The non-specific nature of mental health and structural brain outcomes following childhood trauma.

BACKGROUND: Childhood 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. METHODS: We 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. CONCLUSIONS: These 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.

Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression.

BACKGROUND: Clinical 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. AIMS: We 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. METHOD: Baseline 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). RESULTS: Models 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. CONCLUSIONS: Findings 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.

Does childhood trauma predict schizotypal traits? A path modelling approach in a cohort of help-seeking subjects.

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.

Is there a diagnosis-specific influence of childhood trauma on later educational attainment? A machine learning analysis in a large help-seeking sample.

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.

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis.

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life.

Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach.

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.

Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample.

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.

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and 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.

The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis.

Depression frequently occurs in first-episode psychosis (FEP) and predicts longer-term negative outcomes. It is possible that this depression is seen primarily in a distinct subgroup, which if identified could allow targeted treatments. We hypothesize that patients with recent-onset psychosis (ROP) and comorbid depression would be identifiable by symptoms and neuroanatomical features similar to those seen in recent-onset depression (ROD). Data were extracted from the multisite PRONIA study: 154 ROP patients (FEP within 3 months of treatment onset), of whom 83 were depressed (ROP+D) and 71 who were not depressed (ROP-D), 146 ROD patients, and 265 healthy controls (HC). Analyses included a (1) principal component analysis that established the similar symptom structure of depression in ROD and ROP+D, (2) supervised machine learning (ML) classification with repeated nested cross-validation based on depressive symptoms separating ROD vs ROP+D, which achieved a balanced accuracy (BAC) of 51%, and (3) neuroanatomical ML-based classification, using regions of interest generated from ROD subjects, which identified BAC of 50% (no better than chance) for separation of ROP+D vs ROP-D. We conclude that depression at a symptom level is broadly similar with or without psychosis status in recent-onset disorders; however, this is not driven by a separable depressed subgroup in FEP. Depression may be intrinsic to early stages of psychotic disorder, and thus treating depression could produce widespread benefit.

Basic Symptoms Are Associated With Age in Patients With a Clinical High-Risk State for Psychosis: Results From the PRONIA Study.

In community studies, both attenuated psychotic symptoms (APS) and basic symptoms (BS) were more frequent but less clinically relevant in children and adolescents compared to adults. In doing so, they displayed differential age thresholds that were around age 16 for APS, around age 18 for perceptive BS, and within the early twenties for cognitive BS. Only the age effect has previously been studied and replicated in clinical samples for APS. Thus, we examined the reported age effect on and age thresholds of 14 criteria-relevant BS in a patient sample at clinical-high risk of psychosis (N = 261, age 15-40 yrs.), recruited within the European multicenter PRONIA-study. BS and the BS criteria, "Cognitive Disturbances" (COGDIS) and "Cognitive-perceptive BS" (COPER), were assessed with the "Schizophrenia Proneness Instrument, Adult version" (SPI-A). Using logistic regressions, prevalence rates of perceptive and cognitive BS, and of COGDIS and COPER, as well as the impact of social and role functioning on the association between age and BS were studied in three age groups (15-18 years, 19-23 years, 24-40 years). Most patients (91.2%) reported any BS, 55.9% any perceptive and 87.4% any cognitive BS. Furthermore, 56.3% met COGDIS and 80.5% COPER. Not exhibiting the reported differential age thresholds, both perceptive and cognitive BS, and, at trend level only, COPER were less prevalent in the oldest age group (24-40 years); COGDIS was most frequent in the youngest group (15-18 years). Functional deficits did not better explain the association with age, particularly in perceptive BS and cognitive BS meeting the frequency requirement of BS criteria. Our findings broadly confirmed an age threshold in BS and, thus, the earlier assumed link between presence of BS and brain maturation processes. Yet, age thresholds of perceptive and cognitive BS did not differ. This lack of differential age thresholds might be due to more pronounced the brain abnormalities in this clinical sample compared to earlier community samples. These might have also shown in more frequently occurring and persistent BS that, however, also resulted from a sampling toward these, i.e., toward COGDIS. Future studies should address the neurobiological basis of CHR criteria in relation to age.

General psychopathology links burden of recent life events and psychotic symptoms in a network approach.

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.

Cardiopulmonary resuscitation (CPR) during spaceflight - a guideline for CPR in microgravity from the German Society of Aerospace Medicine (DGLRM) and the European Society of Aerospace Medicine Space Medicine Group (ESAM-SMG).

BACKGROUND: With the "Artemis"-mission mankind will return to the Moon by 2024. Prolonged periods in space will not only present physical and psychological challenges to the astronauts, but also pose risks concerning the medical treatment capabilities of the crew. So far, no guideline exists for the treatment of severe medical emergencies in microgravity. We, as a international group of researchers related to the field of aerospace medicine and critical care, took on the challenge and developed a an evidence-based guideline for the arguably most severe medical emergency - cardiac arrest. METHODS: After the creation of said international group, PICO questions regarding the topic cardiopulmonary resuscitation in microgravity were developed to guide the systematic literature research. Afterwards a precise search strategy was compiled which was then applied to "MEDLINE". Four thousand one hundred sixty-five findings were retrieved and consecutively screened by at least 2 reviewers. This led to 88 original publications that were acquired in full-text version and then critically appraised using the GRADE methodology. Those studies formed to basis for the guideline recommendations that were designed by at least 2 experts on the given field. Afterwards those recommendations were subject to a consensus finding process according to the DELPHI-methodology. RESULTS: We recommend a differentiated approach to CPR in microgravity with a division into basic life support (BLS) and advanced life support (ALS) similar to the Earth-based guidelines. In immediate BLS, the chest compression method of choice is the Evetts-Russomano method (ER), whereas in an ALS scenario, with the patient being restrained on the Crew Medical Restraint System, the handstand method (HS) should be applied. Airway management should only be performed if at least two rescuers are present and the patient has been restrained. A supraglottic airway device should be used for airway management where crew members untrained in tracheal intubation (TI) are involved. DISCUSSION: CPR in microgravity is feasible and should be applied according to the Earth-based guidelines of the AHA/ERC in relation to fundamental statements, like urgent recognition and action, focus on high-quality chest compressions, compression depth and compression-ventilation ratio. However, the special circumstances presented by microgravity and spaceflight must be considered concerning central points such as rescuer position and methods for the performance of chest compressions, airway management and defibrillation.

Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes.

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.

Validation of the Bullying Scale for Adults - Results of the PRONIA-study.

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 < .001) indicated good concurrent validity. 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.

Main Symptomatic Treatment Targets in Suspected and Early Psychosis: New Insights From Network Analysis.

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.

Sex differences in symptomatology of psychosis-risk patients and in prediction of psychosis.

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 < Rosenthal's r < 0.30) in the referral and in the converter sample. In the referral sample, exclusively, males showed more pronounced symptoms (all negative symptoms incl. reduced stress tolerance, grandiosity, and disorganized communication); in converters, females experienced more pronounced perceptual abnormalities, bizarre thinking, and odd behaviors, while males expressed and experienced emotions to a lower degree. Furthermore, sexes differed in psychosis-predictive symptoms: "suspiciousness" and "disorganized communication" were prominent in prediction of psychosis in males, whereas "trouble with focus and attention" was so in females. While most sex differences in patients attending an early detection service seem to reflect general differences that are not specifically related to psychosis, others might be psychosis-specific. These results can inform the development of more individualized and effective interventions for CHR patients based on more precise sex-specific prediction models.

Gray matter volume reductions in patients with schizophrenia: A replication study across two cultural backgrounds.

Structural gray matter (GM) volume reductions in patients with schizophrenia have rarely been replicated across two different sites, the impact of culture and clinical characteristics remains unresolved. Hence, we assessed GM volume reductions in patients with schizophrenia using 3 T magnetic resonace imaging to replicate results across two independent and culturally different backgrounds (Germany, Japan), and to investigate the impact of brain volume reductions on clinical characteristics. In total, 163 German (80 patients) and 203 Japanese (83 patients) participants were included in the analysis. Voxel-based morphometry (VBM) was used to investigate structural differences between the groups and across the two sites, comparing local GM volumes. Clinical variables were used to analyze effects unrelated to the socio-cultural background. Across both data sets, widespread GM reductions in frontal and temporal cortical parts were found between patients and controls, indicating strong effects of diagnosis and only small effects of site. The investigation of clinical characteristics revealed the strongest effects for chlorpromazine equivalents on GM volume reductions primarily in the Japanese sample. Although the effects of site are small, several brain regions do not overlap between the two groups. Thus, GM may be affected differently at the two sites in patients with schizophrenia.

Health literacy in clinical-high-risk individuals for psychosis: A systematic mixed-methods review.

OBJECTIVE: Numerous studies suggest that health literacy (HL) plays a crucial role in maintaining and improving individual health. Empirical findings highlight the relation between levels of a person's HL and her/his clinical outcome. To date, the role of HL in persons at-risk for psychosis has not been systematically reviewed. METHODS: We conducted a systematic review using a mixed-methods approach to analyse a variety of study types. Peer-reviewed publications were systematically searched in PUBMED, Cochrane Library, PsycINFO and Web of Science. RESULTS: The search string returned 10587 publications. After screening, 15 quantitative, four qualitative studies and two reviews were included. Only one study assessed HL as primary outcome, assessing knowledge and beliefs about psychosis among the general population. In the other studies, sub-dimensions of HL were investigated. None of the publications operationalized HL or it's sub-dimensions with a validated measure. CONCLUSIONS: A lack of understanding of their condition, and fear of stigmatization, were associated with a delay in help-seeking among people with clinical-high-risk state for psychosis. Family members, school personnel, general practitioners and the internet play a crucial role in the HL process. Considerable barriers in obtaining adequate specialist support emphasize the urgent need of a "HL environment" for persons at risk for psychosis.

[Social Anxiety in individuals with clinical high-risk state for psychosis]. / Soziale Ängste im klinischen Hochrisikostadium für Psychosen.

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.

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis.

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. ain 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.