Sumiyoshi Chika, Fujino Haruo, Sumiyoshi Tomiki, Yasuda Yuka, Yamamori Hidenaga, Fujimoto Michiko, Hashimoto Ryota
Faculty of Human Development and Culture, Fukushima University, Fukushima, Japan.
Department of Special Needs Education, Oita University, Oita, Japan.
Front Psychiatry. 2018 Mar 21;9:87. doi: 10.3389/fpsyt.2018.00087. eCollection 2018.
Disorganization of semantic memory in patients with schizophrenia has been studied by referring to their category fluency performance. Recently, data-mining techniques such as singular value decomposition (SVD) analysis have been reported to be effective in elucidating the latent semantic memory structure in patients with schizophrenia. The aim of this study is to investigate semantic memory organization in patients with schizophrenia using a novel method based on data-mining approach.
Category fluency data were collected from 181 patients with schizophrenia and 335 healthy controls at the Department of Psychiatry, Osaka University. The 20 most frequently reported animals were chosen for SVD analysis. In the two-dimensional (2D) solution, item vectors (i.e., animal names) were plotted in the 2D space of each group. In the six-dimensional (6D) solution, inter-item similarities (i.e., cosines) were calculated among items. Cosine charts were also created for the six most frequent items to show the similarities to other animal items.
In the 2D spatial representation, the six most frequent items were grouped in the same clusters (i.e., as pet cluster, as wild/carnivorous cluster, and as wild/herbivorous cluster) for patients and healthy adults. As for 6D spatial cosines, the correlations (Pearson's ) between 17 items commonly generated in the two groups were moderately high. However, cosine charts created for the three pairs from the six most frequent animals (---) showed that pair-wise similarities between other animals were less salient in patients with schizophrenia.
Semantic memory organization in patients with schizophrenia, revealed by SVD analysis, did not appear to be seriously impaired in the 2D space representation, maintaining a clustering structure similar to that in healthy controls for common animals. However, the coherence of those animals was less salient in 6D space, lacking pair-wise similarities to other members of the animal category. These results suggests subtle but structural differences between the two groups. A data-mining approach by means of SVD analysis seems to be effective in evaluating semantic memory in patients with schizophrenia, providing both a visual representation and an objective measure of the structural alterations.
通过参考精神分裂症患者的类别流畅性表现来研究其语义记忆的紊乱情况。最近，据报道诸如奇异值分解（SVD）分析等数据挖掘技术在阐明精神分裂症患者潜在的语义记忆结构方面是有效的。本研究的目的是使用基于数据挖掘方法的新方法来研究精神分裂症患者的语义记忆组织。
在大阪大学精神病学系收集了181例精神分裂症患者和335名健康对照者的类别流畅性数据。选择20种最常被提及的动物进行SVD分析。在二维（2D）解决方案中，将项目向量（即动物名称）绘制在每组的二维空间中。在六维（6D）解决方案中，计算项目之间的项目间相似度（即余弦值）。还为六种最常见的项目创建了余弦图，以显示与其他动物项目的相似度。
在二维空间表示中，对于患者和健康成年人，六种最常见的项目被分组在相同的簇中（即作为宠物簇、作为野生/食肉簇和作为野生/食草簇）。至于六维空间余弦值，两组中共同生成的17个项目之间的相关性（皮尔逊相关性）中等偏高。然而，从六种最常见的动物中为三对创建的余弦图（---）显示，精神分裂症患者中其他动物之间的成对相似度不太明显。
通过SVD分析揭示的精神分裂症患者的语义记忆组织，在二维空间表示中似乎没有受到严重损害，对于常见动物保持了与健康对照者相似的聚类结构。然而，这些动物在六维空间中的连贯性不太明显，缺乏与动物类别中其他成员的成对相似度。这些结果表明两组之间存在细微但结构性的差异。通过SVD分析的数据挖掘方法似乎在评估精神分裂症患者的语义记忆方面是有效的，提供了结构改变的直观表示和客观测量。
