nach oben

Erschienen in:

01.02.2009 | Diagnostic Neuroradiology

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

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

Erschienen in: Neuroradiology | Ausgabe 2/2009

Einloggen, um Zugang zu erhalten

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.

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 88:1337–1342PubMedCrossRef

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 PubMedCrossRef

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)

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 PubMedCrossRef

DeKosky ST, Marek K (2003) Looking backward to move forward: early detection of neurodegenerative disorders. Science 302:830–834 doi:10.1126/science.1090349 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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)00021-6PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 54:1760–1767PubMed

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 52:91–100PubMed

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 PubMedCrossRef

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 15:929–937PubMed

15.

Jack CR Jr, Petersen RC, O’Brien PC, Tangalos EG (1992) MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology 42:183–188PubMed

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 8:1–8CrossRef

23.

Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20:273–297

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 34:939–944PubMed

31.

Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43:2412–2414PubMed

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 PubMedCrossRef

33.

Benton AL (1968) Genuine memory deficits in dementia. Neuropsychologia 6:53–60 doi:10.1016/0028-3932(68)90038-9 CrossRef

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 9:65

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 44:2300–2307PubMed

36.

Stern Y, Mayeux R, Sano M, Hauser WA, Bush T (1987) Predictors of disease course in patients with probable Alzheimer’s disease. Neurology 37:1649–1653PubMed

37.

Grober E, Buschke H (1987) Genuine memory deficits in dementia. Dev Neuropsychol 3:13–36CrossRef

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 PubMedCrossRef

39.

Benton AL (1974) The revised visual retention test: clinical and experimental applications. Psychological Corporation, New York

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 31:41–55PubMed

41.

Mayeux R, Rosen W (1983) The dementias. Raven, New York

42.

Deloche G, Hannequin D (1997) Test de dénomination orale d’images D080. Les Editions du Centre de Psychologie Appliquée, Paris

43.

Kaplan E, Goodglass H, Weintraub S (1983) The Boston Naming Test. Lea and Febiger, Philadelphia

44.

Dubois B, Slachevsky A, Litvan I, Pillon B (2000) The FAB: a Frontal Assessment Battery at bedside. Neurology 55:1621–1626PubMed

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 695–706

49.

Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. Thieme Medical Publisher, New York

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 PubMedCrossRef

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-# PubMedCrossRef

52.

Redner R, Walker H (1984) Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev 26:195–239 doi:10.1137/1026034 CrossRef

53.

Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New York

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

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 PubMedCrossRef

Titel: Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI
verfasst von: Benoît Magnin
Lilia Mesrob
Serge Kinkingnéhun
Mélanie Pélégrini-Issac
Olivier Colliot
Marie Sarazin
Bruno Dubois
Stéphane Lehéricy
Habib Benali
Publikationsdatum: 01.02.2009
Verlag: Springer-Verlag
Erschienen in: Neuroradiology / Ausgabe 2/2009
Print ISSN: 0028-3940
Elektronische ISSN: 1432-1920
DOI: https://doi.org/10.1007/s00234-008-0463-x

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Neurologie

Nicht Creutzfeldt Jakob, sondern Abführtee-Vergiftung

29.05.2024 Hyponatriämie Nachrichten

Eine ältere Frau trinkt regelmäßig Sennesblättertee gegen ihre Verstopfung. Der scheint plötzlich gut zu wirken. Auf Durchfall und Erbrechen folgt allerdings eine Hyponatriämie. Nach deren Korrektur kommt es plötzlich zu progredienten Kognitions- und Verhaltensstörungen.

Schutz der Synapsen bei Alzheimer

29.05.2024 Morbus Alzheimer Nachrichten

Mit einem Neurotrophin-Rezeptor-Modulator lässt sich möglicherweise eine bestehende Alzheimerdemenz etwas abschwächen: Erste Phase-2-Daten deuten auf einen verbesserten Synapsenschutz.

Sozialer Aufstieg verringert Demenzgefahr

24.05.2024 Demenz Nachrichten

Ein hohes soziales Niveau ist mit die beste Versicherung gegen eine Demenz. Noch geringer ist das Demenzrisiko für Menschen, die sozial aufsteigen: Sie gewinnen fast zwei demenzfreie Lebensjahre. Umgekehrt steigt die Demenzgefahr beim sozialen Abstieg.

Hirnblutung unter DOAK und VKA ähnlich bedrohlich

17.05.2024 Direkte orale Antikoagulanzien Nachrichten

Kommt es zu einer nichttraumatischen Hirnblutung, spielt es keine große Rolle, ob die Betroffenen zuvor direkt wirksame orale Antikoagulanzien oder Marcumar bekommen haben: Die Prognose ist ähnlich schlecht.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

Springer Medizin

Abstract

Purpose

Materials and methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 2/2009

European Society of Neuroradiology (ESNR)

CT angiography and perfusion imaging in patients with subarachnoid hemorrhage: correlation of vasospasm to perfusion abnormality

Endovascular treatment of high-risk tentorial dural arteriovenous fistulas: clinical outcomes

Endovascular treatment of proximal anterior cerebral artery aneurysms

Serial proton MR spectroscopy and diffusion tensor imaging in infantile Balo’s concentric sclerosis