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Journal Article

Classification of first-episode schizophrenia using multimodal brain features: A combined structural and diffusion imaging study

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Kong,  Xiangzhen
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

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sby091_suppl_supplementary_material.doc
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Citation

Liang, S., Li, Y., Zhang, Z., Kong, X., Wang, Q., Deng, W., et al. (2019). Classification of first-episode schizophrenia using multimodal brain features: A combined structural and diffusion imaging study. Schizophrenia Bulletin, 45(3), 591-599. doi:10.1093/schbul/sby091.


Cite as: http://hdl.handle.net/21.11116/0000-0003-8663-6
Abstract
Schizophrenia is a common and complex mental disorder with neuroimaging alterations. Recent neuroanatomical pattern recognition studies attempted to distinguish individuals with schizophrenia by structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI). 1, 2 Applications of cutting-edge machine learning approaches in structural neuroimaging studies have revealed potential pathways to classification of schizophrenia based on regional gray matter volume (GMV) or density or cortical thickness. 3–5 Additionally, cortical folding may have high discriminatory value in correctly identifying symptom severity in schizophrenia. 6 Regional GMV and cortical thickness have also been combined in attempts to differentiate individuals with schizophrenia from healthy controls (HCs). 7 Applications of machine learning algorithms to diffusion imaging data analysis to predict individuals with first-episode schizophrenia (FES) have achieved encouraging accuracy. 8–10 White matter (WM) abnormalities in schizophrenia as estimated by DTI appear to be present in the early stage of the disorder, most likely reflecting the developmental stage of the sample of interest.