Manifold Forests for Multi-modality Classification of Alzheimer’s Disease

  • K. R. Gray
  • P. Aljabar
  • R. A. Heckemann
  • A. Hammers
  • D. Rueckert
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Neurodegenerative disorders, such as Alzheimer’s disease, are associated with changes in multiple neuroimaging and biological measures. These may provide complementary information for diagnosis and prognosis. This chapter describes a framework within which a supervised version of manifold forests is used to perform multi-modality classification of patients with Alzheimer’s disease, patients with mild cognitive impairment, and elderly cognitively normal individuals. In this chapter, manifold forests are used to derive supervised similarity measures, with the aim of generating manifolds that are optimal for the task of clinical group discrimination. Embeddings are thus learned from labeled training data and used to infer the clinical labels of test data mapped into this space. Similarities from multiple (image- and non-image-based) modalities are combined to generate an embedding that simultaneously encodes information from all diverse features. Multi-modality classification is performed using coordinates from this joint embedding. Manifold forests provide consistent pairwise similarity measures for multiple modalities, thus facilitating the combination of different types of feature data.

Keywords

Multiple Modality Gini Index Mild Cognitive Impairment Patient Classification Forest Balance Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

References

  1. 2.
    Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH (2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Ageing-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement 7(3) Google Scholar
  2. 40.
    Braak H, Braak E (1998) Evolution of neuronal changes in the course of Alzheimer’s disease. J Neural Transm Suppl 53 Google Scholar
  3. 42.
    Breiman L (1996) Bagging predictors. Mach Learn 24(2) Google Scholar
  4. 44.
    Breiman L (2001) Random forests. Mach Learn 45(1) Google Scholar
  5. 45.
    Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall/CRC, London MATHGoogle Scholar
  6. 47.
    Brookmeyer R, Johnson E, Ziegler-Grahamm K, Arrighi HM (2007) Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dement 3(3) Google Scholar
  7. 71.
    Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW, Roses AD, Haines JL, Pericak-Vance MA (1993) Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261(5123) Google Scholar
  8. 73.
    Cox TF, Cox MAA (2001) Multidimensional scaling. Chapman and Hall, London MATHGoogle Scholar
  9. 86.
    Dawbarn D, Allen SJ (eds) (2007) Neurobiology of Alzheimer’s disease, 3rd edn. Oxford University Press, New York Google Scholar
  10. 142.
    Gray KR, Aljabar P, Heckeman RA, Hammers A, Rueckert D (2011) Random forest-based manifold learning for classification of imaging data in dementia. In: Proc medical image computing and computer assisted intervention (MICCAI) Google Scholar
  11. 143.
    Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D (2013) Random forest-based similarity measures for multi-modal classification of Alzheimer’s Disease. Neuroimage 65:167–175 CrossRefGoogle Scholar
  12. 150.
    Hampel H, Burger K, Teipel SJ, Bokde AL, Zetterberg H, Blennow K (2008) Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimer’s Dement 4(1) Google Scholar
  13. 151.
    Hansson O, Zetterberg H, Buchlave P, Londos E, Blennow K, Minthon L (2006) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 5(3) Google Scholar
  14. 154.
    Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, Berlin MATHGoogle Scholar
  15. 159.
    Heckemann RA, Keihaninejad S, Aljabar P, Gray KR, Nielsen C, Rueckert D, Hajnal JV, Hammers A, The Alzheimer’s Disease Neuroimaging Initiative (2011) Automatic morphometry in Alzheimer’s disease and mild cognitive impairment. NeuroImage 56(4) Google Scholar
  16. 161.
    Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frolich L, Schonknecht P, Ito K, Mielke R, Kalbe E, Zundorf G, Delbeuck X, Pelati O, Anchisi D, Fazio F, Kerrouche N, Desgranges B, Eustache F, Beuthien-Baumann B, Menzel C, Schroder J, Kato T, Arahata Y, Henze M, Heiss WD (2002) Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. NeuroImage 17(1) Google Scholar
  17. 162.
    Hinrichs C, Singh V, Xu G, Johnson SC, The Alzheimer’s Disease Neuroimaging Initiative (2011) Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage 55(2) Google Scholar
  18. 183.
    Klafki H-W, Staufenbiel M, Kornhuber J, Wiltfang J (2006) Therapeutic approaches to Alzheimer’s disease. Brain 129(11) Google Scholar
  19. 185.
    Klunk WE, Engler H, Nordberg A, Wang YM, Blomqvist G, Holt DP, Bergstrom M, Savitcheva I, Huang GF, Estrada S, Ausén B, Debnath ML, Barletta J, Price JC, Sandell J, Lopresti BJ, Wall A, Koivisto P, Antoni G, Mathis CA, Långström B (2004) Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-B. Ann Neurol 55(3) Google Scholar
  20. 187.
    Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: 14th intl joint conference on artificial intelligence (IJCAI), vol 2 Google Scholar
  21. 201.
    Landau SM, Harvey D, Madison CM, Reiman EM, Foster NL, Aisen PS, Petersen RC, Shaw LM, Trojanowski JQ, Jack CR Jr., Weiner MW, Jagust WJ (2010) Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 75(3) Google Scholar
  22. 202.
    Langbaum JBS, Chen K, Lee W, Reschke C, Bandy D, Fleisher AS, Alexander GE, Foster NL, Weiner MW, Koeppe RA, Jagust WJ, Reiman EM, The Alzheimer’s Disease Neuroimaging Initiative (2009) Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer’s disease neuroimaging initiative (ADNI). NeuroImage 45(4) Google Scholar
  23. 221.
    Liang Z-P, Lauterbur PC (1999) Principles of magnetic resonance imaging: a signal processing perspective. IEEE Press/Wiley, New York CrossRefGoogle Scholar
  24. 222.
    Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2 Google Scholar
  25. 231.
    Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis, 5th edn. Academic Press, London MATHGoogle Scholar
  26. 243.
    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(7) Google Scholar
  27. 244.
    McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr., Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement 7(3) Google Scholar
  28. 256.
    Motter R, Vigopelfrey C, Kholodenko D, Barbour R, Johnsonwood K, Galasko D, Chang L, Miller B, Clark C, Green R, Olson D, Southwick P, Wolfert R, Munroe B, Lieberburg I, Seubert P, Schenk D (1995) Reduction of beta-amyloid peptide(42) in the cerebrospinal fluid of patients with Alzheimer’s disease. Ann Neurol 38(4) Google Scholar
  29. 284.
    Patwardhan MB, McCrory DC, Matchar DB, Samsa GP, Rutschmann OT (2004) Alzheimer disease: operating characteristics of PET—a meta-analysis. Radiology 231(1) Google Scholar
  30. 289.
    Petersen RC (2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256(3) Google Scholar
  31. 310.
    Rocca WA, Hofman A, Brayne C, Breteler MMB, Clarke ML, Copeland JRM, Dartigues J-F, Engedal K, Hagnell O, Heeren TJ, Jonker C, Lindesay J, Lobo A, Mann AH, Mls PK, Morgan K, O’Connor DLW, da Silva Droux A, Sulkava R, Kay DWK, Amaducci L (1991) Frequency and distribution of Alzheimer’s disease in Europe: a collaborative study of 1980–1990 prevalence findings. Ann Neurol 30(3) Google Scholar
  32. 313.
    Roses AD, Saunders AM (1997) ApoE, Alzheimer’s disease, and recovery from brain stress. Cerebrovasc Pathol Alzheimer’s Dis 826 Google Scholar
  33. 316.
    Rudin M (2005) Molecular imaging: basic principles and applications in biomedical research. Imperial College Press, London CrossRefGoogle Scholar
  34. 329.
    Selkoe DJ (1991) The molecular pathology of Alzheimer’s disease. Neuron 6(4) Google Scholar
  35. 354.
    Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR Jr., Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH (2011) Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement 7(3) Google Scholar
  36. 368.
    Torgerson WS (1952) Multidimensional scaling: I. Theory and method. Psychometrika 17(4) Google Scholar
  37. 372.
    Trojanowski JQ, Vandeerstichele H, Korecka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, Lewczuk P, Dean R, Siemers E, Potter WZ, Weiner MW, Jack CR Jr., Jagust W, Toga AW, Lee VM-Y, Shaw LM (2010) Update on the biomarker core of the Alzheimer’s disease neuroimaging initiative subjects. Alzheimer’s Dement 6(3) Google Scholar
  38. 379.
    Vandermeeren M, Mercken M, Vanmechelen E, Six J, Vandevoorde A, Martin JJ, Cras P (1993) Detection of tau proteins in normal and Alzheimer’s disease cerebrospinal fluid with a sensitive sandwich enzyme-linked immunosorbent assay. J Neurochem 61(5) Google Scholar
  39. 394.
    Walhovd KB, Fjell AM, Dale AM, McEvoy LK, Brewer J, Karow DS, Salmon DP, Fennema-Notestine C (2010) Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol Aging 31(7) Google Scholar
  40. 406.
    Yakushev I, Hammers A, Fellgiebel A, Schmidtmann I, Scheurich A, Buchholz HG, Peters J, Bartenstein P, Lieb K, Schreckenberger M (2009) SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. NeuroImage 44(1) Google Scholar
  41. 417.
    Zhang D, Wang Y, Zhou L, Yuan H, Shen D, The Alzheimer’s Disease Neuroimaging Initiative (2011) Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3) Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • K. R. Gray
    • 1
  • P. Aljabar
    • 1
    • 2
  • R. A. Heckemann
    • 1
    • 3
  • A. Hammers
    • 1
    • 3
  • D. Rueckert
    • 1
  1. 1.Imperial College LondonLondonUK
  2. 2.King’s College LondonLondonUK
  3. 3.Fondation NeurodisLyonFrance

Personalised recommendations