title: Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease.
abstract: BACKGROUND
  The choice of biomarkers for early detection of Alzheimer's disease (AD) is important for improving the accuracy of imaging-based prediction of conversion from mild cognitive impairment (MCI) to AD. The primary goal of this study was to assess the effects of imaging modalities and brain atlases on prediction. We also investigated the influence of support vector machine recursive feature elimination (SVM-RFE) on predictive performance.
  
  
  METHODS
  Eighty individuals with amnestic MCI [40 developed AD within 3 years] underwent structural magnetic resonance imaging (MRI) and (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans at baseline. Using Automated Anatomical Labeling (AAL) and LONI Probabilistic Brain Atlas (LPBA40), we extracted features representing gray matter density and relative cerebral metabolic rate for glucose in each region of interest from the baseline MRI and FDG-PET data, respectively. We used linear SVM ensemble with bagging and computed the area under the receiver operating characteristic curve (AUC) as a measure of classification performance. We performed multiple SVM-RFE to compute feature ranking. We performed analysis of variance on the mean AUCs for eight feature sets.
  
  
  RESULTS
  The interactions between atlas and modality choices were significant. The main effect of SVM-RFE was significant, but the interactions with the other factors were not significant.
  
  
  CONCLUSIONS
  Multimodal features were found to be better than unimodal features to predict AD. FDG-PET was found to be better than MRI.
  
  
  CONCLUSIONS
  Imaging modalities and brain atlases interact with each other and affect prediction. SVM-RFE can improve the predictive accuracy when using atlas-based features.
authors: Human Brain Research Center, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan; Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan.
authors: Kenichi
authors: K
authors: Ota
authors: Human Brain Research Center, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan; Department of Psychiatry, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan. Electronic address: noishi@kuhp.kyoto-u.ac.jp.
authors: Naoya
authors: N
authors: Oishi
authors: Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu-shi, Aichi 474-8511, Japan.
authors: Kengo
authors: K
authors: Ito
authors: Human Brain Research Center, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan; Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan.
authors: Hidenao
authors: H
authors: Fukuyama
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D001271:Atlases as Topic; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000145:classification / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000473:pathology; D015331:Cohort Studies; D066264:Datasets as Topic; D018450:Disease Progression; D005260:Female; D019788:Fluorodeoxyglucose F18; D005500:Follow-Up Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D064847:Multimodal Imaging / Q000379:methods; D049268:Positron-Emission Tomography / Q000379:methods*; D011379:Prognosis; D019275:Radiopharmaceuticals; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
keywords: (18)F-fluorodeoxyglucose positron emission tomography; Alzheimer's disease; Feature selection; Magnetic resonance imaging; Mild cognitive impairment; Support vector machine