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OBJECTIVE: Verbal communication has key information for mental health evaluation. Researchers have linked psychopathology phenomena to some of their counterparts in natural-language-processing (NLP). We study the characterization of subtle impairments presented in early stages of psychosis, developing new analysis techniques and a comprehensive map associating NLP features with the full range of clinical presentation. METHODS: We used NLP to assess elicited and free-speech of 60 individuals in at-risk-mental-states (ARMS) and 73 controls, screened from 4,500 quota-sampled Portuguese speaking citizens in Sao Paulo, Brazil. Psychotic symptoms were independently assessed with Structured-Interview-for-Psychosis-Risk-Syndromes (SIPS). Speech features (e.g.sentiments, semantic coherence), including novel ones, were correlated with psychotic traits (Spearman's-ρ) and ARMS status (general linear models and machine-learning ensembles). RESULTS: NLP features were informative inputs for classification, which presented 86% balanced accuracy. The NLP features brought forth (e.g. Semantic laminarity as 'perseveration', Semantic recurrence time as 'circumstantiality', average centrality in word repetition graphs) carried most information and also presented direct correlations with psychotic symptoms. Out of the standard measures, grammatical tagging (e.g. use of adjectives) was the most relevant. CONCLUSION: Subtle speech impairments can be grasped by sensitive methods and used for ARMS screening. We sketch a blueprint for speech-based evaluation, pairing features to standard thought disorder psychometric items.

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Determining peripheral modulation of the endocannabinoid system (ECS) may be important for differentiating individuals with schizophrenia. Such differentiation can also be extended to subgroups of individuals, those who use cannabis and antipsychotic medications, particularly those who are treatment resistant. Patients and controls were recruited from the outpatient clinic of the Psychosis Group of the University of São Paulo, Brazil. A final sample of 93 individuals was divided into 3 groups: patients with schizophrenia using clozapine (treatment-resistant) (n = 29), patients with schizophrenia using another antipsychotic (n = 31), and controls (n = 33). By measuring the proteins and metabolites involved in the ECS pathways in the peripheral blood, AEA (anandamide), 2-AG (2-arachidonoyl ethanolamine), and CB2 receptor (peripheral) were quantified. Individuals reporting lifetime cannabis use had lower 2-AG plasma levels (p = 0.011). Regarding the CB2 receptor, the values of patients with schizophrenia and controls were similar, but those of patients using antipsychotics other than clozapine differed (p = 0.022). In generalized linear models to control for confounders, the use of cannabis remained the only factor that significantly influenced 2-AG levels. The relationship for non-clozapine antipsychotics as the only factor related to CB2 changes was marginally significant. We found for the first time that cannabis use and non-clozapine antipsychotic medication are potentially involved in the modulation of the ECS, specifically influencing 2-AG endocannabinoid and CB2 receptor levels. More studies regarding the ECS are needed since it has been increasingly related to the physiopathology of schizophrenia.

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BACKGROUND: Cannabis use is associated with an increased risk of developing a psychotic disorder. However, in individuals with at-risk mental states for psychosis (ARMS) this association is not clear, as well as the impact of cannabis use on symptom severity. The objective of this study was to evaluate the association of cannabis use patterns and ARMS risk status, transition to psychotic and psychiatric disorders, and psychopathology. METHOD: A sample of 109 ARMS and 197 control individuals was drawn from the general population. Lifetime, maximum and current amount of cannabis use were assessed with the South Westminster modified questionnaire. Participants were followed-up for a mean of 2.5 years and reassessed for transition to any psychiatric disorder. RESULTS: There were no differences between ARMS and controls regarding lifetime use, current amount of use, or maximum amount of cannabis use. There were also no differences between those who transitioned to a psychiatric disorder and those who did not regarding cannabis use variables. In ARMS individuals, cannabis use was significantly related to disorganization symptoms. CONCLUSION: The results of this study suggest that cannabis plays a role in the psychopathology of ARMS individuals, leading to more severe symptomatology.

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BACKGROUND: Neurotrophins (NTs) and their precursors (pro-NTs) are polypeptides with important roles in neuronal development, differentiation, growth, survival and plasticity, as well as apoptosis and neuronal death. Imbalance in NT levels were observed in schizophrenia spectrum disorders, but evidence in ultra-high risk for psychosis (UHR) samples is scarce. METHODS: A naturalistic sample of 87 non-help-seeking UHR subjects and 55 healthy controls was drawn from the general population. Blood samples were collected and NT-3, NT-4/5, BDNF, pro-BDNF, NGF, pro-NGF were analyzed through enzyme linked immunosorbent assay (ELISA). Information on cannabis and tobacco use was also collected. Logistic regression models and path analysis were used to control for confounders (tobacco, age, cannabis use). RESULTS: NT-4/5 was significantly decreased, and pro-BDNF was significantly increased in UHR individuals compared to controls. Cannabis use and higher NGF levels were significantly related to transition to psychiatric disorders among UHR subjects. Increased pro-BDNF and decreased NT-4/5 influenced transition by the mediation of perceptual abnormalities. CONCLUSIONS: Our study shows for the first time that NTs are altered in UHR compared to healthy control individuals, and that they can be a predictor of transition to psychiatric illnesses in this population. Future studies should employ larger naturalistic samples to confirm the findings.

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AIMS: Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings. METHODS: 58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used. RESULTS: Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item). CONCLUSION: Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.

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