between group-separating CT patterns and brain structure were found.ConclusionsThese results indicate that CT poses a transdiagnostic risk factor for mental health disorders, possibly related to depressive symptoms. While differences in the quality of CT exposure exist, diagnostic differentiation was not possible suggesting a multi-factorial pathogenesis.

Background Childhood adversities and trauma (CAT) are associated with poor functional outcome. However, the influence of the single CAT aspects on the risk of a poor functional outcome within different mental disorders has not been investigated so far. Our aims were (i) to predict individual functional outcome based on CAT (ii) to examine whether the prediction power differs within different diagnostic groups (clinical high-risk for psychosis (CHR), psychosis, affective disorders, anxiety disorders) (iii) to compare the specific patterns of CAT experiences, influencing functional outcomes in these groups. Method Clinical data of 707 patients (mean age: 25.09 years (SD = 5.6), 65.5% male) of the Cologne Early Recognition and Intervention Center were assessed with the Trauma And Distress Scale. Functional outcome was estimated by the Social and Occupational Functioning Assessment Scale and school educational attainment. Using machine learning, we generated individualized models to predict functional outcome and to identify specific CAT patterns. Results Across the entire sample, the best prediction for the functional outcome achieved a balanced accuracy (BAC) of 0.6. After splitting into the single diagnostic groups, an improvement with best results in the psychosis group (BAC = 0.70) was observed. Considering specific CAT patterns, the most predictive items depicted a positive and caring environment – or the absence of these, a positive self-image and experiences of bullying. Conclusions Our results indicated that CAT was differentially associated with functional outcome in the various mental disorders. Thus, the importance of mediating variables, that might explain the interindividual differences in the vulnerability to CAT, like resilience factors, appeared to be crucial.

Negative symptoms occur frequently in individuals at clinical high risk (CHR) for psychosis and contribute to functional impairments. The aim of this study was to predict negative symptom severity in CHR after 9 months. Predictive models either included baseline negative symptoms measured with the Structured Interview for Psychosis-Risk Syndromes (SIPS-N), whole-brain gyrification, or both to forecast negative symptoms of at least moderate severity in 94 CHR. We also conducted sequential risk stratification to stratify CHR into different risk groups based on the SIPS-N and gyrification model. Additionally, we assessed the models’ ability to predict functional outcomes in CHR and their transdiagnostic generalizability to predict negative symptoms in 96 patients with recent-onset psychosis (ROP) and 97 patients with recent-onset depression (ROD). Baseline SIPS-N and gyrification predicted moderate/severe negative symptoms with significant balanced accuracies of 68 and 62%, while the combined model achieved 73% accuracy. Sequential risk stratification stratified CHR into a high (83%), medium (40–64%), and low (19%) risk group regarding their risk of having moderate/severe negative symptoms at 9 months follow-up. The baseline SIPS-N model was also able to predict social (61%), but not role functioning (59%) at above-chance accuracies, whereas the gyrification model achieved significant accuracies in predicting both social (76%) and role (74%) functioning in CHR. Finally, only the baseline SIPS-N model showed transdiagnostic generalization to ROP (63%). This study delivers a multimodal prognostic model to identify those CHR with a clinically relevant negative symptom severity and functional impairments, potentially requiring further therapeutic consideration.

BackgroundExperiences of childhood trauma (CT) are associated with increased psychological vulnerability. Past research suggests that CT might alter stress processing with a subsequent negative impact on mental health. However, it is currently unclear how different domains of CT exert effects on specific subjective experiences of stress during adulthood.MethodsIn the present study, we used network analysis to explore the complex interplay between distinct domains of CT and perceived stress in a large, general-population sample of middle-aged adults (N = 1252). We used a data-driven community-detection algorithm to identify strongly connected subgroups of items within the network. To assess the replicability of the findings, we repeated the analyses in a second sample (N = 862). Combining data from both samples, we evaluated network differences between men (n = 955) and women (n = 1159).ResultsResults indicate specific associations between distinct domains of CT and perceived stress. CT domains reflecting a dimension of deprivation, i.e. experiences of neglect, were associated exclusively to a stress network community representing low perceived self-efficacy. By contrast, CT associated with threat, i.e. experiences of abuse, was specifically related to a stress community reflecting perceived helplessness. Our results replicated with high accordance in the second sample. We found no difference in network structure between men and women, but overall a stronger connected network in women.ConclusionsOur findings emphasize the unique role of distinct domains of CT in psychological stress processes in adulthood, implying opportunities for targeted interventions following distinct domains of CT.

Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.

BackgroundClinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.AimsWe aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.MethodBaseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).ResultsModels combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.ConclusionsFindings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.

Different types of childhood maltreatment (CM) are key risk factors for psychopathology. Specifically, there is evidence for a unique role of emotional abuse in affective psychopathology in children and youth; however, its predictive power for depressive symptomatology in adulthood is still unknown. Additionally, emotional abuse encompasses several facets, but the strength of their individual contribution to depressive affect has not been examined.Here, we used a machine learning (ML) approach based on Random Forests to assess the performance of domain scores and individual items from the Childhood Trauma Questionnaire (CTQ) in predicting self-reported levels of depressive affect in an adult general population sample. Models were generated in a training sample (N = 769) and validated in an independent test sample (N = 466). Using state-of-the-art methods from interpretable ML, we identified the most predictive domains and facets of CM for adult depressive affect.Models based on individual CM items explained more variance in the independent test sample than models based on CM domain scores (RAssessment of CM was retrospective and lacked information on timing and duration. Moreover, reported rates of CM and depressive affect were comparatively low.Our findings corroborate the strong role of subjective experience in CM-related psychopathology across the lifespan that necessitates greater attention in research, policy, and clinical practice.

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.

Sex differences may be important for understanding underlying pathophysiological mechanisms and developing effective preventions and treatments of mental disorders. Despite sex differences in the onset of psychosis, patients at clinical high risk for psychosis (CHR) are underinvestigated for sex effects, especially with respect to models for prediction of conversion to psychosis. We studied psychopathological sex differences in referrals to a German early detection service and in its subgroup of converters and examined sex-specific psychopathological prediction models. In 152 male and 90 female referrals (88% at CHR; 35% converters), symptoms assessed with the Structured Interview for Psychosis-Risk Syndromes were investigated for sex differences using effect sizes. Sex-specific prediction models of psychosis were separately generated using Cox regressions with a LASSO operator. We found different small sex effects (0.10

BackgroundPsychosis expression in the general population may reflect a behavioral manifestation of the risk for psychotic disorder. It can be conceptualized as an interconnected system of psychotic and affective experiences; a so-called ‘symptom network’. Differences in demographics, as well as exposure to adversities and risk factors, may produce substantial heterogeneity in symptom networks, highlighting potential etiological divergence in psychosis risk.MethodsTo explore this idea in a data-driven way, we employed a novel recursive partitioning approach in the 2007 English National Survey of Psychiatric Morbidity (N = 7242). We sought to identify ‘network phenotypes’ by explaining heterogeneity in symptom networks through potential moderators, including age, sex, ethnicity, deprivation, childhood abuse, separation from parents, bullying, domestic violence, cannabis use, and alcohol.ResultsSex was the primary source of heterogeneity in symptom networks. Additional heterogeneity was explained by interpersonal trauma (childhood abuse and domestic violence) in women and domestic violence, cannabis use, ethnicity in men. Among women, especially those exposed to early interpersonal trauma, an affective loading within psychosis may have distinct relevance. Men, particularly those from minority ethnic groups, demonstrated a strong network connection between hallucinatory experiences and persecutory ideation.ConclusionSymptom networks of psychosis expression in the general population are highly heterogeneous. The structure of symptom networks seems to reflect distinct sex-related adversities, etiologies, and mechanisms of symptom-expression. Disentangling the complex interplay of sex, minority ethnic group status, and other risk factors may help optimize early intervention and prevention strategies in psychosis.

Background Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. Methods We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation. Results After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA–CHR|ROD and validation in NAPLS-2–UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts. Conclusions Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

BackgroundPsychosis expression in the general population may reflect a behavioral manifestation of the risk for psychotic disorder. It can be conceptualized as an interconnected system of psychotic and affective experiences; a so-called ‘symptom network’. Differences in demographics, as well as exposure to adversities and risk factors, may produce substantial heterogeneity in symptom networks, highlighting potential etiological divergence in psychosis risk.MethodsTo explore this idea in a data-driven way, we employed a novel recursive partitioning approach in the 2007 English National Survey of Psychiatric Morbidity survey (N = 7,242). We sought to identify ‘network phenotypes’ by explaining heterogeneity in symptom networks through potential moderators, including age, sex, ethnicity, deprivation, childhood abuse, separation from parents, bullying, domestic violence, cannabis use, and alcohol.ResultsSex was the primary source of heterogeneity in symptom networks. Additional heterogeneity was explained by interpersonal trauma (childhood abuse, domestic violence) in women and domestic violence, cannabis use, ethnicity in men. Among women, especially those exposed to early interpersonal trauma, an affective loading within psychosis may have distinct relevance. Men, particularly those from minority ethnic groups, demonstrated a strong network connection between hallucinatory experiences and persecutory ideation.ConclusionSymptom networks of psychosis expression in the general population are highly heterogeneous. The structure of symptom networks seems to reflect distinct sex-related adversities, etiologies, and mechanisms of symptom-expression. Disentangling the complex interplay of sex, minority ethnic group status, and other risk factors may help optimize early intervention and prevention strategies in psychosis.

Background Prevention in psychiatry provides a promising way to address the burden by mental illness. However, established approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of help-seeking populations that present at early recognition services. Thus, novel preventive approaches have been suggested, focusing on a broad transdiagnostic risk syndrome associated with different mental disorders. Conceptualizing such a pluripotent risk syndrome from a network perspective of interacting symptoms allows transdiagnostic investigations beyond binary disease categories. Furthermore, modern technologies such as smartphones facilitate the application of Experience Sampling Methods (ESM). A combination of ESM with network analyses provides valid insights beyond established assessment instruments. Methods We will examine n = 75 individuals (age 18-40 years) of the help-seeking population of the Cologne early recognition centre (FETZ). For a maximally naturalistic sample, only minimal exclusion criteria will be applied. We will collect data for 14 days utilizing a mobile application to assess ten transdiagnostic symptoms, i.e., depressive, anxiety and psychotic symptoms as well as distress level. These data will allow to build both personalized and group symptom network models. Additionally, we will explore associations between symptom networks and sociodemographic, risk and resilience factors, as well as psychosocial functioning. Discussion Our study will provide insights about feasibility and utility of ESM in a help-seeking population. Providing a first explorative phenotyping of the proposed broad transdiagnostic risk syndrome, this study will contribute to innovation of early recognition in psychiatry. Results will help to pave the way for prevention and targeted early intervention in a broader patient group and thus, enable greater intended effects in alleviating the burden of psychiatric disorders. In particular, individual insights provided by our study design are important in the context of precision medicine as well as for patients’ own self-management.

Schizotypy constitutes a susceptibility to beneficial and deleterious schizotypal traits, ranging from coping mechanisms to schizotypal personality disorder on a psychosis continuum. Growing evidence indicates a relationship between childhood adversity and trauma and schizotypy. However, the exact influence of childhood adversity and trauma on schizotypy and its relation to sex is not sufficiently understood. Therefore, we investigated sex-adjusted connections between childhood adversity and trauma subdomains (emotional/physical/sexual abuse, emotional/physical neglect) and positive (magical ideation, perceptual aberration) as well as negative schizotypy (physical/social anhedonia). In total, 240 outpatients of the Early Detection and Intervention Centre of the University Hospital Cologne were assessed with the Trauma and Distress Scale for childhood adversity and trauma and the Wisconsin Schizotypy Scales for schizotypy. Path analyses were performed to investigate sex-adjusted correlations. The well-fitting path model of the total sample linked emotional abuse to magical ideation (p = 0.03; SE = 0.20) and emotional neglect to social anhedonia (p = 0.01; SE = 0.26). In females, physical abuse predicted magical ideation (p = 0.01; SE = 0.33), while emotional neglect forecasted physical anhedonia (p = 0.03; SE = 0.34) and social anhedonia (p = 0.03; SE = 0.32). In males, sexual abuse predicted perceptive aberration (p = 0.04; SE = 0.19) and emotional abuse forecasted magical ideation (p = 0.03; SE = 0.27). Overall, the significance of sex-specific interrelations between trauma and schizotypy were highlighted. Magical ideation and perceptive aberration occurred prominently in the absence of negative and disorganized schizotypy, thus positive schizotypy could be discussed as a beneficial expression of coping with emotional, physical and sexual abuse. Furthermore, emotional neglect should be addressed particularly to prevent deleterious negative schizotypy in females.Trial registration number (20-1243), date of registration (May 19th 2020), retrospectively registered.

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.

Recent life events have been implicated in the onset and progression of psychosis. However, psychological processes that account for the association are yet to be fully understood. Using a network approach, we aimed to identify pathways linking recent life events and symptoms observed in psychosis. Based on previous literature, we hypothesized that general symptoms would mediate between recent life events and psychotic symptoms. We analyzed baseline data of patients at clinical high risk for psychosis and with recent-onset psychosis (n = 547) from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. In a network analysis, we modeled links between the burden of recent life events and all individual symptoms of the Positive and Negative Syndrome Scale before and after controlling for childhood trauma. To investigate the longitudinal associations between burden of recent life events and symptoms, we analyzed multiwave panel data from seven timepoints up to month 18. Corroborating our hypothesis, burden of recent life events was connected to positive and negative symptoms through general psychopathology, specifically depression, guilt feelings, anxiety and tension, even after controlling for childhood trauma. Longitudinal modeling indicated that on average, burden of recent life events preceded general psychopathology in the individual. In line with the theory of an affective pathway to psychosis, recent life events may lead to psychotic symptoms via heightened emotional distress. Life events may be one driving force of unspecific, general psychopathology described as characteristic of early phases of the psychosis spectrum, offering promising avenues for interventions.

A multitude of prediction models for a first psychotic episode in individuals at clinical high-risk (CHR) for psychosis have been proposed, but only rarely validated. We identified transition models based on clinical and neuropsychological data through a registered systematic literature search and evaluated their external validity in 173 CHRs from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the prediction of clinical raters. External discrimination performance varied considerably across the 22 identified models (AUC 0.40-0.76), with two models showing good discrimination performance. None of the tested models significantly outperformed clinical raters (AUC = 0.75). Combining predictions of clinical raters and the best model descriptively improved discrimination performance (AUC = 0.84). Results show that personalized prediction of transition in CHR is potentially feasible on a global scale. For implementation in clinical practice, further rounds of external validation, impact studies, and development of an ethical framework is necessary.

Aim Resilience is rapidly gaining momentum in mental health literature. It provides a new understanding of the highly variable trajectories of mental illness, and has consistently been linked with improved mental health outcomes. The present review aims to clarify the definition of resilience and to discuss new directions for the field. Methods After discussing the definition of resilience, this narrative review synthesizes evidence that identifies the specific protective factors involved in this process. This review also addresses the mechanisms that underlie resilience. Results Recent literature has clarified the three core components of resilience, which are the presence of an adversity or specific risk for mental illness; the influence of protective factors that supersede this risk; and finally, a subsequently more positive outcome than expected. Now that these are largely agreed upon, the field should move on to addressing other topics. Resilience is a dynamic process by which individuals utilize protective factors and resources to their benefit. It can vary within one individual across time and circumstance. It can also refer to good functional outcomes in the context of diagnosable illness. While previous research has focused on psychological resilience, it is essential that resilience is conceptualized across modalities. Conclusions The field should move towards the development of a multimodal model of resilience. Researchers should now focus on producing empirical research which clarifies the specific protective factors and mechanisms of the process, aligning with the core concepts of resilience. This growing, more homogeneous evidence base, can then inform new intervention strategies.

ZusammenfassungIn den letzten Jahren wird zunehmend über die Komorbidität von schizophrenen Störungen mit Angsterkrankungen berichtet. So wurde bei Patienten mit einer Störung aus dem Schizophrenie-Spektrum in 38,3 % eine Angsterkrankung und darunter mit 14,9 % am häufigsten eine soziale Phobie (SP) gefunden. Gerade sozialer Angst kommt bei Patienten mit Psychose eine besondere Bedeutung zu, weil sie sich oft mit Depressivität verbindet und zur Entstehung von psychosozialen Behinderungen beitragen kann. Auch im Hochrisikostadium für Psychosen („Clinical High- Risk State for Psychosis“, HRP) treten Angsterkrankungen bereits gehäuft auf. Daher stellen sich die Fragen, ob auch hier die soziale Angststörung dominiert und welche Bedeutung dies für die Arbeit in den Früherkennungs- und Präventivzentren hätte. Um diese Fragen zu klären, gibt diese Arbeit erstmals einen systematischen Überblick über alle bisher publizierten Untersuchungen zu sozialen Ängste im klinischen HRP.Insgesamt wurden 124 Studien eingeschlossen. Nach der aussagekräftigsten dieser Arbeiten kamen Angsterkrankungen bei den HRP-Probanden mit knapp 51 % hochsignifikant häufiger als bei Kontrollpersonen mit nur knapp 4 % vor. Die SP dominierte und war mit einer Prävalenz 14,4 % gegenüber nur 0,36 % bei den Kontrollpersonen fast genauso häufig vertreten, wie man sie unter den Angststörungen im Schizophrenie-Spektrum (14,9 %) gefunden hat. Der in 9 Studien mit Hilfe der Social Interaction and Anxiety Scale (SIAS) ermittelte Ausprägungsgrad sozialer Ängstlichkeit (SÄ) lag im Mittel bei den HRP Gruppen nahezu genauso hoch (SIAS-Score = 34,4; SD = 6,11) wie bei den bereits an einer Psychose erkrankten Patienten (SIAS-Score = 35,0; SD = 9,56) und hob sich deutlich von den Werten für Angehörige von Psychosekranken (SIAS-Score = 22,1; SD = 8,7) und Kontrollen (SIAS Score = 14,6; SD = 7,28) ab. Der Ausprägungsgrad der SÄ stand zwar mit der für das HRP maßgeblichen attenuierten psychotischen Symptomatik im Zusammenhang, ließ aber keine Vorhersagekraft für den Übergang in die psychotische Erstmanifestation erkennen. Die psychosozialen Funktionsverluste im HRP scheinen zudem nicht nur mit der SÄ, sondern auch mit den ebenfalls häufigen komorbiden depressiven Störungen in Verbindung zu stehen. Ferner konnte eine Studie zeigen, dass es erfolgversprechend ist, die SÄ im HRP durch neu entwickelte spezialisierte kognitive Verhaltenstherapien anzugehen.

Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.

Importance Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. Objective To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning. Design, Setting, and Participants This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018. Main Outcomes and Measures Performance and generalizability of prognostic models. Results A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD. Conclusions and Relevance Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.

Background Precise prognosis of clinical outcomes in individuals at clinical high-risk (CHR) of developing psychosis is imperative to guide treatment selection. While much effort has been put into the prediction of transition to psychosis in CHR individuals, prognostic models focusing on negative symptom progression in this population are widely missing. This is a major oversight bearing in mind that 82% of CHR individuals exhibit at least one negative symptom in the moderate to severe range at first clinical presentation, whereas 54% still meet this criteria after 12 months. Negative symptoms are strong predictors of poor functional outcome irrespective of other symptoms such as depression or anxiety. Prognostic tools are therefore urgently required to track negative symptom progression in CHR individuals in order to guide early personalized interventions. Here, we applied machine-learning to multi-site data from five European countries with the aim of predicting negative symptoms of at least moderate severity 9-month after study inclusion. Methods We analyzed data from the ‘Personalized Prognostic Tools for Early Psychosis Management’ (PRONIA; www.pronia.eu) study, which consisted of 94 individuals at clinical high-risk of developing psychosis (CHR). Predictive models either included baseline level of negative symptoms, measured with the Structured Interview for Prodromal Syndromes, whole-brain gyrification pattern, or both to forecast negative symptoms of moderate severity or above in CHR individuals. Using data from the clinical and gyrification model, further sequential testing simulations were conducted to stratify CHR individuals into different risk groups. Lastly, we assessed the models’ ability to predict functional outcomes in CHR individuals. Results Baseline negative symptom severity alone predicted moderate to severe negative symptoms with a balanced accuracy (BAC) of 68%, whereas predictive models trained on gyrification measures achieved a BAC of 64%. Stacking the two modalities allowed for an increased BAC of 72%. Additional sequential testing simulations suggested, that CHR patients could be stratified into a high risk group with 83% probability of experiencing at least moderate negative symptoms at follow-up and a medium/low risk group with a risk ranging from 25 to 38%, when using the two models sequentially. Furthermore, the models trained to predict negative symptom severity from baseline symptoms were less predictive of role (60% BAC) and social (62% BAC) functioning at follow-up. However, the model trained on gyrification data also predicted role (74% BAC) and social (73% BAC) functioning later on. The stacking model predicted role, and social functioning with 64% BAC and 66% BAC respectively. Discussion To the best of our knowledge this is the first study using state-of-the-art predictive modelling to prospectively identify CHR subjects with negative symptoms in the moderate to above moderate severity range who potentially require further therapeutic consideration. While the predictive performance will need to be validated in other samples and may be improved by expanding the models with additional predictors, we believe that this pragmatic approach will help to stratify individual risk profiles and optimize personal interventions in the future.

Background Functional deficits associated with the Clinical High Risk (CHR) status very often lead to inability to attend school, unemployment, as well as social isolation, thus calling for predictors of individual functional outcomes which may facilitate the identification of people requiring care irrespective of transition to psychosis. Studies have revealed that a pattern of cortical and subcortical gray matter volumes (GMV) anomalies measured at baseline in CHR individuals could predict their functional abilities at follow up. Furthermore, literature is consistent in revealing the crucial role of several environmental adverse events in increasing the risk of developing either transition to psychosis, or a worse overall personal functioning. Therefore, the aim of this study is to employ machine learning to test the individual and combined ability of baseline GMV data and of history of environmental adverse events in predicting good vs. poor social and occupational outcome in CHR individuals at follow up. Methods 92 CHR individuals recruited from the 7 discovery PRONIA sites were included in this project. Social and occupational impairment at follow up (9–12 months) were respectively measured through the Global Functioning: Social (GF:S) and Role (GF:R) scale, and CHR with a follow up rating of 7 or below were labeled as having a poor functional outcome. This way, we could separate our cohort in 52 poor outcome CHR and 40 good outcome CHR. GMV data were preprocessed following published procedures which allowed also to correct for site effects. The environmental classifier was built based on Childhood Trauma Questionnaire, Bullying Scale, and Premorbid Adjustment Scale (childhood, early adolescence, late adolescence and adulthood) scores. Raw scores have been normalized according to the psychometric properties of the healthy samples used for validating these questionnaires and scale, in order to obtain individual scores of deviation from the normative occurrence of adverse environmental events. GMV and environmental-based predictive models were independently trained and tested within a leave-site-out cross validation framework using a Support Vector Machine algorithm (LIBSVM) through the NeuroMiner software, and their predictions were subsequently combined through stacked generalization procedures. Results Our GMV-based model could predict follow up social outcome with 67.4% Balanced Accuracy (BAC) and significance (p=0.01), while it could not predict occupational outcome (46.6% BAC). On the other hand, our environmental-based model could discriminate both poor vs. good social and occupational outcomes at follow up with, respectively, 71% and 66.4% BACs, and significance (both p=0.0001). Specifically, the most reliable features in the environmental classifier were scores reflecting deviations from the normative values in childhood trauma and adult premorbid adjustment, for social outcome prediction, and in bullying experiences and late adolescence premorbid adjustment, for occupational outcome prediction. Only for social outcome prediction, stacked models outperformed individual classifiers’ predictions (74.3% BAC, p=0.0001). Discussion Environmental features seem to be more accurate than GMV in predicting both social and occupational outcomes in CHR. Interestingly, the predictions of follow up social and occupational outcomes rely on different patterns of occurrence of specific environmental adverse events, thus providing novel insights about how environmental adjustment disabilities, bullying and traumatic premorbid experiences may impact on different bad outcomes associated with the CHR status.

Background Formal thought disorder (FThD) has been associated with more severe illness courses and functional deficits in psychosis patients. Given these associations, it remains unclear whether the presence of FThD accounts for the heterogeneous presentation of psychoses, and whether it characterises a specific subgroup of patients showing prominent differential illness severity, neurocognitive and functional impairments already in the early stages of psychosis. Thus, our aim is 1) to evaluate whether there are stable subtypes of patients with Recent-Onset Psychosis (ROP) that are characterized by distinct FThD patterns, 2) to investigate whether this FThD-related stratification is associated with clinical, and neurocognitive phenotypes at an early stage of the disease, and 3) to explore correlation patterns among the FThD-related symptoms, functioning and neurocognition through network analysis. Methods 279 individuals experiencing ROP were recruited for this project as part of multi-site European PRONIA study. In the present study, FThD was assessed with conceptual disorganization, difficulty in abstract thinking, poverty of content of speech, increased latency of response and poverty of speech items from the Positive and Negative Symptom Scale (PANSS) and the Scale for the Assessment of Negative Symptoms (SANS). We first applied a multi-step clustering protocol comparing three clustering algorithms: (i) k-means, (ii) hierarchical clustering, and (iii) partitioning around medoids with the number of clusters ranging from 2 to 10. Our protocol runs following four checkpoints; (i) validity [ClValid package], (ii) re-evaluation of validity results and unbiased determination of the winning algorithm [NbClust package], (iii) stability test [ClusterStability package] and (iv) generalizability [predict.strength package] testing for the most optimal clustering solution. Thereafter, we investigated whether the identified FThD subgrouping solution was associated with neurocognitive performance, social and occupational functioning by using Welch’s two-sample t-test or Mann-Whitney-U test based on the distribution of data, and explored the interrelation of these domains with network analysis by using qgraph package with the spearman correlation matrix among variables. All analyses and univariate statistical comparisons were conducted with R version 3.5.2. We used the False Discovery Rate (FDR)37 to correct all P-values for the multiple comparisons. Results The k-means algorithm-based on two-cluster solution (FThD high vs. low) surviving these validity, stability and generalizability tests was chosen for further association tests and network analysis with core disease phenotypes. Patients in FThD high subgroup had lower scores in global (pfdr = 0.0001), social (pfdr < 0.0001) and role (pfdr < 0.0001) functioning, in semantic (pfdr < 0.0001) and phonological verbal fluency (pfdr = 0.0004), verbal short-term memory (pfdr = 0.0018) and abstract thinking (pfdr = 0.0099). Cluster assignment was not informed by the global disease severity (pfdr = 0.7786) but was associated with more pronounced negative symptoms (pfdr = 0.0001) in the FThD high subgroup. Discussion Our findings highlight how the combination of unsupervised machine learning algorithms with network analysis techniques may provide novel insight about the mappings between psychopathology, neurocognition and functioning. Furthermore, they point how FThD may represent a target variable for individualized psycho-, socio-, logotherapeutic interventions aimed at improving neurocognition abilities and functioning. Prospective studies should further test this promising perspective.

The early detection and intervention in psychoses prior to their first episode are presently based on the symptomatic ultra-high-risk and the basic symptom criteria. Current models of symptom development assume that basic symptoms develop first, followed by attenuated and, finally, frank psychotic symptoms, though interrelations of these symptoms are yet unknown. Therefore, we studied for the first time their interrelations using a network approach in 460 patients of an early detection service (mean age = 26.3 y, SD = 6.4; 65% male; n = 203 clinical high-risk [CHR], n = 153 first-episode psychosis, and n = 104 depression). Basic, attenuated, and frank psychotic symptoms were assessed using the Schizophrenia Proneness Instrument, Adult version (SPI-A), the Structured Interview for Psychosis-Risk Syndromes (SIPS), and the Positive And Negative Syndrome Scale (PANSS). Using the R package qgraph, network analysis of the altogether 86 symptoms revealed a single dense network of highly interrelated symptoms with 5 discernible symptom subgroups. Disorganized communication was the most central symptom, followed by delusions and hallucinations. In line with current models of symptom development, the network was distinguished by symptom severity running from SPI-A via SIPS to PANSS assessments. This suggests that positive symptoms developed from cognitive and perceptual disturbances included basic symptom criteria. Possibly conveying important insight for clinical practice, central symptoms, and symptoms “bridging” the association between symptom subgroups may be regarded as the main treatment targets, in order to prevent symptomatology from spreading or increasing across the whole network.

Background Bullying as a specific subtype of adverse life events is a major risk factor for poor mental health. Although many questionnaires on bullying are available, so far none covers bullying retrospectively throughout school and working life. To close this gap, the Bullying Scale for Adults (BSA) was designed. Methods Based on data of 622 participants from five European countries collected in the prospective multicenter Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study, we investigated whether the BSA is a reliable and valid measurement for bullying and whether there is a difference across different diagnostic groups of early mental disorders (recent onset depressive/ psychotic patients, patients at clinical high-risk of psychosis) and healthy controls. Results Bullying experiences were significantly less frequent in healthy controls than in patient groups, with no significant differences between the three clinical groups. The BSA exhibited a high item scale discrimination (r > .3) and very good internal consistency (Cronbach's α = .93). Four factors were identified: 1. Sexual harassment, 2. Emotional Abuse, 3. Physical Abuse, 4. Problems at school. The highly significant correlation between bullying, and childhood adversities and trauma (r = .645, p Discussion The BSA is the first validated questionnaire that, in retrospective, reliably records various aspects of bullying (incl. its consequences) not only throughout childhood but also working life. It can be used to assess bullying as a transdiagnostic risk factor of mental disorders in different mental disorders, esp. psychosis and depression.

Background A multitude of clinical models to predict transition to psychosis in individuals at clinical high risk (CHR) have been proposed. However, only limited efforts have been made to systematically compare these models and to validate their performance in independent samples. Therefore, in this study we identified psychosis risk models based on information readily obtainable in general clinical settings, such as clinical and neuropsychological data, and compared their performance in the PRONIA study (Personalised Prognostic Tools for Early Psychosis Management, www.pronia.eu) as an independent sample. Methods Of the 278 CHR participants in the PRONIA sample, 150 had available data until month 18 and were included in the validation of eleven psychosis prediction models identified through systematic literature search. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the performance of the prognosis of clinical raters. Psychosocial functioning was explored as an alternative outcome. Results Discrimination performance varied considerably across models (AUC ranging from 0.42 to 0.79). High model performance was associated with the inclusion of neurocognitive variables as predictors. Low model performance was associated with predictors based on dichotomized variables. Clinical raters performed comparable to the best data-driven models (AUC = 0.75). Combining raters’ prognosis and model-based predictions improved discrimination performance (AUC = 0.84), particularly for less experienced raters. One of the tested models predicted transition to psychosis and psychosocial outcomes comparably well. Discussion The present external validation study highlights the benefit of enriching clinical information with neuropsychological data in predicting transition to psychosis satisfactorily and with good generalizability across samples. Integration of data-driven risk models and clinical expertise may improve clinical decision-making in CHR for psychosis, particularly for less experienced raters. This external validation study provides an important step toward early intervention and the personalized treatment of psychotic disorders.

Background Psychotic disorders are associated with serious deterioration in functioning even before the first psychotic episode. Also on clinical high risk (CHR) states of developing a first psychotic episode, several studies reported a decreased global functioning. In a considerable proportion of CHR individuals, functional deterioration remains even after (transient) remission of symptomatic risk indicators. Furthermore, deficits in functioning cause immense costs for the health care system and are often more debilitating for individuals than positive symptoms. However in the past, CHR research has mostly focused on clinical outcomes like transition. Prediction of functioning in CHR populations has received less attention. Therefore, the current study aims at predicting functioning in CHR individuals at a single subject level applying multi pattern recognition to clinical data. Patients with a first depressive episode who frequently have persistent functional deficits comparable to patients in the CHR state were investigated in addition. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Management’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n°602152). Considering a broad set of variables (MRI, clinical data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. 11 university centers in five European countries and in Australia (Munich, Basel, Birmingham, Cologne, Düsseldorf, Münster, Melbourne, Milan, Udine, Bari, Turku) participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis [CHR], patients with a recent onset psychosis [ROP] and patients with a recent onset depression [ROD]) as well as healthy controls. In the current study, we analysed data of 114 CHR and 106 ROD patients. Functioning was measured by the ‘Global Functioning: Social and Role’ Scales (GF S/R). In a repeated, nested cross validation framework we trained a l1-regularized SVM to predict good versus bad outcome. Multivariate pattern recognition analysis allowed to identify most predictive variables from a multitude of clinical, environmental as well as sociodemographic potential predictors assessed in PRONIA. Results Based on the 5 to 20 identified most predictive features, prediction models revealed a balanced accuracy (BAC) up to 77/72 for social functioning in CHR/ROD patients and up to 73/69 for role functioning. These models showed satisfying performance of BACs up to 69/63 for social functioning and 67/60 for role functioning in an independent test sample. As expected, prior functioning levels were identified as main predictive factor but also distinct protective and risk factors were selected into the prediction models. Discussion Results suggest that especially prediction of the multi-faceted construct of role functioning could benefit from inclusion of a rich set of clinical variables. To the best of our knowledge this is the first study that has validated clinical prediction models of functioning in an independent test sample. Identification of predictive variables enables a much more efficient prognostic process. Moreover, understanding the mechanisms underlying functional decline and its illness related pattern might enable an improved definition of targets for intervention. Future research should aim at further maximisation of prediction accuracy and cross-centre generalisation capacity. In addition, other functioning outcomes as well as clinical outcomes need to be focused on.

Background Childhood trauma (CT) is associated with an increased risk for psychiatric disorders like major depression and psychosis. However, the pathophysiological relationship between CT, psychiatric disease and structural brain alterations is still unknown. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Mangement’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n° 602152). Considering a broad set of variables (sMRI, rsMRI, DTI, psychopathological, life event related and sociobiographic data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. Seven clinical centers in five European countries and in Australia participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis (CHR), patients with a recent onset psychosis (ROP) and patients with a recent onset depression (ROD)) as well as healthy controls (HC). To investigate the high-dimensional patterns of CT experience, measured by the childhood trauma questionnaire (CTQ), in HC and our three patient groups (PAT) (n=643), we used a Support Vector Machine (SVM). Furthermore, we tested whether patient-specific CT exposure is associated with structural brain changes by VBM analyses. Results We found that patients and HC could be separated very well by their CTQ pattern, whereas the different patient groups showed no specific CTQ pattern. Furthermore, an association with extensive grey matter changes suggests an impact on brain maturation which may put individuals at increased risk for mental disease. Discussion We have demonstrated in this large multi-center cohort that adverse experiences in childhood contribute transdiagnostically to the riskr for developing a psychiatric disease. The observed association between CTQ scores and structural changes suggests an impact of adverse childhood experiences on brain development. Resulting alterations may add to a neurobiological vulnerability for depression and psychosis. A role of both features for other mental disorders could be assumed and warrants further investigation.

In recent years an increased comorbidity of schizophrenic disorders with anxiety disorders has been reported. Thus, among patients with a disorder from the schizophrenia spectrum, a general anxiety disorder was found in 38.3 % of patients, with 14.9 % of these with social phobia (SP). Especially social anxiety (SA) is of particular importance because it is often associated with depression and can contribute to psychosocial disabilities in patients with psychosis.Anxiety disorders already seem to occur prior to the first psychotic manifestation in the clinical high-risk state (CHR). Therefore, the questions arise as to whether this comorbidity is also dominated by SP in this patient group and, if so, what its consequences are on early detection and prevention of psychotic disorders. To clarify these questions, the present paper provides a systematic review of all published studies on social anxiety (SA) in the CHR.A total of 124 studies were included comprising 1702 CHR individuals, 445 healthy controls, 67 relatives / siblings of patients with psychotic disorders and 95 patients with a psychosis. In the most meaningful study, anxiety disorders were generally highly significant in CHR individuals (51 %) compared to control subjects from the normal population (4 %). Among those with anxiety disorders, 14.4 % suffered from SP compared to 0.36 % in normal controls and thus SP was almost as frequent as the prevalence of this type of anxiety disorders in the schizophrenic spectrum (14.9 %). Also, the degree of SA in CHR individuals (SIAS score = 34.4, SD = 6.11) (SIAS-Score = 22.1, SD = 8.7), measured with the Social Interaction and Anxiety Scale (SIAS) in 9 studies, was almost as high as in psychotic patients (SIAS score = 35.0, SD = 9.56) and healthy controls (SIAS score = 14.,6; SD = 7,28). This degree of SA was also related to the attenuated psychotic symptomatology of the CHR individuals. However, the only study investigating the relationship between SA and a possible transition to a first psychotic manifestation did not reveal any predictive power. The feared psychosocial loss of function, which is already present in CHR, seems to be connected not only to the strong SA, but also to the similarly frequent comorbid depressive disorders.Moreover, one study has already provided some evidence that it is promising to address the SA as well as functional impairments in the CHR through newly developed specialized cognitive behavioural therapies.In den letzten Jahren wird zunehmend über die Komorbidität von schizophrenen Störungen mit Angsterkrankungen berichtet. So wurde bei Patienten mit einer Störung aus dem Schizophrenie-Spektrum in 38,3 % eine Angsterkrankung und darunter mit 14,9 % am häufigsten eine soziale Phobie (SP) gefunden. Gerade sozialer Angst kommt bei Patienten mit Psychose eine besondere Bedeutung zu, weil sie sich oft mit Depressivität verbindet und zur Entstehung von psychosozialen Behinderungen beitragen kann. Auch im Hochrisikostadium für Psychosen („Clinical High- Risk State for Psychosis“, HRP) treten Angsterkrankungen bereits gehäuft auf. Daher stellen sich die Fragen, ob auch hier die soziale Angststörung dominiert und welche Bedeutung dies für die Arbeit in den Früherkennungs- und Präventivzentren hätte. Um diese Fragen zu klären, gibt diese Arbeit erstmals einen systematischen Überblick über alle bisher publizierten Untersuchungen zu sozialen Ängste im klinischen HRP.Insgesamt wurden 124 Studien eingeschlossen. Nach der aussagekräftigsten dieser Arbeiten kamen Angsterkrankungen bei den HRP-Probanden mit knapp 51 % hochsignifikant häufiger als bei Kontrollpersonen mit nur knapp 4 % vor. Die SP dominierte und war mit einer Prävalenz 14,4 % gegenüber nur 0,36 % bei den Kontrollpersonen fast genauso häufig vertreten, wie man sie unter den Angststörungen im Schizophrenie-Spektrum (14,9 %) gefunden hat. Der in 9 Studien mit Hilfe der Social Interaction and Anxiety Scale (SIAS) ermittelte Ausprägungsgrad sozialer Ängstlichkeit (SÄ) lag im Mittel bei den HRP Gruppen nahezu genauso hoch (SIAS-Score = 34,4; SD = 6,11) wie bei den bereits an einer Psychose erkrankten Patienten (SIAS-Score = 35,0; SD = 9,56) und hob sich deutlich von den Werten für Angehörige von Psychosekranken (SIAS-Score = 22,1; SD = 8,7) und Kontrollen (SIAS Score = 14,6; SD = 7,28) ab. Der Ausprägungsgrad der SÄ stand zwar mit der für das HRP maßgeblichen attenuierten psychotischen Symptomatik im Zusammenhang, ließ aber keine Vorhersagekraft für den Übergang in die psychotische Erstmanifestation erkennen. Die psychosozialen Funktionsverluste im HRP scheinen zudem nicht nur mit der SÄ, sondern auch mit den ebenfalls häufigen komorbiden depressiven Störungen in Verbindung zu stehen. Ferner konnte eine Studie zeigen, dass es erfolgversprechend ist, die SÄ im HRP durch neu entwickelte spezialisierte kognitive Verhaltenstherapien anzugehen.

Background Psychotic disorders are associated with serious deterioration in functioning even before the first psychotic episode. Also on clinical high risk (CHR) states of developing a first psychotic episode, several studies reported a decreased global functioning. In a considerable proportion of CHR individuals, functional deterioration remains even after (transient) remission of symptomatic risk indicators. Furthermore, deficits in functioning cause immense costs for the health care system and are often more debilitating for individuals than other symptoms. However in the past, CHR research has mostly focused on clinical outcomes like transition and therefore, functioning in CHR patients is under-investigated. The current study aims at predicting functioning at a single subject level applying multi pattern recognition to clinical data for the first time. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Management’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n° 602152). Considering a broad set of variables (sMRI, rsMRI, DTI, psychopathological, life event related and sociobiographic data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. Seven university centers in five European countries and in Australia (Munich, Basel, Birmingham, Cologne, Melbourne, Milan/Udine, Turku) participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis [CHR], patients with a recent onset psychosis [ROP] and patients with a recent onset depression [ROD]) as well as healthy controls. In the current study, we analysed data of 114 CHR patients. Functioning was measured by the ‘Global Functioning: Social and Role’ Scales (GF S/R). Features were derived from the large pool of clinical data that were assessed in PRONIA including questionnaires measuring CHR criteria as well as psychopathology, family history of psychotic disorders or treatment and various self-rating scales. Feature Elimination method of a strict Wrapper was used to identify most predictive variables from the multitude of clinical data included into the analysis. Results Balanced Accuracy of predicting social functioning in CHR patients was acceptable (pooled cross-validation: BAC = 74.3%, Sens = 72.8%, Spec = 60.3%; leave-site-out cross-validation: BAC = 69.9%, Sens = 84.3%, Spec = 55.6%). In contrast, applying the strict wrapper model revealed worse prediction performance for role functioning. Which might indicate that predicting level of role functioning requires more information than social functioning. As expected, prior functioning levels were identified as main predictive factor but also distinct protective and risk factors were selected into the prediction models. Discussion Identifying single predictive variables is in purpose of a much more efficient prognostic process. Moreover, understanding the mechanisms underlying functional decline and its illness related pattern might enable an improved definition of targets for intervention. Future research should aim at further maximisation of prediction accuracy and cross-centre generalisation capacity. In addition, other functioning outcomes as well as clinical outcomes need to be focused on.

Background Neuro-cognitive deficits are a core feature of psychosis. In the clinical high risk stages of psychosis, neuro-cognitive deficits qualitatively affect the same functions while being quantitatively less marked compared to those in full-blown disorder. Therefore, cognitive impairments are considered to be an important intermediate phenotype for transition to psychosis. Partially overlapping deficits were also reported in depressive disorders, so it is important to identify deficits specifically associated to psychotic symptoms from those common to other conditions. We aimed to identify and differentiate cognitive deficits specifically associated to [i] psychopathology in general (i.e., presence of clinical diagnosis); [ii] psychotic symptoms; [iii] sub- and threshold levels of psychotic symptoms. Methods We compared four groups of participants within the project Personalised Prognostic Tools for Early Psychosis Management (PRONIA; www.pronia.eu). The PRONIA Cognitive Battery (PCB) includes 10 tests selected as reliable measures of neuropsychological difficulties in patients at high-risk of psychosis. The scores were obtained from the PRONIA Discovery Sample, which included 707 participants: 278 healthy controls (HC); 138 recent-onset depression (ROD); 139 clinical high-risk (CHR); 152 recent-onset psychosis (ROP), tested in seven sites across Europe. At first the norms were calculated correcting the HC’s raw scores by sex, age, cognitive level, education, and mother language (English, Finnish, German, Italian, or other). Then, univariate analyses of variance with a priori contrasts were used for directly comparing [i] HC vs ROD/CHR/ROP; [ii] ROD vs CHR/ROP; [iii] CHR vs ROP. Results The difference in cognitive performance between the clinical groups (ROD, CHR, ROP) as compared to the HC [i], was shown in measures of: speed of execution (ωP2 range 0.016–0.123; all p≤0.035); sustained attention (ωP2: 0.024–0.080; p≤0.022); verbal fluency (ωP2: 0.020–0.031; p≤0.002); emotion recognition (ωP2=0.026; p=0.001); visuo-spatial (ωP2: 0.018–0.049; p≤0.006) and verbal (ωP2: 0.038–0.075; p

Background Schizotypy traits range from odd behaviors to symptoms that resemble full schizophrenia, although less severe. Previous studies associated different degrees of positive and negative schizotypal traits to variations in the persons’ cognitive profiles while others related them to the risk to develop psychosis. We hypothesize that similar pattern of positive and negative schyzoypy traits characterize individuals at risk of psychosis and patients meeting the criteria for recent onset psychosis, although with different degrees of severity. Also, both should differ from depressed patients. Moreover, specific combinations of schizotypy traits and neurocognitive alterations should be associated to the different psychopathological profiles. The final goal of the study is to identify candidate predictors of risk of psychosis that will be used as features in next machine learning analyses. Methods The present is a multi-centric study that was conducted as part of the project titled ‘Personalised Prognostic Tools for Early Psychosis Management’ (PRONIA). 115 participants at high-risk for psychosis (CHR), 114 recent onset psychosis (ROP), 123 recent onset depression (ROD) and 252 healthy controls (HC) took part in the study. All were aged between 15 and 40 years. The participants filled the Wisconsin Schizotypy questionnaire, measuring positive (Magical Ideation Scale - MIS; Perceptual Aberration Scale - PAS) and negative schizotypy traits (Social Anhedonia Scale - SAnS; Physical Anhedonia Scale - PAnS). Moreover, they were administered the PRONIA Cognitive Battery (PCB), comprising measures of visuo-spatial dexterity and memory (Rey Figure, copy and delayed drawing), short-term memory (Digit Span - DS), Verbal Learning, Verbal Fluency, Attention (Continuous Performance Test - CPT, Digit Symbol Substitution Test - DSST), Emotions’ Recognition, General Intelligence (WAIS Vocabulary, Matrix Reasoning). Results We run i) a Multivariate Analysis of Covariance with ‘WSS subscales’ as dependent variable; ‘Group’ as between subject factor; ‘Age’ and ‘Gender’ as covariates; ii) a Multinomial logistic regression with ‘Group’ as dependent variable; HC ‘Group’ as reference parameter; ‘WSS subscales’ and scores at the PCB’s tests as predictors; ‘Age’ and ‘Gender’ as covariates. ROP and CHR reported both positive and negative schizotypy traits, although only the negative symptoms involving social aspects were clearly evident in CHR. Also, ROP and CHR differed for the positive symptoms, as they were present but at a lower level in CHR than in ROP. ROD instead scored high at the negative symptoms. Interestingly, ROP, CHR and ROD did not differ between each other for the negative symptoms, probably reflecting the effect of the psychopathology on the patients’ general motivation to life. The regressions analysis highlighted different patterns of associations of WSS and neurocognitive scores with the clinical status. In particular, the scores at the MIS, PAS and SanS combined with the Rey Figure (delayed drawing), predicted that the participants were CHR; the MIS, PAS and SAnS with measures of attention (CPT, DSST) predicted that the participant were ROP; the PAS; SAnS and short-term memory (DS) predicted to being ROD. Discussion Coherently with the hypotheses, different schizotypy traits or grade of severity characterized patients with distinct psychopathology profiles. Also, the association of WSS subscales with the cognitive measures differentiated between groups, with visuo-spatial long-term memory being associated to CHR, measures of attention relating to ROP and verbal short term memory relating to ROD. This makes these measures good candidates for the upcoming machine learning analyses.

Background Childhood trauma (CT) plays an important role in psychiatric disorders. It is associated with an increased risk for psychiatric disorders like major depression, anxiety disorders, dependency, post-traumatic stress disorders and even psychosis. There is a high incidence of CT in patients with psychosis, especially for physical and sexual abuse. Already in UHR-individuals increased CT could be observed. A study of Thompson and colleagues showed that 97% of their UHR sample reported a trauma in the past. 83% of the cases were physical abuse, 67% emotional abuse and 27% sexual abuse. Our aim was to investigate if there are neurobiological surrogate markers of trauma existing which can be detected by a multi pattern analysis. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Management’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n° 602152). Considering a broad set of variables (sMRI, rsMRI, DTI, psychopathological, life event related and sociobiographic data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. Seven clinical centers in five European countries and in Australia participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis [CHR], patients with a recent onset psychosis [ROP] and patients with a recent onset depression [ROD]) as well as healthy controls; planned sample size is n=1680. CT was assessed by the Childhood Trauma Questionnaire (CTQ). To identify neuroanatomical and functional surrogate markers of CT, a multi pattern analysis via Neurominer (NM) was conducted. An additional VBM analysis was performed to evaluate the results of the NM analysis. Results We found that patients and HC could be separated very well by the CTQ pattern. Moreover, the classification among the patient groups yielded results that were not much better than a random classification. This finding underlines that CT is an overarching risk factor for mental diseases and not specific for single clinical entities. Furthermore, the decision scores that were conducted in the first NM analysis revealed highly significant negative correlations with grey matter changes in frontotemporal cortical areas, the anterior cingulate and the insular cortex. These areas are already discussed for CT in the literature. Discussion CT seems to be a global risk factor for psychiatric disorders. Moreover, we could re-examine the results of our multi variate analysis successfully in a VBM procedure.