Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 221, Issue -, Pages 139-150Publisher
ELSEVIER
DOI: 10.1016/j.jneumeth.2013.10.003
Keywords
Alzheimer's disease (AD); Mild cognitive impairment (MCI); Magnetic resonance imaging (MRI); Voxel-based morphometry (VBM); Support vector machine (SVM); Atlas-based parcellation
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Funding
- Health Labour Sciences Research Grand from the Ministry of Health, Labour, and Welfare of Japan [H17-Tyojyu-023]
- National Center for Geriatrics and Gerontology, Japan [20-1]
- Grants-in-Aid for Scientific Research [24791296, 25117008, 22220003, 24390292] Funding Source: KAKEN
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Background: Although previous voxel-based studies using features extracted by atlas-based parcellation produced relatively poor performances on the prediction of Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI), classification performance usually depends on features extracted from the original images by atlas-based parcellation. To establish whether classification performance differs depending on the choice of atlases, support vector machine (SVM)-based classification using different brain atlases was performed. New method: Seventy-seven three-dimensional T1 -weighted MRI data sets of subjects with amnestic MCI, including 39 subjects who developed AD (MCI-C) within three years and 38 who did not (MCI-NC), were used for voxel-based morphometry (VBM) analyses and analyzed using SVM-based pattern recognition methods combined with a feature selection method based on the SVM recursive feature elimination (RFE) method. Three brain atlases were used for the feature selections: the Automated Anatomical Labeling (AAL) Atlas, Brodmann's Areas (BA), and the LONI Probabilistic Brain Atlas (LPBA40). Results: The VBM analysis showed a significant cluster of gray matter density reduction, located at the left hippocampal region, in MCI-C compared to MCI-NC. The SVM analyses with the SVM-RFE algorithm revealed that the best classification performance was achieved by LPBA40 with 37 selected features, giving an accuracy of 77.9%. The overall performance in LPBA40 was better than that of AAL and BA regardless of the number of selected features. Conclusions: These results suggest that feature selection is crucial to improve the classification performance in atlas-based analysis and that the choice of atlases is also important. (C) 2013 Elsevier B.V. All rights reserved.
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