Abstract
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., dog, cat as pet cluster, lion, tiger as wild/carnivorous cluster, and elephant, giraffe as wild/herbivorous cluster) for patients and healthy adults. As for 6D spatial cosines, the correlations (Pearson's r) 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 (dog-cat, lion-tiger, elephant-giraffe) 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.
Highlights
Cognitive impairment in patients with schizophrenia is a cardinal feature of the disease and is generally independent of positive or negative psychiatric symptoms
The verbal fluency performance was significantly better in healthy controls than patients with schizophrenia (LFT score: t = 9.90, df = 514, p < 0.001, category fluency task (CFT): t = 12.06, df = 514, p < 0.001)
singular value decomposition (SVD) Analysis As previous studies have suggested [39], there is no statistical rules for choosing an appropriate number of singular values for the dimensionality reduction
Summary
Cognitive impairment in patients with schizophrenia is a cardinal feature of the disease and is generally independent of positive or negative psychiatric symptoms (e.g., hallucinations or withdrawal) This impairment disturbs favorable functional outcomes of patients, including daily living skills, social functioning, and work [1,2,3,4]. The Brief Assessment of Cognition in Schizophrenia (BACS) [5] and MATRICS Consensus Cognitive Battery (MCCB) [6] are the most acknowledged batteries, and they have been used for research and clinical purposes Those “gold-standard” cognitive batteries have been reported to be effective for predicting functional outcomes in patients with schizophrenia [7], the target domains are mainly executive aspects of cognition (i.e., attention, processing speed, and visual/verbal working memory). The aim of this study is to investigate semantic memory organization in patients with schizophrenia using a novel method based on data-mining approach
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