title: A comparison of three brain atlases for MCI prediction.
abstract: 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.
  
  
  METHODS
  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.
authors.affiliation: Human Brain Research Center, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan. Electronic address: otak@kuhp.kyoto-u.ac.jp.
authors.forename: Kenichi
authors.initials: K
authors.lastname: Ota
authors.forename: Naoya
authors.initials: N
authors.lastname: Oishi
authors.forename: Kengo
authors.initials: K
authors.lastname: Ito
authors.forename: Hidenao
authors.initials: H
authors.lastname: Fukuyama
mesh_terms: D000368:Aged; D000716:Anatomy, Artistic; D001271:Atlases as Topic; D001921:Brain / Q000473:pathology; D060825:Cognitive Dysfunction / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D060388:Support Vector Machine
keywords: Alzheimer's disease (AD); Atlas-based parcellation; Magnetic resonance imaging (MRI); Mild cognitive impairment (MCI); Support vector machine (SVM); Voxel-based morphometry (VBM)