Psychosis Transition Research Articles

Recent Insights in the Treatment for Clinical High Risk for Psychosis and Recent Onset Psychosis

Objectives: This study aims to assess the effectiveness of early interventions in preventing psychosis transition, promoting remission, and reducing hospitalization rates in individuals at high risk for psychosis and those with recent onset psychosis (ROP).Methods: A systematic review and meta-analysis were conducted, comparing early intervention strategies such as cognitive-behavioral therapy and psychosocial support to no intervention. The study focused on outcomes related to psychosis transition, remission rates, and prevention of psychiatric hospitalization.Results: Although only a subset of clinical high risk (CHR) individuals transition to full psychosis, non-pharmacological treatments like cognitive-behavioral therapy are generally recommended as a first-line approach. In ROP patients, early pharmacological treatment reduces relapse rates, while psychosocial interventions aim to improve various functional outcomes. The meta-analysis results of this study did not show a significant reduction in psychosis transition rates with specialized interventions for CHR patients. For ROP patients, early interventions initially reduced hospitalization rates, but this effect was not sustained in mid-term follow-up results.Conclusion: While early interventions offer short-term benefits in reducing psychosis transition and hospitalization, additional research is needed to determine their long-term effectiveness in functional recovery and overall patient outcomes.

Antipsychotic Treatment in People at Clinical High Risk for Psychosis: A Narrative Review of Suggestions for Clinical Practice.

The "early intervention" paradigm in psychiatry holds significant promise for preventing psychosis. Recent evidence showed that individuals at clinical high risk for psychosis (CHR-P) with antipsychotic (AP) prescription at baseline have higher psychosis transition rates compared with those without AP, although the underlying cause remains unclear. In this article, we reviewed international guidelines on early intervention in CHR-P people, paying specific attention to clinical recommendations on AP treatment. Then, we comment on these suggestions in the light of recent empirical evidence examining AP prescription in CHR-P populations within "real-world" clinical settings. This search was conducted on PubMed/MEDLINE, PsycINFO, EMBASE, and Google, looking for both "Guidelines AND CHR-P OR UHR OR Early Psychosis." International guidelines generally recommend not using AP as first-line treatment, but only when psychosocial interventions have failed. CHR-P people with AP drug showed high prevalence rates and had more severe clinical picture at entry. Is this a "warning signal" for potentially higher psychosis transition risk? Is it a direct AP iatrogenic effect? Is it possible to detect specific CHR-P subgroup that may benefit from AP? These are the questions that this article seeks to explore. The current framework for identifying CHR-P subjects has defined psychometric criteria mainly based on positive symptoms. In our opinion, this is reductive, especially for evaluating therapeutic outcomes and prognosis. A more comprehensive assessment considering quality of life, psychiatric comorbidity, persistent negative symptoms, subjective experience of CHR-P psychopathology, and social/personal recovery is thus needed.

Identification of youth at clinical high-risk for psychosis: A community-based study from India.

A two-stage process, wherein self-report screening precedes the structured interview, is suggested for identifying individuals at clinical high-risk for psychosis (CHR-P) in community samples. Aim of this study was to screen a community youth sample from India for CHR-P using the two-stage method. Specific objectives were to assess concordant validity of the self-report measure and predictive validity of the two-stage method. Based on probability sampling, 2025 youth aged 15-24 years were recruited from one rural and one urban area of Telangana, a Telugu-speaking state in India. Telugu version of the PRIME Screen-Revised (PS-R) and structured interview for psychosis-risk syndromes (SIPS) were used. CHR-P positive and negative cohorts were followed-up for transition to psychosis at 3-monthly intervals. One hundred ten individuals screened positive on PS-R. SIPS conducted on 67 out of 110 individuals confirmed 62 (92.54%) to be CHR-P positive. PS-R showed 98.41% sensitivity and 90.74% specificity. Among CHR-P positive, three participants transitioned to psychosis in 15 months. The hazard ratio for psychosis transition was 11.4. Screening accuracy of PS-R in the community youth sample in Telangana is optimum. The hazard ratio for psychosis transition in the community identified CHR-P indicates good predictive validity for the two-stage method.

Multimodal fusion of brain signals for robust prediction of psychosis transition

The prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.

Effects of Benzodiazepine Exposure on Real-World Clinical Outcomes in Individuals at Clinical High Risk for Psychosis.

Animal models indicate GABAergic dysfunction in the development of psychosis, and that benzodiazepine (BDZ) exposure can prevent the emergence of psychosis-relevant phenotypes. However, whether BDZ exposure influences real-world clinical outcomes in individuals at clinical high risk for psychosis (CHR-P) is unknown. This observational cohort study used electronic health record data from CHR-P individuals to investigate whether BDZ exposure (including hypnotics, eg, zopiclone) reduces the risk of developing psychosis and adverse clinical outcomes. Cox proportional-hazards models were employed in both the whole-unmatched sample, and a propensity score matched (PSM) subsample. 567 CHR-P individuals (306 male, mean[±SD] age = 22.3[±4.9] years) were included after data cleaning. The BDZ-exposed (n = 105) and BDZ-unexposed (n = 462) groups differed on several demographic and clinical characteristics, including psychotic symptom severity. In the whole-unmatched sample, BDZ exposure was associated with increased risk of transition to psychosis (HR = 1.61; 95% CI: 1.03-2.52; P = .037), psychiatric hospital admission (HR = 1.93; 95% CI: 1.13-3.29; P = .017), home visit (HR = 1.64; 95% CI: 1.18-2.28; P = .004), and Accident and Emergency department attendance (HR = 1.88; 95% CI: 1.31-2.72; P < .001). However, after controlling for confounding-by-indication through PSM, BDZ exposure did not modulate the risk of any outcomes (all P > .05). In an analysis restricted to antipsychotic-naïve individuals, BDZ exposure reduced the risk of transition to psychosis numerically, although this was not statistically significant (HR = 0.59; 95% CI: 0.32-1.08; P = .089). BDZ exposure in CHR-P individuals was not associated with a reduction in the risk of psychosis transition or adverse clinical outcomes. Results in the whole-unmatched sample suggest BDZ prescription may be more likely in CHR-P individuals with higher symptom severity.

Baseline antipsychotic prescription and short-term outcome indicators in individuals at clinical high-risk for psychosis: Findings from an Italian longitudinal study

IntroductionThe prognostic prediction of outcomes in individuals at clinical high-risk for psychosis (CHR-P) is still a significant clinical challenge. Among multiple baseline variables of risk calculator models, the role of ongoing pharmacological medications has been partially neglected, despite meta-analytical evidence of higher risk of psychosis transition associated with baseline prescription exposure to antipsychotics (AP) in CHR-P individuals. In particular, baseline AP exposure in CHR-P individuals may be considered as a functional equivalent of the psychometric transition to psychosis, as already postulated in the original ‘Ultra High-Risk’ model.ObjectivesThe main aim of the current study was to test the hypothesis that ongoing AP need at baseline indexes a subgroup of CHR-P individuals with more severe psychopathology and worse prognostic trajectories along a 1-year follow-up period.MethodsThis research was settled within the ‘Parma At-Risk Mental States’ program. Baseline and 1-year follow-up assessment included the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF). CHR-P individuals who were taking AP medications at entry were included in the CHR-P-AP+ subgroup. The remaining participants were grouped as CHR-P-AP-. The acquisition of drug and outcome information was collected both at baseline and across the follow-up period. Finally, logistic regression analyses with dichotomized 1-year outcome parameters (previously showing statistically significant differences in inter-group comparisons) as dependent measures and sociodemographic and clinical characteristics as independent variables were also performed.ResultsHundred and seventy-eight CHR-P individuals (aged 12–25 years) were enrolled (91 CHR-P-AP+, 87 CHR-P-AP-). Compared to CHR-P AP-, CHR-P AP+ individuals had older age, greater baseline PANSS ‘Positive Symptoms’ and ‘Negative Symptoms’ factor subscores and a lower GAF score. At the end of our follow-up, CHR-P-AP+ subjects showed higher rates of psychosis transition, new hospitalizations and urgent/non-planned visits compared to CHRP- AP- individuals.ConclusionsThe current study suggests that AP need is a significant prognostic variable in cohorts of CHR-P individuals and should be included in the current risk calculators. In particular, the results of this study conducted in a realworld clinical setting indicate that the rate of CHR-P individuals who were already exposed to AP at the time of CHR-P status ascription was higher than those reported in recent meta-analyses on this topic. Moreover, our findings confirm that baseline AP prescription appears to increase psychotic transition risk.Disclosure of InterestNone Declared

Exploring Associations between Grey Matter Volume and Clinical High-Risk for Psychosis: A Transdiagnostic Study Utilizing the NAPLS-2 Risk Calculator in the PRONIA Cohort

Introduction The clinical high-risk state for psychosis (CHR) is associated with alterations in grey matter volume (GMV) in various regions such as the hippocampus (Vissink et al. BP:GOS 2022; 2(2) 147-152). Within the scope of the North American Prodrome Longitudinal Study (NAPLS-2; Cannon et al. AM J Psychiatry 2016; 173(10), 980-988), a publicly available risk calculator based on clinical variables was developed to assess the likelihood of individuals to transition to psychosis within a 2-year period.Objectives In the current study, we aim to examine the association between GMV and NAPLS-2 risk scores calculated for individuals with CHR and recent-onset depression (ROD), taking a transdiagnostic approach on the transition to psychosis.Methods The sample consisted of 315 CHR (M = 23.85, SD = ± 5.64; female: 164) and 295 ROD (M = 25.11, SD = ± 6.21; female: 144) patients from the multi-site Personalised Prognostic Tools for Early Psychosis Management (PRONIA) Study (Koutsouleris et al. JAMA Psychiatry 2018; 57(11), 1156-1172). Risk scores were calculated using the six clinical and neurocognitive variables included in the NAPLS-2 risk calculator that were significant for predicting psychosis. Further, we derived smoothed GMV maps from T1-weighted structural magnetic resonance imaging using a full width at half maximum kernel size of 8 mm. We employed a multiple regression design in SPM12 to examine associations between risk scores and GMV. On the whole-brain level, we calculated permutation-based threshold-free cluster enhancement (TFCE) contrasts using the TFCE toolbox. Additionally, we calculated t-contrasts within a region-of-interest (ROI) analysis encompassing the hippocampus. All results were thresholded at p &lt; 0.05 with family wise error correction to address multiple comparisons.ResultsOur analysis revealed that linear GMV increases in the right middle and superior frontal gyrus (kE= 2726 voxels) were significantly associated with higher risk for psychosis transition within two years (see figure 1, highlighted in blue). In the ROI analysis, we found a significant negative linear association between GMV decreases in the left hippocampus (kE = 353 voxels) and higher risk for psychosis transition (see figure 1, highlighted in red).Image:ConclusionsGMV reductions in the hippocampus have frequently been observed in CHR and psychosis patients (Vissink et al. BP:GOS 2022; 2(2) 147-152), therefore our results further highlight the crucial role of this region in the progression of the disease. There is limited evidence on GMV increases in CHR patients. However, the GMV increase we found in the frontal pole may reflect compensatory mechanisms of the brain in the development of psychosis. In addition, we were able to provide biological validation of the NAPLS-2 risk calculator and its assessment of risk for transition to psychosis.Disclosure of InterestNone Declared

Subgroups of Clinical High Risk for Psychosis Based on Baseline Antipsychotic Exposure: Clinical and Outcome Comparisons Across a 2-Year Follow-up Period.

Antipsychotic (AP) prescription in clinical high risk for psychosis (CHR-P) subjects remains a divisive issue. Although official guidelines currently discourage AP treatment in CHR-P, it is common in clinical practice, especially for psychosis prevention. The aim of this study was to investigate whether baseline AP need (especially in high-dose) indexes a CHR-P subgroup with poorer prognosis and differs from AP-naïve subjects in terms of sociodemographic, clinical, and outcome parameters across a 2-year follow-up. CHR-P participants were treated within an "Early Intervention in Psychosis" program and completed the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF) scale both at baseline and every 12 months. Individuals with baseline AP prescription were included in the high-dose or low-dose CHR-P-AP+ subgroup. The others were grouped as AP-naïve. Cox regression analyses and mixed-design ANOVA were performed. 180 CHR-P individuals were enrolled (32 high-dose, 60 low-dose, and 88 AP-naïve). Compared to AP-naive, CHR-P AP+ subgroups showed older age and more severe clinical presentation. High-dose subgroup also had grater functioning decline at entry and poorer functional recovery at follow-up. No inter-group differences in psychosis transition and symptomatic remission were found. Significant improvement in clinical outcomes were found over time in all subgroups. Baseline AP prescription was specifically associated with a more relevant improvement in PANSS total score, and in negative and disorganized symptoms. Our results suggest that baseline AP need is an important prognostic parameter in CHR-P and should be considered in risk/benefit calculators.

Prediction of psychotic disorder in individuals with clinical high-risk state by multimodal machine-learning: A preliminary study

Objective markers which can reliably predict psychosis transition among individuals with at-risk mental state (ARMS) are warranted. In this study, sixty-five ARMS subjects [of whom 17 (26.2%) later developed psychosis] were recruited, and we performed supervised linear support vector machine (SVM) with a variety of combinations of.modalities (clinical features, cognition, structural magnetic resonance imaging, eventrelated.potentials, and polyunsaturated fatty acids) to predict future psychosis onset. While single-modality SVMs showed a poor to fair accuracy, multi-modal SVMs revealed better predictions, up to 0.88 of the balanced accuracy, suggesting the advantage of multi-modal machine-learning methods for forecasting psychosis onset in ARMS.

Adverse outcome analysis in people at clinical high risk for psychosis: results from a 2-year Italian follow-up study.

Since January 2016, the Parma Department of Mental Health (in Italy) developed a specialized care program for Early Intervention (EI) in individuals at Clinical High Risk for Psychosis (CHR-P). As unfavorable outcomes other than transition to psychosis were not systematically reported in the current literature (thereby compromising more sophisticated prognostic stratifications), the aims of this research were (1) to investigate adverse outcome indicators (i.e., service disengagement, psychosis transition, hospitalization, prolonged functioning impairment, prolonged persistence of CHR-P criteria, suicide attempts) in an Italian CHR-P population enrolled within a specialized EI service across a 2-year follow-up period, and (2) to examine their relevant associations with sociodemographic and clinical characteristics of the CHR-P total sample at baseline. All participants were young CHR-P help-seekers aged 12-25years. They completed the "Comprehensive Assessment of At-Risk Mental States" (CAARMS) and the Health of the Nation Outcome Scale (HoNOS). Both univariate and multivariate Cox regression analyses were performed. 164 CHR-P individuals were enrolled in this study. Across the follow-up, 30 (18.0%) dropped out the EI program, 23 (14%) transitioned to psychosis, 24 (14.6%) were hospitalized, 23 (14%) had a prolonged persistence of CHR-P criteria and 54 (47%) showed prolonged impairment in socio-occupational functioning. As almost half of our participants did not functionally remit over time, sustained clinical attention for young CHR individuals people should be offered in the longer term, also to monitor unfavorable outcomes and to improve long-term prognosis.

The SPEAK study rationale and design: A linguistic corpus-based approach to understanding thought disorder.

Psychotic symptoms are typically measured using clinical ratings, but more objective and sensitive metrics are needed. Hence, we will assess thought disorder using the Research Domain Criteria (RDoC) heuristic for language production, and its recommended paradigm of "linguistic corpus-based analyses of language output". Positive thought disorder (e.g., tangentiality and derailment) can be assessed using word-embedding approaches that assess semantic coherence, whereas negative thought disorder (e.g., concreteness, poverty of speech) can be assessed using part-of-speech (POS) tagging to assess syntactic complexity. We aim to establish convergent validity of automated linguistic metrics with clinical ratings, assess normative demographic variance, determine cognitive and functional correlates, and replicate their predictive power for psychosis transition among at-risk youths. This study will assess language production in 450 English-speaking individuals in Australia and Canada, who have recent onset psychosis, are at clinical high risk (CHR) for psychosis, or who are healthy volunteers, all well-characterized for cognition, function and symptoms. Speech will be elicited using open-ended interviews. Audio files will be transcribed and preprocessed for automated natural language processing (NLP) analyses of coherence and complexity. Data analyses include canonical correlation, multivariate linear regression with regularization, and machine-learning classification of group status and psychosis outcome. This prospective study aims to characterize language disturbance across stages of psychosis using computational approaches, including psychometric properties, normative variance and clinical correlates, important for biomarker development. SPEAK will create a large archive of language data available to other investigators, a rich resource for the field.

Omega-3 index as risk factor in psychiatric diseases: a narrative review.

Numerous studies have described associations between the omega-3 index (defined as the RBC percentage of EPA and DHA) and mental conditions, but no risk stratification or target value has gathered consensus so far. This narrative review aims to summarize the published data on the association between omega-3 index and mental illness and to contribute to the concept of an omega-3 index in the field of mental health. The bibliographic searches have been carried out in PubMed, Scopus and Web of Science databases to find relevant English language original research studies related to that association. The study search and selection process were registered in a PRISMA flow. Thirty-six studies were included in this review examining the links between omega-3 index and postpartum depression (3), major depression (15), major depression and bipolar disorder (1), bipolar disorder (4), schizophrenia and major depression (1), schizophrenia and other psychosis (5) and dementia (7). Thirty of these studies found either significant differences in omega-3 index between patients and controls or inverse relationships between omega-3 index and disease severity. The published evidence is compelling enough to suggest omega-3 index as a risk factor for some psychiatric diseases, specifically, major depression, postpartum depression, psychosis, and dementia. In occidental populations, we propose a risk threshold of (a) 4-5% in major depression and dementia, (b) 5% in postpartum depression, and (c) 4% for psychosis transition.

Baseline antipsychotic prescription and short-term outcome indicators in individuals at clinical high-risk for psychosis: Findings from the Parma At-Risk Mental States (PARMS) program.

The prognostic prediction of outcomes in individuals at clinical high-risk for psychosis (CHR-P) is still a significant clinical challenge. Among multiple baseline variables of risk calculator models, the role of ongoing pharmacological medications has been partially neglected, despite meta-analytical evidence of higher risk of psychosis transition associated with baseline prescription exposure to antipsychotics (AP) in CHR-P individuals. The main aim of the current study was to test the hypothesis that ongoing AP need at baseline indexes a subgroup of CHR-P individuals with more severe psychopathology and worse prognostic trajectories along a 1-year follow-up period. This research was settled within the 'Parma At-Risk Mental States' program. Baseline and 1-year follow-up assessment included the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF). CHR-P individuals who were taking AP medications at entry were included in the CHR-P-AP+ subgroup. The remaining participants were grouped as CHR-P-AP-. Hundred and seventy-eight CHR-P individuals (aged 12-25 years) were enrolled (91 CHR-P-AP+, 87 CHR-P-AP-). Compared to CHR-P AP-, CHR-P AP+ individuals had older age, greater baseline PANSS 'Positive Symptoms' and 'Negative Symptoms' factor subscores and a lower GAF score. At the end of our follow-up, CHR-P-AP+ subjects showed higher rates of psychosis transition, new hospitalizations and urgent/non-planned visits compared to CHRP- AP- individuals. In agreement with increasing empirical evidence, also the results of the current study suggest that AP need is a significant prognostic variable in cohorts of CHR-P individuals and should be included in risk calculators.

Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data.

Psychosis is usually preceded by a prodromal phase in which patients are clinically identified as being at in an "At Risk Mental State" (ARMS). A few studies have demonstrated the feasibility of predicting psychosis transition from an ARMS using structural magnetic resonance imaging (sMRI) data and machine learning (ML) methods. However, the reliability of these findings is unclear due to possible sampling bias. Moreover, the value of genetic and environmental data in predicting transition to psychosis from an ARMS is yet to be explored. In this study we aimed to predict transition to psychosis from an ARMS using a combination of ML, sMRI, genome-wide genotypes, and environmental risk factors as predictors, in a sample drawn from a pool of 246 ARMS subjects (60 of whom later transitioned to psychosis). First, the modality-specific values in predicting transition to psychosis were evaluated using several: (a) feature types; (b) feature manipulation strategies; (c) ML algorithms; (d) cross-validation strategies, as well as sample balancing and bootstrapping. Subsequently, the modalities whose at least 60% of the classification models showed an balanced accuracy (BAC) statistically better than chance level were included in a multimodal classification model. Results showed that none of the modalities alone, i.e., neuroimaging, genetic or environmental data, could predict psychosis from an ARMS statistically better than chance and, as such, no multimodal classification model was trained/tested. These results suggest that the value of structural MRI data and genome-wide genotypes in predicting psychosis from an ARMS, which has been fostered by previous evidence, should be reconsidered.

Linguistic markers and basic self-disturbances among adolescents at risk of psychosis. A qualitative study.

Language impairments are key features of schizophrenia spectrum disorders, and have also been suggested to signal enhanced psychosis risk. Incoherence, derailment, and monotonous speaking are however closely related to psychosis onset, and thus not very early markers. Recent phenomenologic-psychiatric studies claim that basic self-disturbance (BSD) may represent more useful early markers. We searched for distinctive irregular linguistics of 30 CHR outpatient adolescents, aged 12-18 years. Standard instruments established psychosis risk and BSD. Participants chose three personal and well manifested BSD phenomena. Ninety verbatim statements were analyzed and grouped into higher order clusters of linguistic irregularities. We identified five clusters of irregular language features: distinctive words, describing an atmosphere of unreality; irregular use of prepositions, indicating experiential detachment; shifts of personal pronouns, indicating identity confusion; near-literal use of metaphors and conjunctions indicating existential insecurity, and idiosyncratic use of adjectives indicating perceptual transcendence. The adolescents provided naturalistic descriptions of experiences that were markedly twisted and almost ineffable. This unique irregular "BSD -language" was highly meaningful in its proper context, expressing informative characteristics of first-personal experiential alterations, essential for early detection. The features may additionally represent precursors of psychosis transition, useful for clinical decision-making. Foundation Dam, Oslo, Norway (Grant Number 2017/FO143368).

Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the ‘normativeness’ of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.

Eye movement indices as predictors of conversion to psychosis in individuals at clinical high risk.

Eye movement abnormalities have been established as an "endophenotype" of schizophrenia. However, less is known about the possibility of these abnormalities as biomarkers for psychosis conversion among clinical high risk (CHR) populations. In the present study, 108 CHR individuals and 70 healthy controls (HC) underwent clinical assessments and eye-tracking tests, comprising fixation stability and free-viewing tasks. According to three-year follow-up outcomes, CHR participants were further stratified into CHR-converter (CHR-C; n = 21) and CHR-nonconverter (CHR-NC; n = 87) subgroups. Prediction models were constructed using Cox regression and logistic regression. TheCHR-C group showed more saccades of the fixation stability test (no distractor) and a reduced saccade amplitude of the free-viewing test than HC. Moreover, the CHR-NC group exhibited excessive saccades and an increased saccade amplitude of the fixation stability test (no distractor; with distractor) compared with HC. Furthermore, two indices could effectively discriminate CHR-C from CHR-NC with an area under the receiver-operating characteristic (ROC) curve of 0.80, including the saccade number of the fixation stabilitytest (no distractor) and thesaccade amplitude of the free-viewing test. Combined with negative symptom scores of the Scale of Prodromal Symptoms, the area was 0.81. These findings support that eye movement alterations might emerge before the onset of clinically overt psychosis and could assist in predicting psychosis transition among CHR populations.

Global fractional anisotropy predicts transition to psychosis after 12months in individuals at ultra-high risk for psychosis.

Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR). 110 UHR individuals underwent 3Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression. Ten UHR individuals had transitioned to psychosis after 12months (9%). The model reliably predicted transition at 12months (χ2 =17.595, p=0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88 and AUC of 0.87. Global FA predicted level of UHR symptoms (R2 =0.055, F=6.084, p=0.016) and functional level (R2 =0.040, F=4.57, p=0.036) at 6months, but not at 12months. Global FA provided prognostic information on clinical outcome and symptom course of UHR individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition.

Cognitive Behavioral Therapy for Prodromal Stage of Psychosis-Outcomes for Transition, Functioning, Distress, and Quality of Life: A Systematic Review and Meta-analysis.

This study aimed to provide insight into the efficacy of cognitive-behavioral therapy for psychosis (CBTp) in patients with "clinical high risk of psychosis (CHR-P)". Major scientific databases were searched up to April 17, 2020. Randomized controlled trials in CHR-P individuals, comparing CBTp with needs-based interventions (NBI, including treatment as usual or nonspecific control treatment) were included, following PRISMA guidelines. The primary outcome (efficacy) was transition to psychosis by 6 months, 12 months, 24 months, and over 24 months. Secondary outcomes were change in attenuated psychotic symptoms, depression, distress, improvements in functioning, and quality of life. Ten randomized controlled studies met inclusion criteria. The comparisons included 1128 participants. CBTp was significantly more efficacious in reducing rate of transition to psychosis by 6 months (after post-hoc sensitivity analysis) (relative risk [RR] = 0.44, 95% confidence interval [CI]: 0.26, 0.73), 12 months (RR = 0.44, 95% CI: 0.30, 0.64), 12 months (RR = 0.46, 95%CI: 0.30, 0.69), and over 24 months (RR = 0.58, 95% CI: 0.35, 0.95) after treatment, compared with those receiving NBI. CBTp was also associated with more reduced attenuated psychotic symptoms by 12 months (SMD = -0.17, 95% CI: -0.33, -0.02) and by 24 months (SMD = -0.24, 95% CI: -0.43, -0.06). No beneficial effects on functioning, depression, quality of life, or distress were observed favoring CBTp. CBTp is effective in reducing both psychosis transition rates and attenuated psychotic symptoms for the prodromal stage of psychosis. It is a promising intervention at the preventative stage.

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

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. 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. 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. Accuracy and generalizability of prognostic systems. 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. 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.