Different electrophysiological (EEG) indices have been investigated as possible biomarkers of schizophrenia. However, these indices have a very limited use in clinical practice, as their associations with clinical and functional outcomes remain unclear. This study aimed to investigate the associations of multiple EEG markers with clinical variables and functional outcomes in subjects with schizophrenia (SCZs).

Resting-state EEGs (frequency bands and microstates) and auditory event-related potentials (MMN-P3a and N100-P3b) were recorded in 113 SCZs and 57 healthy controls (HCs) at baseline. Illness- and functioning-related variables were assessed both at baseline and at 4-year follow-up in 61 SCZs. We generated a machine-learning classifier for each EEG parameter (frequency bands, microstates, N100-P300 task, and MMN-P3a task) to identify potential markers discriminating SCZs from HCs, and a global classifier. Associations of the classifiers’ decision scores with illness- and functioning-related variables at baseline and follow-up were then investigated.

The global classifier discriminated SCZs from HCs with an accuracy of 75.4% and its decision scores significantly correlated with negative symptoms, depression, neurocognition, and real-life functioning at 4-year follow-up.

These results suggest that a combination of multiple EEG alterations is associated with poor functional outcomes and its clinical and cognitive determinants in SCZs. These findings need replication, possibly looking at different illness stages in order to implement EEG as a possible tool for the prediction of poor functional outcome.

One in six adolescents suffers from mental health problems. Despite the presence of general information on Italian adolescents' mental health, researches conducted with standardized assessment tools are scarce in the literature. We evaluated the prevalence of self-reported behavioral and emotional problems in a group of Italian adolescents and examined their relation to socio-demographical variables.

This population-based sampling survey was conducted on high school students aged 14–18 from urban areas of Rome and Latina. Participants completed Youth Self-Report (YSR) and a socio-demographic schedule to collect information on age, gender, type of school attended, socio-economic status, urbanicity.

Final sample consisted of 1400 adolescents (38.61% male, mean age 16 years, s.d. 1.42). Prevalence of Internalizing Problems, Externalizing Problems and Total Problems was 29.55%, 18.34% and 24.13%. In our multivariable model, Internalizing Symptoms were not explained by sociodemographic variables while Externalizing Symptoms were explained by Male Gender [OR = 1.53 (1.14–2.06)], older age [OR = 2.06 (1.52–2.79)] and attending a Technical and Professional Institute [OR = 2.15 (1.53–3.02)], with an adjusted R2 = 4.32%. Total Problems were explained by School Type [Technical and Professional Institutes and Art and Humanities v. Grammar and Science School; OR respectively 1.93 (1.40–2.67) and 1.64 (1.08–2.47)], adjusted R2 = 1.94.

The study provides, for the first time, evidence of a great prevalence of self-reported behavioral and emotional problems in a large sample of Italian adolescents, highlighting the role of different socio-demographic variables as risk factors for externalizing behaviors. Our results emphasize the urgent need for implementing prevention programs on mental health in adolescence.