Classification of Patients with Bipolar Disorder and Their Healthy Siblings from Healthy Controls Using MRI

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Gönül A. S. , Eker M. Ç. , Kitiş Ö. , Demirel H., Çiğdem Ö., Oğuz K., ...More

IEEE Xplore, vol.19, pp.1-6, 2019 (National Conference Book)

  • Publication Type: Article / Article
  • Volume: 19
  • Publication Date: 2019
  • Doi Number: 10.1109/memea.2019.8802207
  • Title of Journal : IEEE Xplore
  • Page Numbers: pp.1-6


Detection of Bipolar Disease (BD), one of the most common neuroanatomical abnormalities, using machine learning algorithms together with Magnetic Resonance Imaging (MRI) data has been widely studied. BD is a highly heritable disease, yet not all siblings tend to have it despite they might have similar genetic and environmental risk factors. In this paper, the classifications of two self-acquired data groups, namely 26BD patients and 38 unrelated Healthy Controls (HCs) as well as 27 Healthy Siblings of BD (BDHSs) and 38HCs are examined. Voxel-Based Morphometry (VBM) is utilized to segment and pre-process the MRI data. In order to obtain the morphological alterations in the Gray Matter (GM) and White Matter (WM) of data groups separately, a general linear model is configured and a two sample t-test based statistical method is used. The obtained differentiated voxels are considered as Voxel of Interests (VOIs) and using VOIs reduces the dimension of the original data into the number of VOIs. The effects of using different covariates (i.e. total intracranial volume (TIV), age, and sex) on classification of the two data groups have been studied for GM-only, WM-only, and their combination. Principle Component Analysis (PCA) is used to reduce the dimension of the extracted VOIs data and Support Vector Machine (SVM) with Gaussian kernel is taken into account as a classifier. The experimental results indicate that among three covariates, TIV provides better results for both data groups and the classification accuracies of the combination of GM and WM maps is higher than that of GM-only and WM- only for both groups. In BD and HC comparison, the highest classification accuracies of 70.3% for GM, 79.7% for WM, and 82.8% for fusion of extracted GM as well as WM are obtained. In BDHS and HC comparison, the highest classification accuracies of 72.3% for GM, 76.9% for WM, and 78.5% for fusion of extracted GM as well as WM are obtained.