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.
Highlights
The development of neuroimaging techniques such as magnetic resonance imaging (MRI) has enabled the noninvasive in vivo examination of brain structure
The 7 ROIs that were found to make up the optimal subset were the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior division of the superior temporal gyrus, left thalamus and left lateral ventricle
The accuracies and the cumulative proportions of the 7 ROIs to the cerebrum volume were found to be: the occipital fusiform gyrus (77.5%, 1.3%); middle frontal gyrus (79.5%, 5.3%); pars opercularis of the inferior frontal gyrus (84.2%, 6.3%); anterior division of the superior temporal gyrus (87.2%, 6.7%); superior frontal gyrus (90.1%, 10.7%); left thalamus (91.1%, 11.6%) and left lateral ventricle (92.0%, 12.4%)
Summary
The development of neuroimaging techniques such as magnetic resonance imaging (MRI) has enabled the noninvasive in vivo examination of brain structure. There is substantial evidence from neuroimaging research that has revealed a range of structural brain abnormalities implicated in schizophrenia[9,10,11,12]; some of which are present at early course of illness[13,14,15], or even before disease onset in high-risk individuals[16,17,18]. We sought to apply SVM with customized anatomical and spatial kernels to classify schizophrenia patients and healthy controls using structural MRI brain scans in a relatively large sample set and validated the classification results with an independent sample. We investigated the utility of a sequential step involving the use of an ROI selection algorithm to localize an optimal subset of ROIs to efficiently classify schizophrenia patients and healthy controls
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