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• W3033130100 abstract "Abstract An accurate identification of schizophrenia spectrum disorder (SSD) at early stage could potentially allow for treating SSD with appropriate intervention to potentially prevent future deterioration. Despite mounting studies found neuroimaging combined with machine learning can identify chronic medicated SSD, whether or not the classification model identified the trait biomarker of SSD that can be used to identify early stage SSD is largely unknown. The present study aimed to investigate whether or not the classification model trained using chronic medicated SSD identified the trait biomarker of SSD that whether or not the model can be generalized to early stage SSD, by using functional connectivity (FC) combined with support vector machine (SVM) using a large sample from 4 independent sites (n = 1077). We found that the classification model trained using chronic medicated SSD from three sites(dataset 2, 3 and 4) classified SSD from HCs in another site (dataset 1) with 69% accuracy (P = 2.86e-13). Subgroup analysis indicated that this model can identify chronic medicated SSD in dataset 1 with 71% sensitivity (P = 4.63e-05), but cannot be generalized to first episode unmedicated SSD (sensitivity = 48%, P = 0.68) and first episode medicated SSD (sensitivity = 59%, P = 0.10). Univariable analysis showed that medication usage had significant effect on FC, but disease duration had no significant effect on FC. These findings suggest that the classification model trained using chronic medicated SSD may mainly identified the pattern of chronic medication usage state, rather than the trait biomarker of SSD. Therefore, we should reconsider the current machine learning studies in chronic medicated SSD more cautiously in term of the clinical application." @default.
• W3033130100 created "2020-06-12" @default.
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• W3033130100 date "2020-06-03" @default.
• W3033130100 modified "2023-10-17" @default.
• W3033130100 title "Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application" @default.
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• W3033130100 doi "https://doi.org/10.1101/2020.05.30.20118026" @default.
• W3033130100 hasPublicationYear "2020" @default.
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