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Family-based clusters of cognitive test performance in familial schizophrenia

机译:基于家族的家族性精神分裂症认知测试表现

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Background Cognitive traits derived from neuropsychological test data are considered to be potential endophenotypes of schizophrenia. Previously, these traits have been found to form a valid basis for clustering samples of schizophrenia patients into homogeneous subgroups. We set out to identify such clusters, but apart from previous studies, we included both schizophrenia patients and family members into the cluster analysis. The aim of the study was to detect family clusters with similar cognitive test performance. Methods Test scores from 54 randomly selected families comprising at least two siblings with schizophrenia spectrum disorders, and at least two unaffected family members were included in a complete-linkage cluster analysis with interactive data visualization. Results A well-performing, an impaired, and an intermediate family cluster emerged from the analysis. While the neuropsychological test scores differed significantly between the clusters, only minor differences were observed in the clinical variables. Conclusions The visually aided clustering algorithm was successful in identifying family clusters comprising both schizophrenia patients and their relatives. The present classification method may serve as a basis for selecting phenotypically more homogeneous groups of families in subsequent genetic analyses.
机译:背景技术从神经心理学测试数据得出的认知特征被认为是精神分裂症的潜在内表型。以前,已经发现这些特征为将精神分裂症患者的样本聚类为同质亚组提供了有效的基础。我们着手确定此类聚类,但除先前的研究外,我们还将精神分裂症患者和家属纳入聚类分析。该研究的目的是检测具有相似认知测试表现的家庭聚类。方法来自54个随机选择的家庭的测验分数包括至少两个未患精神分裂症谱系障碍的兄弟姐妹,以及至少两个未受影响的家庭成员,并通过交互式数据可视化进行全链接聚类分析。结果从分析中得出了一个表现良好,受损的家庭中间群体。虽然各组之间的神经心理测验分数差异显着,但在临床变量中仅观察到微小差异。结论视觉辅助聚类算法成功地识别了包括精神分裂症患者及其亲属在内的家庭聚类。本分类方法可作为在随后的遗传分析中选择表型上更均一的家庭群体的基础。

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