Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 43, Issue 10, Pages 1313-1320Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2013.07.004
Keywords
Mild cognitive impairment; Diffusion tensor imaging; Tractography; Tract-based spatial statistics; Fiber pathways
Categories
Funding
- Basic Science Research program [2012R1A1A3011982]
- Key Research Institute program through the National Research Foundation of Korea (NRF) [2010-0020163]
- Ministry of Education, Science and Technology
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Early detection of Alzheimer's disease (AD) is important since treatments are more efficacious when used at the beginning of the disease. Despite significant advances in diagnostic methods for AD, there is no single diagnostic method for AD with high accuracy. We developed a support vector machine (SVM) model that classifies mild cognitive impairment (MCI) and normal control subjects using probabilistic tractography and tract-based spatial statistics of diffusion tensor imaging (DTI) data. MCI is an intermediate state between normal aging and AD, so finding MCI is important for an early diagnosis of AD. The key features of DTI data we identified through extensive analysis include the fractional anisotropy (FA) values of selected voxels, their average FA value, and the volume of fiber pathways from a pre-defined seed region. In particular, the volume of the fiber pathways to thalamus is the most powerful single feature in classifying MCI and normal subjects regardless of the age of the subjects. The best performance achieved by the SVM model in a 10-fold cross validation and in independent testing was sensitivity of 100%, specificity of 100% and accuracy of 100%. (c) 2013 Elsevier Ltd. All rights reserved.
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