Psychiatric disorders have been traditionally conceptualized as latent conditions producing observable symptoms, but recent studies suggest that psychopathology may emerge from symptoms interactions. Psychometric networking model these relations focusing on pairwise associations but overlooks higher-order dependencies arising among groups of variables. These dependencies may reflect synergistic mechanisms, where joint symptom configurations convey more information than pairwise relations, or redundancy, where information overlaps. We introduce an information-theoretic multiplex hypergraph framework to identify and compare higher-order interactions in eating disorders data, across diagnostic groups (e.g., anorexia nervosa). Higher-order structures are quantified using $Ω$-information, a measure that captures the balance between redundancy and synergy. To address the combinatorial growth of candidate subsets, multiple testing and estimation instability, we propose a structured pipeline comprising: (i) targeted candidate selection based on dyadic network topology and theory-driven subscale information; (ii) a three-stage inferential procedure combining null-model testing with bootstrap robustness assessment; and (iii) the construction and analysis of diagnosis-layered, synergistic and redundant multiplex hypergraphs. Results highlight how synergy captures the emergent, higher-order organization of diagnoses, revealing both a stable transdiagnostic core and diagnosis-specific ways in which these domains combine. By contrast, redundancy is confined to eating and body-image related content, marking reinforcement rather than broader symptom integration.

翻译：精神障碍传统上被概念化为产生可观察症状的潜在条件，但近期研究表明精神病理学可能源于症状间的相互作用。心理测量网络通过关注成对关联来建模这些关系，却忽视了变量组间产生的高阶依赖性。这些依赖性可能反映协同机制（即联合症状构型比成对关系传递更多信息）或冗余机制（信息重叠）。我们引入信息论多重超图框架，用于识别和比较进食障碍数据中跨诊断组（如神经性厌食症）的高阶相互作用。高阶结构通过Ω-信息（捕捉冗余与协同平衡的度量）进行量化。针对候选子集组合增长、多重检验及估计不稳定性问题，我们提出结构化流程，包含：(i) 基于二元网络拓扑和理论驱动子量表信息的目标候选子集选择；(ii) 结合零模型检验与自助法稳健性评估的三阶段推断程序；(iii) 构建与分析诊断分层、协同性与冗余性多重超图。结果揭示协同性如何捕获诊断的涌现性高阶组织，展现出稳定的跨诊断核心以及这些领域组合方式的诊断特异性模式。相比之下，冗余性局限于进食与身体意象相关内容，标志着强化而非更广泛的症状整合。