Wu Chaoyi, Yuan Chenyu, Fan Yinqing, Hong Ang, Wu Zhiling, Wang Zhen
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, P.R. China.
School of Psychology, Shanghai Jiao Tong University, Shanghai, P.R. China.
BMC Psychiatry. 2025 Jul 1;25(1):619. doi: 10.1186/s12888-025-06960-8.
Traditional descriptive nosology arbitrarily distinguishes between mental illness and health, hindering the progress of scientific research and clinical practice. Building on recent advancements in psychiatric conceptualization, this study proposes an innovative phased framework for deconstructing psychopathological heterogeneity. The framework involves four key steps: extraction of symptom dimensions, identification of psychopathological subtypes, characterization of symptom interaction patterns using a network approach, and validation of their incremental validity through links to neurobehavioral functions. This framework is preliminarily applied to a large, non-selective community sample (N = 4102) to explore its utility and potential for deconstructing psychopathological heterogeneity.
Data on comprehensive psychopathology and RDoC negative valence constructs were collected from the sample. Factor analysis and exploratory graph analysis were used to extract symptom dimensions. Latent profile analysis based on these dimensions was applied to identify psychopathological profiles. Partial correlation networks were estimated for each profile, and symptom network characteristics were compared across profiles. Finally, hierarchical multiple regression was applied to assess incremental validity.
The first step of the phased framework involves extracting homogeneous dimensions based on symptom co-occurrence patterns, yielding seven distinct dimensions: Obsessive-Compulsive, Emotional Distress, Eating-Related, Substance-Related, Aggressive, Psychotic, and Somatoform dimensions. The second step involves applying a person-centered approach to identify latent subgroups based on these symptom dimensions. Four profiles were identified, namely Substance Use Group, Moderate Symptomatology Group, Disengaged from Symptomatology Group, and Severe Symptomatology Group. The third step involves characterizing symptom interaction patterns across subgroups. Using a network approach, the Severe Symptomatology Group exhibited the densest interconnections and the highest global network strength, with Aggressive and Psychotic dimensions serving as core issues compared to other profiles. Finally, incremental validity was assessed through associations with self-reported neurobehavioral functions. Results showed that these profiles provided unique predictive value for RDoC negative valence constructs beyond both dichotomous diagnostic status and purely dimensional approach.
This study introduces a fine-grained framework for deconstructing psychopathological heterogeneity, providing a comprehensive approach to parsing psychopathology. While the framework is preliminarily applied to a large sample from the Chinese population, future studies should integrate multimodal neurobehavioral measures, employ intensive longitudinal designs to track symptom dynamics, and validate its consistency across diverse cultural and regional contexts.
传统的描述性疾病分类法随意区分精神疾病和健康状态，阻碍了科学研究和临床实践的进展。基于精神病学概念化的最新进展，本研究提出了一个创新的阶段性框架，用于解构精神病理异质性。该框架包括四个关键步骤：症状维度提取、精神病理亚型识别、使用网络方法表征症状交互模式以及通过与神经行为功能的联系验证其增量效度。此框架初步应用于一个大型、非选择性的社区样本（N = 4102），以探索其在解构精神病理异质性方面的效用和潜力。
从样本中收集了关于综合精神病理学和RDoC负性效价结构的数据。使用因子分析和探索性图分析来提取症状维度。基于这些维度应用潜在类别分析来识别精神病理类别。为每个类别估计偏相关网络，并比较各分类之间的症状网络特征。最后，应用分层多元回归来评估增量效度。
阶段性框架的第一步涉及根据症状共现模式提取同质维度，产生七个不同维度：强迫、情绪困扰、饮食相关、物质相关、攻击、精神病性和躯体形式维度。第二步涉及采用以人为主的方法，基于这些症状维度识别潜在亚组。识别出四个类别，即物质使用组、中度症状组、脱离症状组和重度症状组。第三步涉及表征各亚组间的症状交互模式。使用网络方法，重度症状组表现出最密集的相互连接和最高的全局网络强度，与其他类别相比，攻击和精神病性维度是核心问题。最后，通过与自我报告的神经行为功能的关联评估增量效度。结果表明，这些类别为RDoC负性效价结构提供了超越二分诊断状态和纯维度方法的独特预测价值。
本研究引入了一个用于解构精神病理异质性的细粒度框架，为剖析精神病理学提供了一种全面的方法。虽然该框架初步应用于来自中国人群的大型样本，但未来的研究应整合多模态神经行为测量，采用密集纵向设计来跟踪症状动态，并验证其在不同文化和地区背景下的一致性。
