IntroductionThe heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders.MethodsIn this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category.ResultsWe identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled “Neurotic” (C1), “Extraverted” (C2), “Anxious to please” (C3), “Self-critical” (C4), “Conscientious” (C5). The non-clinical clusters were labeled “Self-confident” (N1), “Low endorsement” (N2), “Non-neurotic” (N3), “Neurotic” (N4), “High endorsement” (N5). The combined clusters were labeled “Self-confident” (NC1), “Externalising” (NC2), “Neurotic” (NC3), “Secure” (NC4), “Low endorsement” (NC5), “High endorsement” (NC6), “Self-critical” (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism.DiscussionOverall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.

中文翻译：

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根据自我参照判断对自我图式进行跨诊断聚类，识别出健康人格和抑郁的亚型

简介抑郁症和焦虑症的异质性使临床管理变得复杂，因为它可能导致轨迹和治疗反应的差异。自我图式可以通过自我参照判断（SRJ）来确定，是异构但稳定的。SRJ 已被用来表征一般人群的人格特征，并被证明可以预测抑郁症和焦虑症。方法在这项研究中，我们使用来自自我参照编码任务 (SRET) 的 SRJ 从招募的 119 名患者的临床样本中识别聚类。来自心理健康研究所的具有抑郁或焦虑症状的研究以及 115 名健康成年人的非临床样本。根据最认可的单词、跨样本对应性、与抑郁症状的关联以及抑郁经历问卷和诊断类别对生成的聚类进行检查。结果我们在每个样本中识别出 5 聚类解决方案，在组合样本中识别出 7 聚类解决方案。当受到扰动时，最佳簇数、标准值、可能性、DBI 和 CHI 等指标保持稳定，并且当使用 BIC 或 ICL 作为标准时，聚类中心显得稳定。集群中最受认可的单词在人格、相关性和自我定义的心理动力学概念以及自我参照处理中的效价等理论框架中都是有意义的。临床集群被标记为“神经质”（C1）、“外向”（C2）、“急于取悦”（C3）、“自我批评”（C4）、“尽责”（C5）。非临床集群被标记为“自信”（N1）、“低认可度”（N2）、“非神经质”（N3）、“神经质”（N4）、“高认可度”（N5）。组合后的聚类被标记为“自信”（NC1）、“外向化”（NC2）、“神经质”（NC3）、“安全”（NC4）、“低认可度”（NC5）、“高认可度”（NC6） ，“自我批评”（NC7）。在正面和负面词语的认可、潜伏偏差、回忆偏差、抑郁症状、抑郁症频率和自我批评方面观察到聚类差异。讨论总体而言，认可更多负面词语的聚类倾向于认可更少的正面词语，在反应中表现出更多的负面偏差时间和负面回忆偏差，报告了临床人群中更严重的抑郁症状和更高的抑郁症频率以及更多的自我批评。基于 SRJ 的聚类代表了一种新颖的跨诊断框架，用于对患有抑郁和焦虑症状的患者进行分组，这可能支持未来将自我参照处理、人格和自我定义的心理动力学概念科学转化为临床应用。