Classification of Healthy Siblings of Bipolar Disorder Patients From Healthy Controls Using Mri

Abstract

Three Dimensional magnetic resonance imaging (3D-MRI) has been utilized to classify patients with neuroanatomical abnormalities apart from healthy controls (HCs). The studies on the diagnosis of Bipolar Disorder (BD) focuses also on the unaffected relatives of BD patients in order to examine the heritable resistance factors associated with the disorder. Hence, the comparison of Healthy Siblings of Bipolar Disorder patients (HSBDs) and HCs is also required owing to the high heritability of BD. In this paper, the classification of 27HSBDs from 38HCs has been studied by using 3D-MRI and Computer-Aided Detection (CAD). The pre-processing of 3D-MRI data is performed by taking advantage of Voxel-Based Morphometry (VBM) and the structural deformations in the Gray Matter (GM) and White Matter (WM) are obtained by using a general linear model. The model is configured by using a two sample t-test technique and Total Intracranial Volume (TIV) as a covariate. The altered voxels between data groups are considered as Voxel of Interests (VOIs) and the 3D masks are generated for GM and WM tissue probability maps. The Relief-F algorithm is utilized to rank the features and a Fisher Criterion (FC) method is considered to determine the number of top-ranked discriminative features. The performances of Support Vector Machines (SVM) and the Naive Bayes (NB) algorithms are compared on the classification of HSBD and HC. The experiments are performed for GM-only, WM-only, and their combinations. The experimental results indicate that the changes between the brain regions of HSBD and HC might provide information on the heritable factors associated with the BD. Additionally, it is concluded that using the combination of GM and WM tissue probability map provides better results than considering them, separately. Finally, it is obtained that the classification accuracy of SVM on HSBD and HC comparison is better than that of NB.