Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging
| Autor: | Rowena Chin, Alex Xiaobin You, Kang Sim, Fanwen Meng, Juan Zhou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Support Vector Machine Computer science Inferior frontal gyrus lcsh:Medicine computer.software_genre Left lateral ventricle Article Left thalamus 03 medical and health sciences Superior temporal gyrus 0302 clinical medicine Neuroimaging Region of interest Voxel medicine Image Processing Computer-Assisted Middle frontal gyrus Humans Gray Matter lcsh:Science Multidisciplinary Fusiform gyrus business.industry lcsh:R Pattern recognition medicine.disease Magnetic Resonance Imaging 030227 psychiatry Superior frontal gyrus nervous system Schizophrenia Case-Control Studies lcsh:Q Female Artificial intelligence business computer 030217 neurology & neurosurgery Pars opercularis |
| Zdroj: | Scientific Reports Scientific Reports, Vol 8, Iss 1, Pp 1-10 (2018) |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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