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Background Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness data, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between the predictions and clinical variables. Methods 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI (sMRI) session and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20484 vertices) were extracted. Supervised (linear Support Vector Machine) classification was used to differentiate FEP from HC, within a repeated nested Cross-Validation (CV) framework through the NeuroMiner software. In both inner and outer CVs, a 10-fold CV cycle was employed. We performed repeated nested CV at the outer cross-validation cycle by randomly permuting the participants within their groups (10 permutations) and repeating the CV cycle for each of these permutations. As feature preprocessing, regression of covariates (age, sex, and site), Principal Component Analysis and Scaling were applied. All preprocessing steps were implemented within the CV. Further analyses were conducted by stratifying the sample for MRI scanner, sex and by performing random resampling with increasingly reduced sample sizes. Results Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy (BAC) of 66.2% and an Area Under the Curve of 72%, compared to a BAC of 59% and an Area Under the Curve of 61% obtained with the ROI-based approach. The two BACs were significantly different based on the McNemar’s Test. By stratifying our sample for MRI scanner, we increased the overall BAC to more than 70% and we also increased generalizability across sites. Temporal areas resulted the most influential regions in the classification. The predictive decision scores presented significant correlations with age at onset, duration of treatment and the presence of affective vs non-affective psychosis. Discussion Cortical thickness could represent a valid measure to classify FEP subjects, showing temporal areas as potential markers in the early stages of psychosis. The assessment of site-dependent variables allowed us to increase the across-site generalizability of the model, thus attempting to address an important machine learning limitation, especially in the framework of large multi-site cohort and big data analysis.

Background The self-disturbances (SDs) concept is considered to be part of the Schneider’s first rank symptoms, i.e., thought-withdrawal, thought-insertion, thought-broadcasting, somatic-passivity experiences, mental/motor automatisms, disrupted unitary self-experience (Mishara et al., 2014). SDs were originally described by W. Mayer-Gross (1920), who observed them in psychotic patients. Methods We classified Mayer-Gross’ findings on SDs into the following categories: experience is new/compelling (aberrant salience), reduced access/importance of autobiographical past, cognitions/emotions occur independently from self’s volition, foreign agents have power over self and developed an SDs scale based on these categories and cognitive domains (perception, motor, speech, thinking etc.). Scale is applied as a measure of the frequency of the experiences. In our current study on phenomenology and neurobiology of psychotic symptoms, we administered the scale to a study group of patients with schizophrenia (N=84) and healthy volunteers (N=170). Further, the resting state fMRI was performed and the group was divided into two subgroups with (N=13) and without self-disturbances (N=10) and in healthy individuals (N=39). Results We found substantial differences in the frequency of self-disturbances in patients with schizophrenia compared to healthy controls (total score differences, Z=-5.83, p< 0.001). On a neural level, patients with self-disturbances experienced a decreased functional brain connectivity of the default mode and salience networks as compared to the patients without self-disturbances and healthy controls. The differences were mainly explained by the factor ‘’foreign agents’’ and the novelty of the experience. Discussion The scale identifies self-disturbances in schizophrenia and confirms self-related processing in patients with schizophrenia to be associated with altered activation in the cortical midline structures. Supported by the grant projects MH CR AZV 17-32957A and MEYS NPU4NUDZ: LO1611.