Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-018-32290-9
Title: Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging
Authors: Chin, R
You, A.X
Meng, F
Zhou, J 
Sim, K 
Keywords: adult
case control study
diagnostic imaging
female
gray matter
human
image processing
male
nuclear magnetic resonance imaging
procedures
schizophrenia
support vector machine
Adult
Case-Control Studies
Female
Gray Matter
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Schizophrenia
Support Vector Machine
Issue Date: 2018
Publisher: Nature Publishing Group
Citation: Chin, R, You, A.X, Meng, F, Zhou, J, Sim, K (2018). Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging. Scientific Reports 8 (1) : 13858. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-018-32290-9
Rights: Attribution 4.0 International
Abstract: Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the individual level. Machine-learning approaches have emerged as potential diagnostic and prognostic tools. We used an anatomically and spatially regularized support vector machine (SVM) framework to categorize schizophrenia and healthy individuals based on whole-brain gray matter densities estimated using voxel-based morphometry from structural MRI scans. The regularized SVM model yielded recognition accuracy of 86.6% in the training set of 127 individuals and validation accuracy of 83.5% in an independent set of 85 individuals. A sequential region-of-interest (ROI) selection step was adopted for feature selection, improving recognition accuracy to 92.0% in the training set and 89.4% in the validation set. The combined model achieved 96.6% sensitivity and 74.1% specificity. Seven ROIs were identified as the optimal discriminatory subset: the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior superior temporal gyrus, superior frontal gyrus, left thalamus and left lateral ventricle. These findings demonstrate the utility of spatial and anatomical priors in SVM for neuroimaging analyses in conjunction with sequential ROI selection in the recognition of schizophrenia. © 2018, The Author(s).
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/178392
ISSN: 2045-2322
DOI: 10.1038/s41598-018-32290-9
Rights: Attribution 4.0 International
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