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

  • Diagnostic Neuroradiology
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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.

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Conflict of interest statement

S. Kinkingnéhun has a financial relationship with e(ye)BRAIN. B. Dubois and H. Benali consult for e(ye)BRAIN.

Author information

Authors and Affiliations

  1. UMR-S 678, Inserm, Paris, 75013, France

    Benoît Magnin, Mélanie Pélégrini-Issac & Habib Benali

  2. UMR-S 610, Inserm, Paris, 75013, France

    Benoît Magnin, Lilia Mesrob, Serge Kinkingnéhun, Marie Sarazin, Bruno Dubois & Stéphane Lehéricy

  3. Faculté de médecine Pitié-Salpêtrière, UMPC Univ Paris 06, Paris, 75013, France

    Benoît Magnin, Lilia Mesrob, Serge Kinkingnéhun, Mélanie Pélégrini-Issac, Marie Sarazin, Bruno Dubois, Stéphane Lehéricy & Habib Benali

  4. IFR 49, Gif-sur-Yvette, 91191, France

    Benoît Magnin, Lilia Mesrob, Serge Kinkingnéhun, Mélanie Pélégrini-Issac, Olivier Colliot, Marie Sarazin, Bruno Dubois, Stéphane Lehéricy & Habib Benali

  5. e(ye)BRAIN, 94400, Vitry-sur-Seine, France

    Serge Kinkingnéhun

  6. UPR 640 LENA, CNRS, Paris, 75013, France

    Olivier Colliot

  7. Department of Neurology, Pitié-Salpêtrière Hospital, Paris, 75013, France

    Marie Sarazin & Bruno Dubois

  8. Center for NeuroImaging Research–CENIR, UMPC Univ Paris 06, Paris, 75013, France

    Stéphane Lehéricy

  9. Department of Neuroradiology, Pitié-Salpêtrière Hospital, Paris, 75013, France

    Stéphane Lehéricy

  10. UNF/CRIUGM, Université de Montréal, Montréal, QC, H3W 1W5, Canada

    Habib Benali

Authors
  1. Benoît Magnin
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  2. Lilia Mesrob
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  3. Serge Kinkingnéhun
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  4. Mélanie Pélégrini-Issac
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  5. Olivier Colliot
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  6. Marie Sarazin
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  7. Bruno Dubois
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  8. Stéphane Lehéricy
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  9. Habib Benali
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Corresponding author

Correspondence to Mélanie Pélégrini-Issac.

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Magnin, B., Mesrob, L., Kinkingnéhun, S. et al. Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51, 73–83 (2009). https://doi.org/10.1007/s00234-008-0463-x

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  • DOI: https://doi.org/10.1007/s00234-008-0463-x

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