Classification of Healthy Siblings of Bipolar Disorder Patients from Healthy Controls Using MRI


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

2019 Medical Technologies National Conference (TIPTEKNO2019, Aydın, Turkey, 3 - 05 October 2019, pp.477-480

  • Publication Type: Conference Paper / Full Text
  • City: Aydın
  • Country: Turkey
  • Page Numbers: pp.477-480

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 Detec-
tion (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.