Multiplex Hypergraph Modeling of Higher Order Structures in Psychometric Networks

A team of researchers from the University of Bologna and the Institute for Applied Mathematics of the National Research Council of Italy (CNR) has published a paper on arXiv that introduces a novel multiplex hypergraph framework for modeling psychiatric disorders. The work, led by Francesca Possenti, Laura Girelli, Paolo Tieri, and Manuela Petti, challenges the traditional view that mental disorders are latent conditions causing observable symptoms. Instead, it builds on the psychometric network approach, which suggests that psychopathology emerges from interactions between symptoms. Traditional networks, however, only capture pairwise relationships, missing higher-order dependencies that may be crucial for understanding complex disorders like anorexia nervosa.

The proposed framework uses information-theoretic measures, specifically Ω-information, to quantify the balance between redundancy and synergy in symptom groups. Synergy occurs when joint symptom configurations convey more information than pairwise relations, while redundancy indicates overlapping information. To tackle the combinatorial explosion of possible symptom subsets, the researchers developed a structured pipeline: targeted candidate selection based on network topology and theory-driven subscales, a three-stage inferential procedure combining null-model testing with bootstrap robustness assessment, and the construction of diagnosis-layered multiplex hypergraphs. Applied to eating disorders data, the results revealed that synergy captures emergent, higher-order organization across diagnoses, showing both a stable transdiagnostic core and diagnosis-specific patterns. In contrast, redundancy was confined to eating and body-image related content, marking reinforcement rather than broader integration. This approach could lead to more precise diagnostic tools and personalized treatments by identifying how symptoms dynamically interact.