Neuroradiology

, Volume 51, Issue 2, pp 73–83 | Cite as

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

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

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 disease Diagnosis Magnetic resonance image Support vector machine Sensitivity Specificity 

References

  1. 1.
    Brookmeyer R, Gray S, Kawas C (1998) Projections of Alzheimer’s disease in the United States and the public health impact of delaying disease onset. Am J Public Health 88:1337–1342PubMedCrossRefGoogle Scholar
  2. 2.
    Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, Hall K, Hasegawa K, Hendrie H, Huang Y, Jorm A, Mathers C, Menezes PR, Rimmer E, Scazufca M (2005) Global prevalence of dementia: a Delphi consensus study. Lancet 366:2112–2117 doi:10.1016/S0140-6736(05)67889-0 PubMedCrossRefGoogle Scholar
  3. 3.
    Ramaroson H, Helmer C, Barberger-Gateau P, Letenneur L, Dartigues J (2003) Prevalence of dementia and Alzheimer’s disease among subjects aged 75 years or over: updated results of the PAQUID cohort. Rev Neurol (Paris) 159:405–411 (in French)Google Scholar
  4. 4.
    Winblad B, Wimo A (1999) Assessing the societal impact of acetylcholinesterase inhibitor therapies. Alzheimer Dis Assoc Disord 13(Suppl 2):S9–S19 doi:10.1097/00002093-199911002-00003 PubMedCrossRefGoogle Scholar
  5. 5.
    DeKosky ST, Marek K (2003) Looking backward to move forward: early detection of neurodegenerative disorders. Science 302:830–834 doi:10.1126/science.1090349 PubMedCrossRefGoogle Scholar
  6. 6.
    Petersen RC (2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256:183–194 doi:10.1111/j.1365-2796.2004.01388.x PubMedCrossRefGoogle Scholar
  7. 7.
    Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund L, Nordberg A, Bäckman L, Albert M, Almkvist O, Arai H, Basun H, Blennow K, de Leon M, DeCarli C, Erkinjuntti T, Giacobini E, Graff C, Hardy J, Jack C, Jorm A, Ritchie K, van Duijn C, Visser P, Petersen RC (2004) Mild cognitive impairment—beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med 256:240–246 doi:10.1111/j.1365-2796.2004.01380.x PubMedCrossRefGoogle Scholar
  8. 8.
    Braak H, Braak E (1995) Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging 16:271–278 (discussion 278–284) doi:10.1016/0197-4580(95)00021-6PubMedCrossRefGoogle Scholar
  9. 9.
    Bastos Leite AJ, Scheltens P, Barkhof F (2004) Pathological aging of the brain: an overview. Top Magn Reson Imaging 15:369–389 doi:10.1097/01.rmr.0000168070.90113.dc PubMedCrossRefGoogle Scholar
  10. 10.
    Glodzik-Sobanska L, Rusinek H, Mosconi L, Li Y, Zhan J, de Santi S, Convit A, Rich K, Brys M, de Leon MJ (2005) The role of quantitative structural imaging in the early diagnosis of Alzheimer’s disease. Neuroimaging Clin N Am 15:803–826 doi:10.1016/j.nic.2005.09.004 PubMedCrossRefGoogle Scholar
  11. 11.
    Xu Y, Jack CR Jr, O’Brien PC, Kokmen E, Smith GE, Ivnik RJ, Boeve BF, Tangalos RG, Petersen RC (2000) Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology 54:1760–1767PubMedGoogle Scholar
  12. 12.
    Frisoni GB, Laakso MP, Beltramello A, Geroldi C, Bianchetti A, Soininen H, Trabucchi M (1999) Hippocampal and entorhinal cortex atrophy in frontotemporal dementia and Alzheimer’s disease. Neurology 52:91–100PubMedGoogle Scholar
  13. 13.
    Laakso MP, Soininen H, Partanen K, Lehtovirta M, Hallikainen M, Hänninen T, Helkala EL, Vainio P, Riekkinen PJS (1998) MRI of the hippocampus in Alzheimer’s disease: sensitivity, specificity, and analysis of the incorrectly classified subjects. Neurobiol Aging 19:23–31 doi:10.1016/S0197-4580(98)00006-2 PubMedCrossRefGoogle Scholar
  14. 14.
    Lehéricy S, Baulac M, Chiras J, Piérot L, Martin N, Pillon B, Deweer B, Dubois B, Marsault C (1994) Amygdalohippocampal MR volume measurements in the early stages of Alzheimer disease. Am J Neuroradiol 15:929–937PubMedGoogle Scholar
  15. 15.
    Jack CR Jr, Petersen RC, O’Brien PC, Tangalos EG (1992) MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology 42:183–188PubMedGoogle Scholar
  16. 16.
    Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hänninen T, Laakso MP, Hallikainen M, Vanhanen M, Nissinen A, Helkala E, Vainio P, Vanninen R, Partanen K, Soininen H (2004) Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging 25:303–310 doi:10.1016/S0197-4580(03)00084-8 PubMedCrossRefGoogle Scholar
  17. 17.
    Du AT, Schuff N, Amend D, Laakso MP, Hsu YY, Jagust WJ, Yaffe K, Kramer JH, Reed B, Norman D, Chui HC, Weiner MW (2001) Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry 71:441–447 doi:10.1136/jnnp.71.4.441 PubMedCrossRefGoogle Scholar
  18. 18.
    De Santi S, de Leon MJ, Rusinek H, Convit A, Tarshish CY, Roche A, Tsui WH, Kandil E, Boppana M, Daisley K, Wang GJ, Schlyer D, Fowler J (2001) Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging 22:529–539 doi:10.1016/S0197-4580(01)00230-5 PubMedCrossRefGoogle Scholar
  19. 19.
    Convit A, De Leon MJ, Tarshish C, De Santi S, Tsui W, Rusinek H, George A (1997) Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiol Aging 18:131–138 doi:10.1016/S0197-4580(97)00001-8 PubMedCrossRefGoogle Scholar
  20. 20.
    Chetelat G, Baron J (2003) Early diagnosis of Alzheimer’s disease: contribution of structural neuroimaging. Neuroimage 18:525–541 doi:10.1016/S1053-8119(02)00026-5 PubMedCrossRefGoogle Scholar
  21. 21.
    Lao Z, Shen D, Xue Z, Karacali B, Resnick SM, Davatzikos C (2004) Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage 21:46–57 doi:10.1016/j.neuroimage.2003.09.027 PubMedCrossRefGoogle Scholar
  22. 22.
    Fan Y, Shen D, Davatzikos C (2005) Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM. Med Image Comput Comput Assist Interv Int Conf 8:1–8CrossRefGoogle Scholar
  23. 23.
    Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20:273–297Google Scholar
  24. 24.
    Fan Y, Batmanghelich N, Clark CM, Davatzikos C (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39:1731–1743 doi:10.1016/j.neuroimage.2007.10.031 PubMedCrossRefGoogle Scholar
  25. 25.
    Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR Jr, Ashburner J, Frackowiak RSJ (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131:681–689 doi:10.1093/brain/awm319 PubMedCrossRefGoogle Scholar
  26. 26.
    Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, Knopman DS, Boeve BF, Petersen RC, Jack CR Jr (2008) Alzheimer’s disease diagnosis in individual subjects using structural MR images: Validation studies. Neuroimage 39:1186–1197 doi:10.1016/j.neuroimage.2007.09.073 PubMedCrossRefGoogle Scholar
  27. 27.
    Teipel SJ, Born C, Ewers M, Bokde ALW, Reiser MF, Möller H, Hampel H (2007) Multivariate deformation-based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. Neuroimage 38:13–24 doi:10.1016/j.neuroimage.2007.07.008 PubMedCrossRefGoogle Scholar
  28. 28.
    Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM (2008) Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29:514–523 doi:10.1016/j.neurobiolaging.2006.11.010 PubMedCrossRefGoogle Scholar
  29. 29.
    Davatzikos C, Resnick SM, Wu X, Parmpi P, Clark CM (2008) Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage 41:1220–1227 doi:10.1016/j.neuroimage.2008.03.050 PubMedCrossRefGoogle Scholar
  30. 30.
    McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34:939–944PubMedGoogle Scholar
  31. 31.
    Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43:2412–2414PubMedGoogle Scholar
  32. 32.
    Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198 doi:10.1016/0022-3956(75)90026-6 PubMedCrossRefGoogle Scholar
  33. 33.
    Benton AL (1968) Genuine memory deficits in dementia. Neuropsychologia 6:53–60 doi:10.1016/0028-3932(68)90038-9 CrossRefGoogle Scholar
  34. 34.
    Sano M, Stern Y, Mayeux R, Hartman S, Devanand DP (1987) A standardized technique for establishing the onset symptoms of probable Alzheimer’s disease. J Clin Exp Neuropsychol 9:65Google Scholar
  35. 35.
    Stern Y, Albert M, Brandt J, Jacobs DM, Tang MX, Marder K, Bell K, Sano M, Devanand DP, Bylsma F et al (1994) Utility of extrapyramidal signs and psychosis as predictors of cognitive and functional decline, nursing home admission, and death in Alzheimer’s disease: prospective analyses from the Predictors Study. Neurology 44:2300–2307PubMedGoogle Scholar
  36. 36.
    Stern Y, Mayeux R, Sano M, Hauser WA, Bush T (1987) Predictors of disease course in patients with probable Alzheimer’s disease. Neurology 37:1649–1653PubMedGoogle Scholar
  37. 37.
    Grober E, Buschke H (1987) Genuine memory deficits in dementia. Dev Neuropsychol 3:13–36CrossRefGoogle Scholar
  38. 38.
    Goldblum MC, Gomez CM, Dalla Barba G, Boller F, Deweer B, Hahn V, Dubois B (1998) The influence of semantic and perceptual encoding on recognition memory in Alzheimer’s disease. Neuropsychologia 36:717–729 doi:10.1016/S0028-3932(98)00007-4 PubMedCrossRefGoogle Scholar
  39. 39.
    Benton AL (1974) The revised visual retention test: clinical and experimental applications. Psychological Corporation, New YorkGoogle Scholar
  40. 40.
    Sirigu A, Cohen L, Duhamel JR, Pillon B, Dubois B, Agid Y (1995) A selective impairment of hand posture for object utilization in apraxia. Cortex 31:41–55PubMedGoogle Scholar
  41. 41.
    Mayeux R, Rosen W (1983) The dementias. Raven, New YorkGoogle Scholar
  42. 42.
    Deloche G, Hannequin D (1997) Test de dénomination orale d’images D080. Les Editions du Centre de Psychologie Appliquée, ParisGoogle Scholar
  43. 43.
    Kaplan E, Goodglass H, Weintraub S (1983) The Boston Naming Test. Lea and Febiger, PhiladelphiaGoogle Scholar
  44. 44.
    Dubois B, Slachevsky A, Litvan I, Pillon B (2000) The FAB: a Frontal Assessment Battery at bedside. Neurology 55:1621–1626PubMedGoogle Scholar
  45. 45.
    Dorion AA, Sarazin M, Hasboun D, Hahn-Barma V, Dubois B, Zouaoui A, Marsault C, Duyme M (2002) Relationship between attentional performance and corpus callosum morphometry in patients with Alzheimer’s disease. Neuropsychologia 40:946–956 doi:10.1016/S0028-3932(01)00150-6 PubMedCrossRefGoogle Scholar
  46. 46.
    Robbins TW, James M, Owen AM, Sahakian BJ, Lawrence AD, McInnes L, Rabbitt PM (1998) A study of performance on tests from the CANTAB battery sensitive to frontal lobe dysfunction in a large sample of normal volunteers: implications for theories of executive functioning and cognitive aging. Cambridge Neuropsychological Test Automated Battery. J Int Neuropsychol Soc 4:474–490 doi:10.1017/S1355617798455073 PubMedCrossRefGoogle Scholar
  47. 47.
    Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289 doi:10.1006/nimg.2001.0978 PubMedCrossRefGoogle Scholar
  48. 48.
    Ashburner J, Friston K (2003) Image segmentation. In: Frackowiak R, Friston K, Frith C, Dolan R, Price C, Zeki S, Ashburner J, Penny W (eds) Human brain function, 2nd edn. Academic, San Diego, pp 695–706Google Scholar
  49. 49.
    Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. Thieme Medical Publisher, New YorkGoogle Scholar
  50. 50.
    Ashburner J, Friston KJ (1999) Nonlinear spatial normalization using basis functions. Hum Brain Mapp 7:254–266 doi:10.1002/(SICI)1097-0193(1999)7:4<254::AID-HBM4>3.0.CO;2-G PubMedCrossRefGoogle Scholar
  51. 51.
    Ashburner J, Andersson JL, Friston KJ (2000) Image registration using a symmetric prior—in three dimensions. Hum Brain Mapp 9:212–225 doi:10.1002/(SICI)1097-0193(200004)9:4<212::AID-HBM3>3.0.CO;2-# PubMedCrossRefGoogle Scholar
  52. 52.
    Redner R, Walker H (1984) Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev 26:195–239 doi:10.1137/1026034 CrossRefGoogle Scholar
  53. 53.
    Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New YorkGoogle Scholar
  54. 54.
    Burton EJ, Karas G, Paling SM, Barber R, Williams ED, Ballard CG, McKeith IG, Scheltens P, Barkhof F, O’Brien JT (2002) Patterns of cerebral atrophy in dementia with Lewy bodies using voxel-based morphometry. Neuroimage 17:618–630 doi:10.1016/S1053-8119(02)91197-3 PubMedCrossRefGoogle Scholar
  55. 55.
    Barber R, McKeith IG, Ballard C, Gholkar A, O’Brien JT (2001) A comparison of medial and lateral temporal lobe atrophy in dementia with Lewy bodies and Alzheimer’s disease: magnetic resonance imaging volumetric study. Dement Geriatr Cogn Disord 12:198–205 doi:10.1159/000051258 PubMedCrossRefGoogle Scholar
  56. 56.
    Burton EJ, McKeith IG, Burn DJ, Williams ED, O’Brien JT (2004) Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with Lewy bodies and controls. Brain 127:791–800 doi:10.1093/brain/awh088 PubMedCrossRefGoogle Scholar
  57. 57.
    Ballmaier M, O’Brien JT, Burton EJ, Thompson PM, Rex DE, Narr KL, McKeith IG, DeLuca H, Toga AW (2004) Comparing gray matter loss profiles between dementia with Lewy bodies and Alzheimer’s disease using cortical pattern matching: diagnosis and gender effects. Neuroimage 23:325–335 doi:10.1016/j.neuroimage.2004.04.026 PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Benoît Magnin
    • 1
    • 2
    • 3
    • 4
  • Lilia Mesrob
    • 2
    • 3
    • 4
  • Serge Kinkingnéhun
    • 2
    • 3
    • 4
    • 5
  • Mélanie Pélégrini-Issac
    • 1
    • 3
    • 4
  • Olivier Colliot
    • 4
    • 6
  • Marie Sarazin
    • 2
    • 3
    • 4
    • 7
  • Bruno Dubois
    • 2
    • 3
    • 4
    • 7
  • Stéphane Lehéricy
    • 2
    • 3
    • 4
    • 8
    • 9
  • Habib Benali
    • 1
    • 3
    • 4
    • 10
  1. 1.UMR-S 678, InsermParisFrance
  2. 2.UMR-S 610, InsermParisFrance
  3. 3.Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06ParisFrance
  4. 4.IFR 49Gif-sur-YvetteFrance
  5. 5.e(ye)BRAINVitry-sur-SeineFrance
  6. 6.UPR 640 LENA, CNRSParisFrance
  7. 7.Department of NeurologyPitié-Salpêtrière HospitalParisFrance
  8. 8.Center for NeuroImaging Research–CENIRUMPC Univ Paris 06ParisFrance
  9. 9.Department of NeuroradiologyPitié-Salpêtrière HospitalParisFrance
  10. 10.UNF/CRIUGM, Université de MontréalMontréalCanada

Personalised recommendations