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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 (p fdr < 0.001) and role (p fdr < 0.001) functioning, as well as worse neurocognitive performance in semantic (p fdr < 0.001) and phonological verbal fluency (p fdr < 0.001), short-term verbal memory (p fdr = 0.002) and abstract thinking (p fdr = 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.

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.

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

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: chi 2 = 14.874; P < .001; GMV model: chi 2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: chi 2 = 1.956; P = 0.162; GMV model: chi 2 = 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 interve

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.

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.

Increased striatal dopaminergic activity and decreased prefrontal functioning have been reported in individuals at clinical high risk (CHR) for psychosis. Abnormal metabolic rate might affect resting-state cerebral blood flow (rCBF) in the respective regions. Here, we examined if striatal and prefrontal rCBF differ between patients with CHR, first-episode psychosis (FEP), chronic schizophrenia-spectrum disorder (SZ) and controls. Two cohorts with a total of 122 participants were included and analyzed separately: 32 patients with SZ and 31 healthy controls (HC) from the University Hospital of Psychiatry, and 59 patients from the Bern Early Recognition and Intervention Center (29 with CHR, 12 with FEP, and 18 clinical controls [CC]). Ultra-high risk criteria were assessed with the Structured Interview for Psychosis-Risk Syndromes, basic symptom criteria with the Schizophrenia Proneness Instrument. rCBF was measured with pseudo-continuous arterial spin labeling 3T-Magnetic Resonance Imaging. Striatal rCBF was significantly increased and prefrontal rCBF significantly decreased in the SZ group compared to HC group and in the CHR and FEP groups compared to CC group. Striatal rCBF correlated significantly with positive symptom scores in SZ and CHR. An inverse correlation between striatal and frontal rCBF was found in controls (HC, CC), but not in patient groups (SZ, FEP, CHR). This is the first study to demonstrate increased neuronal activity within the striatum, but reduced prefrontal activity in patients with CHR, FEP, and SZ compared to the respective controls. Our results indicate that alterations in striatal and prefrontal rCBF are reflecting metabolic abnormalities preceding the onset of frank psychosis.

Purpose: Prevention of psychosis requires both presence of clinical high risk (CHR) criteria and early help-seeking. Previous retrospective studies of the duration of untreated illness (i.e. prodrome plus psychosis) did not distinguish between prodromal states with and without CHR symptoms. Therefore, we examined the occurrence of CHR symptoms and first help-seeking, thereby considering effects of age at illness-onset. Methods: Adult patients first admitted for psychosis (n=126) were retrospectively assessed for early course of illness and characteristics of first help-seeking. Results: One-hundred and nine patients reported a prodrome, 58 with CHR symptoms. In patients with an early illness-onset before age 18 (n=45), duration of both illness and psychosis were elongated, and CHR symptoms more frequent (68.9 vs. 33.3%)compared to those with adult illness-onset. Only 29 patients reported help-seeking in the prodrome; this was mainly self-initiated, especially in patients with an early illness-onset. After the onset of first psychotic symptoms, help-seeking was mainly initiated by others. State- and age-independently, mental health professionals were the main first point-of-call (54.0%). Conclusions: Adult first-admission psychosis patients with an early, insidious onset of symptoms before age 18 were more likely to recall CHR symptoms as part of their prodrome. According to current psychosis-risk criteria, these CHR symptoms, in principle, would have allowed the early detection of psychosis. Furthermore, compared to patients with an adult illness-onset, patients with an early illness-onset were also more likely to seek help on their own account. Thus, future awareness strategies to improve CHR detection might be primarily related to young persons and self-perceived subtle symptoms.