Textual forma mentis networks bridge language structure, emotional content and psychopathology levels in adolescents

We introduce a network-based AI framework for identifying dimensions of psychopathology in adolescents using natural language. We focused on data capturing psychometric scores of social maladjustment, internalising behaviors, and neurodevelopmental risk, assessed in 232 adolescents from the Healthy Brain Network. This dataset included structured interviews in which adolescents discussed a common emotioninducing topic. To model conceptual associations within these interviews, we applied textual forma mentis networks (TFMNs)-a cognitive/AI approach integrating syntactic, semantic, and emotional word-word associations in language. From TFMNs, we extracted network features (semantic/syntactic structure) and emotional profiles to serve as predictors of latent psychopathology factor scores. Using Random Forest and Gradient Boosting Machine regression models, we found significant associations between language-derived features and clinical scores: social maladjustment (r=0.37, p<.01), specific internalising behaviors (r=0.33, p<.05), and neurodevelopmental risk (r=0.34, p<.05). Explainable AI analysis using SHAP values revealed that higher modularity and a pronounced core-periphery network structure-reflecting clustered conceptual organisation in language-corresponded with increased social maladjustment. Internalising scores were positively associated with higher betweenness centrality and stronger expressions of disgust, suggesting a linguistic signature of rumination. In contrast, neurodevelopmental risk was inversely related to local efficiency in syntactic/semantic networks, indicating disrupted conceptual integration. These findings demonstrated the potential of cognitive network approaches to capture meaningful links between psychopathology and language use in adolescents.