Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images.

ABSTRACT: Voxel-based morphometry (VBM) studies have revealed gray matter alterations in smokers, but this type of analysis has poor predictive value for individual cases, which limits its applicability in clinical diagnoses and treatment. A predictive model would essentially embody a complex biomarker that could be used to evaluate treatment efficacy. In this study, we applied VBM along with a multivariate classification method consisting of a support vector machine with recursive feature elimination to discriminate smokers from nonsmokers using their structural MRI data. Mean gray matter volumes in 1,024 cerebral cortical regions of interest created using a subparcellated version of the Automated Anatomical Labeling template were calculated from 60 smokers and 60 nonsmokers, and served as input fe

SUBMITTER: Ding X 

PROVIDER: S-EPMC5531448 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature