Neuroradiology

, Volume 51, Issue 2, pp 73–83

Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI

Authors

  • Benoît Magnin
    • UMR-S 678, Inserm
    • UMR-S 610, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
  • Lilia Mesrob
    • UMR-S 610, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
  • Serge Kinkingnéhun
    • UMR-S 610, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
    • e(ye)BRAIN
    • UMR-S 678, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
  • Olivier Colliot
    • IFR 49
    • UPR 640 LENA, CNRS
  • Marie Sarazin
    • UMR-S 610, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
    • Department of NeurologyPitié-Salpêtrière Hospital
  • Bruno Dubois
    • UMR-S 610, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
    • Department of NeurologyPitié-Salpêtrière Hospital
  • Stéphane Lehéricy
    • UMR-S 610, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
    • Center for NeuroImaging Research–CENIRUMPC Univ Paris 06
    • Department of NeuroradiologyPitié-Salpêtrière Hospital
  • Habib Benali
    • UMR-S 678, Inserm
    • Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06
    • IFR 49
    • UNF/CRIUGM, Université de Montréal
Diagnostic Neuroradiology

DOI: 10.1007/s00234-008-0463-x

Cite this article as:
Magnin, B., Mesrob, L., Kinkingnéhun, S. et al. Neuroradiology (2009) 51: 73. doi:10.1007/s00234-008-0463-x

Abstract

Purpose

We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer’s disease (AD) and elderly control subjects.

Materials and methods

We studied 16 patients with AD [mean age ± standard deviation (SD) = 74.1 ± 5.2 years, mini-mental score examination (MMSE) = 23.1 ± 2.9] and 22 elderly controls (72.3 ± 5.0 years, MMSE = 28.5 ± 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results.

Results

We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%).

Conclusions

Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.

Keywords

Alzheimer’s diseaseDiagnosisMagnetic resonance imageSupport vector machineSensitivitySpecificity

Copyright information

© Springer-Verlag 2008