Psychosis superspectrum I: Nosology, etiology, and lifespan development.

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

Characterizing the acute antibody response of monkeypox and MVA-BN vaccine following an Australian outbreak.

In response to the emergence of the monkeypox virus (MPXV) in Australia in May 2022, we developed and evaluated indirect immunofluorescence assays (IFA) for MPXV and Vaccinia virus (VACV) IgG and IgM antibodies using serum samples from patients with nucleic acid amplification test (NAAT)-confirmed mpox and uninfected unvaccinated controls. Additionally, 47 healthcare workers receiving two doses of the third-generation smallpox vaccine Modified Vaccinia Ankara-Bavarian Nordic (MVA-BN) undertook serial serum collection to describe the serological response to vaccination. MPXV antibodies were detected in 16/18 individuals with NAAT-confirmed mpox (sensitivity 0.89, specificity 1.00), and VACV antibodies were detected in 28/29 individuals who received two doses of MVA-BN vaccine (sensitivity 0.97, specificity 1.00). Detectable antibody in subjects historically vaccinated with early-generation vaccines against smallpox was found in 7/7 subjects, at a median of 48 years following vaccination. MPXV NAAT-positive patients with serum samples collected within the first 14 days after rash onset had detectable IgG and IgM in 9/12 and 5/12 of patients, respectively, with maintenance of IgG and disappearance of IgM titers after 60 days. While specificity was high when testing unvaccinated and uninfected subjects, significant cross-reactivity between MPXV and VACV antibodies was observed.

Variation in the effectiveness of reinforcement and nonreinforcement in generating different conditioned behaviors.

Rat autoshaping procedures generate two readily measurable conditioned responses: During lever presentations that have previously signaled food, rats approach the food well (called goal-tracking) and interact with the lever itself (called sign-tracking). We investigated how reinforced and nonreinforced trials affect the overall and temporal distributions of these two responses across 10-second lever presentations. In two experiments, reinforced trials generated more goal-tracking than sign-tracking, and nonreinforced trials resulted in a larger reduction in goal-tracking than sign-tracking. The effect of reinforced trials was evident as an increase in goal-tracking and reduction in sign-tracking across the duration of the lever presentations, and nonreinforced trials resulted in this pattern transiently reversing and then becoming less evident with further training. These dissociations are consistent with a recent elaboration of the Rescorla-Wagner model, HeiDI (Honey, R.C., Dwyer, D.M., & Iliescu, A.F. (2020a). HeiDI: A model for Pavlovian learning and performance with reciprocal associations. Psychological Review, 127, 829-852.), a model in which responses related to the nature of the unconditioned stimulus (e.g., goal-tracking) have a different origin than those related to the nature of the conditioned stimulus (e.g., sign-tracking).

Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis.

BACKGROUND: Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce. AIMS: To evaluate fairness in prediction models for development of psychosis and functional outcome. METHOD: Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements. RESULTS: Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions. CONCLUSIONS: Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Longitudinal Characterization of Phagocytic and Neutralization Functions of Anti-Spike Antibodies in Plasma of Patients after Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Phagocytic responses by effector cells to opsonized viruses have been recognized to play a key role in antiviral immunity. Limited data on coronavirus disease 2019 suggest that the role of Ab-dependent and -independent phagocytosis may contribute to the observed immunological and inflammatory responses; however, their development, duration, and role remain to be fully elucidated. In this study of 62 acute and convalescent patients, we found that patients with acute coronavirus disease 2019 can mount a phagocytic response to autologous plasma-opsonized Spike protein-coated microbeads as early as 10 d after symptom onset, while heat inactivation of this plasma caused 77-95% abrogation of the phagocytic response and preblocking of Fc receptors showed variable 18-60% inhibition. In convalescent patients, phagocytic response significantly correlated with anti-Spike IgG titers and older patients, while patients with severe disease had significantly higher phagocytosis and neutralization functions compared with patients with asymptomatic, mild, or moderate disease. A longitudinal subset of the convalescent patients over 12 mo showed an increase in plasma Ab affinity toward Spike Ag and preservation of phagocytic and neutralization functions, despite a decline in the anti-Spike IgG titers by >90%. Our data suggest that early phagocytosis is primarily driven by heat-liable components of the plasma, such as activated complements, while anti-Spike IgG titers account for the majority of observed phagocytosis at convalescence. Longitudinally, a significant increase in the affinity of the anti-Spike Abs was observed that correlated with the maintenance of both the phagocytic and neutralization functions, suggesting an improvement in the quality of the Abs.

New Detection of Locally Acquired Japanese Encephalitis Virus Using Clinical Metagenomics, New South Wales, Australia.

In the context of an emerging Japanese encephalitis outbreak within Australia, we describe a novel locally acquired case in New South Wales. A man in his 70s had rapidly progressive, fatal meningoencephalitis, diagnosed as caused by Japanese encephalitis virus by RNA-based metagenomic next-generation sequencing performed on postmortem brain tissue.

Placental endocrine insufficiency programs anxiety, deficits in cognition and atypical social behaviour in offspring.

Abnormally elevated expression of the imprinted PHLDA2 gene has been reported in the placenta of human babies that are growth restricted in utero in several studies. We previously modelled this gene alteration in mice and found that just 2-fold increased expression of Phlda2 resulted in placental endocrine insufficiency. In addition, elevated Phlda2 was found to drive fetal growth restriction (FGR) of transgenic offspring and impaired maternal care by their wildtype mothers. Being born small and being exposed to suboptimal maternal care have both been associated with the increased risk of mental health disorders in human populations. In the current study we probed behavioural consequences of elevated Phlda2 for the offspring. We discovered increased anxiety-like behaviours, deficits in cognition and atypical social behaviours, with the greatest impact on male offspring. Subsequent analysis revealed alterations in the transcriptome of the adult offspring hippocampus, hypothalamus and amygdala, regions consistent with these behavioural observations. The inclusion of a group of fully wildtype controls raised in a normal maternal environment allowed us to attribute behavioural and molecular alterations to the adverse maternal environment induced by placental endocrine insufficiency rather than the specific gene change of elevated Phlda2. Our work demonstrates that a highly common alteration reported in human FGR is associated with negative behavioural outcomes later in life. Importantly, we also establish the experimental paradigm that placental endocrine insufficiency can program atypical behaviour in offspring highlighting the under-appreciated role of placental endocrine insufficiency in driving disorders of later life behaviour.

High activation levels maintained in receptor-binding domain-specific memory B cells in people with severe coronavirus disease 2019.

The long-term health consequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are still being understood. The molecular and phenotypic properties of SARS-CoV-2 antigen-specific T cells suggest a dysfunctional profile that persists in convalescence in those who were severely ill. By contrast, the antigen-specific memory B-cell (MBC) population has not yet been analyzed to the same degree, but phenotypic analysis suggests differences following recovery from mild or severe coronavirus disease 2019 (COVID-19). Here, we performed single-cell molecular analysis of the SARS-CoV-2 receptor-binding domain (RBD)-specific MBC population in three patients after severe COVID-19 and four patients after mild/moderate COVID-19. We analyzed the transcriptomic and B-cell receptor repertoire profiles at ~2 months and ~4 months after symptom onset. Transcriptomic analysis revealed a higher level of tumor necrosis factor-alpha (TNF-α) signaling via nuclear factor-kappa B in the severe group, involving CD80, FOS, CD83 and TNFAIP3 genes that was maintained over time. We demonstrated the presence of two distinct activated MBCs subsets based on expression of CD80hi TNFAIP3hi and CD11chi CD95hi at the transcriptome level. Both groups revealed an increase in somatic hypermutation over time, indicating progressive evolution of humoral memory. This study revealed distinct molecular signatures of long-term RBD-specific MBCs in convalescence, indicating that the longevity of these cells may differ depending on acute COVID-19 severity.

dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning.

MOTIVATION: In multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates this differentiation through the simultaneous learning of prediction tasks across cohorts. Since multi-cohort data can often not be combined into a single storage solution, there would be the substantial utility of an MTL application for geographically distributed data sources. RESULTS: Here, we describe the development of 'dsMTL', a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. First, we derive the theoretical properties of these methods and the relevant machine learning workflows to ensure the validity of the software implementation. Second, we implement dsMTL as a library for the R programming language, building on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data. Third, we demonstrate the applicability of dsMTL for comorbidity modeling in distributed data. We show that comorbidity modeling using dsMTL outperformed conventional, federated machine learning, as well as the aggregation of multiple models built on the distributed datasets individually. The application of dsMTL was computationally efficient and highly scalable when applied to moderate-size (n < 500), real expression data given the actual network latency. AVAILABILITY AND IMPLEMENTATION: dsMTL is freely available at https://github.com/transbioZI/dsMTLBase (server-side package) and https://github.com/transbioZI/dsMTLClient (client-side package). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Risk of a first clinical diagnosis of central nervous system demyelination in relation to human herpesviruses in the context of Epstein-Barr virus.

BACKGROUND AND PURPOSE: Epstein-Barr virus (EBV) is implicated in multiple sclerosis (MS) risk; evidence for other herpesviruses is inconsistent. Here, we test blood markers of infection with human herpesvirus 6 (HHV-6), varicella zoster virus (VZV), and cytomegalovirus (CMV) as risk factors for a first clinical diagnosis of central nervous system demyelination (FCD) in the context of markers of EBV infection. METHODS: In the Ausimmune case-control study, cases had an FCD, and population controls were matched on age, sex, and study region. We quantified HHV-6- and VZV-DNA load in whole blood and HHV-6, VZV, and CMV antibodies in serum. Conditional logistic regression tested associations with FCD risk, adjusting for Epstein-Barr nuclear antigen (EBNA) IgG, EBV-DNA load, and other covariates. RESULTS: In 204 FCD cases and 215 matched controls, only HHV-6-DNA load (positive vs. negative) was associated with FCD risk (adjusted odds ratio = 2.20, 95% confidence interval = 1.08-4.46, p = 0.03). Only EBNA IgG and HHV-6-DNA positivity were retained in a predictive model of FCD risk; the combination had a stronger association than either alone. CMV-specific IgG concentration modified the association between an MS risk-related human leucocyte antigen gene and FCD risk. Six cases and one control had very high HHV-6-DNA load (>1.0 × 106 copies/mL). CONCLUSIONS: HHV-6-DNA positivity and high load (possibly due to inherited HHV-6 chromosomal integration) were associated with increased FCD risk, particularly in association with markers of EBV infection. With growing interest in prevention/management of MS through EBV-related pathways, there should be additional consideration of the role of HHV-6 infection.

SABRes: in silico detection of drug resistance conferring mutations in subpopulations of SARS-CoV-2 genomes.

The emergence of resistance to antiviral drugs increasingly used to treat SARS-CoV-2 infections has been recognised as a significant threat to COVID-19 control. In addition, some SARS-CoV-2 variants of concern appear to be intrinsically resistant to several classes of these antiviral agents. Therefore, there is a critical need for rapid recognition of clinically relevant polymorphisms in SARS-CoV-2 genomes associated with significant reduction of drug activity in virus neutralisation experiments. Here we present SABRes, a bioinformatic tool, which leverages on expanding public datasets of SARS-CoV-2 genomes and allows detection of drug resistance mutations in consensus genomes as well as in viral subpopulations. We have applied SABRes to detect resistance-conferring mutations in 25,197 genomes generated over the course of the SARS-CoV-2 pandemic in Australia and identified 299 genomes containing resistance conferring mutations to the five antiviral therapeutics that retain effectiveness against currently circulating strains of SARS-CoV-2 - Sotrovimab, Bebtelovimab, Remdesivir, Nirmatrelvir and Molnupiravir. These genomes accounted for a 1.18% prevalence of resistant isolates discovered by SABRes, including 80 genomes with resistance conferring mutations found in viral subpopulations. Timely recognition of these mutations within subpopulations is critical as these mutations can provide an advantage under selective pressure and presents an important step forward in our ability to monitor SARS-CoV-2 drug resistance.

Novel Gyrification Networks Reveal Links with Psychiatric Risk Factors in Early Illness.

Adult gyrification provides a window into coordinated early neurodevelopment when disruptions predispose individuals to psychiatric illness. We hypothesized that the echoes of such disruptions should be observed within structural gyrification networks in early psychiatric illness that would demonstrate associations with developmentally relevant variables rather than specific psychiatric symptoms. We employed a new data-driven method (Orthogonal Projective Non-Negative Matrix Factorization) to delineate novel gyrification-based networks of structural covariance in 308 healthy controls. Gyrification within the networks was then compared to 713 patients with recent onset psychosis or depression, and at clinical high-risk. Associations with diagnosis, symptoms, cognition, and functioning were investigated using linear models. Results demonstrated 18 novel gyrification networks in controls as verified by internal and external validation. Gyrification was reduced in patients in temporal-insular, lateral occipital, and lateral fronto-parietal networks (pFDR < 0.01) and was not moderated by illness group. Higher gyrification was associated with better cognitive performance and lifetime role functioning, but not with symptoms. The findings demonstrated that gyrification can be parsed into novel brain networks that highlight generalized illness effects linked to developmental vulnerability. When combined, our study widens the window into the etiology of psychiatric risk and its expression in adulthood.

The Origins of Severe Acute Respiratory Syndrome-Coronavirus-2.

An outbreak of severe pneumonia of unknown cause was identified in Wuhan, China in December 2019: the causative agent was a novel betacoronavirus, severe acute respiratory syndrome-cotonavirus-2 (SARS-CoV-2), a virus that joins a list of coronaviruses causing severe (e.g., SARS and Middle East respiratory syndrome) or milder (e.g., 229E, OC43, NL63, and HKU1) respiratory tract infection. The World Health Organization (WHO) classified the spreading outbreak as a pandemic on March 11, 2020. Many SARS-related coronaviruses (SARSr-CoVs) have been identified in bats, particularly in Rhinolophus horseshoe bats, animals that are common in southern China and Southeast Asia. Many of the features of SARS-CoV-2 that facilitate human infection-the furin cleavage site, the receptor binding domain that binds to the human ACE2 receptor-can be identified in SARSr-CoVs. Related coronaviruses can be detected in pangolins and other animals, and human SARS-CoV-2 itself can infect various animals, some of which can transmit SARS-CoV-2 back to humans. Investigation by the WHO and others pointed to the initial outbreak being centered on the Huanan wet market in Wuhan where wild and farmed animals were sold, and where environmental testing revealed widespread SARS-CoV-2 contamination. This supports the hypothesis that bats, probably via an intermediate animal, are the origin of SARS-CoV-2. Other possible origins have been postulated, such as an accidental or deliberate laboratory leak, or virus present in frozen foods, but evidence for these ideas has not surfaced. Study of the origins of SARS-CoV-2 have been complicated by intense media and political commentary, features that may slow the studies required to understand the viral origins. Such studies are complex and may be slow: international openness and co-operation is vital. Origins explanations are needed to predict or prevent future pandemics and support the "One Health" approach to disease.

Fearfulness: An important addition to the starter kit for distinctively human minds.

Grossmann's impressive article indicates that - along with attentional biases, expansion of domain-general processes of learning and memory, and other temperamental tweaks - heightened fearfulness is part of the genetic starter kit for distinctively human minds. The learned matching account of emotional contagion explains how heightened fearfulness could have promoted the development of caring and cooperation in our species.

Five points to consider when reading a translational machine-learning paper.

Machine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are helpful for a new reader to consider in order to engage with the field and evaluate the literature. These points include the consideration of why we are interested in new statistical approaches, how they may produce individualised predictions, what caveats need to be kept in-mind and why the interest and engagment of clinicians and clinical researchers is critical to successful model development and implementation. We hope that the following primer will provide shared understanding to encourage dialogue between clinical and methodological fields.

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.

Annual Research Review: Translational machine learning for child and adolescent psychiatry.

Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.

The clinical relevance of formal thought disorder in the early stages of psychosis: results from the PRONIA study.

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.