Textual forma mentis networks bridge language structure, emotional content and psychopathology levels in adolescents
Journal:
arXiv
Published Date:
May 9, 2025
Abstract
We introduce a network-based AI framework for predicting dimensions of
psychopathology in adolescents using natural language. We focused on data
capturing psychometric scores of social maladjustment, internalizing behaviors,
and neurodevelopmental risk, assessed in 232 adolescents from the Healthy Brain
Network. This dataset included structured interviews in which adolescents
discussed a common emotion-inducing 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 XGBoost
regression models, we found significant associations between language-derived
features and clinical scores: social maladjustment (r = 0.37, p < .01),
specific internalizing 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 organization in language-predicted
increased social maladjustment. Internalizing 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.
