Random forest-based similarity measures for multi-modal classification of Alzheimer's disease

NeuroImage

Volumn 65, Issue , 2013, Pages 167-175

  Gray, Katherine R ^a   Aljabar, Paul ^a,b   Heckemann, Rolf A ^a,c   Hammers, Alexander ^a,c   Rueckert, Daniel ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c CERMEP IMAGERIE DU VIVANT   (France)

Author keywords

Alzheimer's disease; Cerebrospinal fluid biomarkers; Genetics; Magnetic resonance imaging; Mild cognitive impairment; Multi modality classification; Positron emission tomography

Indexed keywords

AMYLOID BETA PROTEIN; APOLIPOPROTEIN E; FLUORODEOXYGLUCOSE F 18; TAU PROTEIN;

ACCURACY; AGED; ALZHEIMER DISEASE; APOE GENE; ARTICLE; CEREBROSPINAL FLUID; CLASSIFICATION; CLASSIFIER; CONTROLLED STUDY; FEMALE; GENOTYPE; HUMAN; KERNEL METHOD; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; RANDOM FOREST;

AGED; ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MODELS, THEORETICAL; POSITRON-EMISSION TOMOGRAPHY;

EID: 84868215748     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.09.065     Document Type: Article

References (74)

1. Aisen P.S., Petersen R.C., Donohue M.C., Gamst A., Raman R., Thomas R.G., Walter S., Trojanowski J.Q., Shaw L.M., Beckett L.A., Jack C.R., Jagust W., Toga A.W., Saykin A.J., Morris J.C., Green R.C., Weiner M.W., the Alzheimer's Disease Neuroimaging Initiative Clinical core of the Alzheimer's disease neuroimaging initiative: progress and plans. Alzheimers Dement. 2010, 6:239-246.
2. Albert M.S., DeKosky S.T., Dickson D., Dubois B., Feldman H.H., Fox N.C., Gamst A., Holtzman D.M., Jagust W.J., Petersen R.C., Snyder P.J., Carrillo M.C., Thies B., Phelps C.H. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011, 7:270-279.
3. Aljabar P., Rueckert D., Crum W.R. Automated morphological analysis of magnetic resonance brain imaging using spectral analysis. Neuroimage 2008, 43:225-235.
4. Aljabar P., Wolz R., Srinivasan L., Counsell S., Boardman J.P., Murgasova M., Doria V., Rutherford M., Edwards A.D., Hajnal J.V., Rueckert D. Combining morphological information in a manifold learning framework: application to neonatal MRI. 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'10) 2010, 1-8.
5. Aljabar P., Wolz R., Rueckert D. Manifold learning for medical image registration, segmentation, and classification. Machine Learning in Computer-aided Diagnosis: Medical Imaging Intelligence and Analysis 2012, IGI Global. K. Suzuki (Ed.).
6. Amit Y., Geman D. Shape quantization and recognition with randomized trees. Neural Comput. 1997, 9:1545-1588.
7. Anchisi D., Borroni B., Franceschi M., Kerrouche N., Kalbe E., Beuthien-Beumann B., Cappa S., Lenz O., Ludecke S., Marcone A., Mielke R., Ortelli P., Padovani A., Pelati O., Pupi A., Scarpini E., Weisenbach S., Herholz K., Salmon E., Holthoff V., Sorbi S., Fazio F., Perani D. Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch. Neurol. 2005, 62:1728-1733.
8. Andreasen N., Minthon L., Davidsson P., Vanmechelen E., Vanderstichele H., Winblad B., Blennow K. Evaluation for CSF-tau and CSF-Abeta42 as diagnostic markers for Alzheimer disease in clinical practice. Arch. Neurol. 2001, 58:373-379.
9. Ashburner J., Friston K.J. Unified segmentation. Neuroimage 2005, 26:839-851.
10. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 2003, 15:1373-1396.
11. Blennow K., Hampel H. CSF markers for incipient Alzheimer's disease. Lancet Neurol. 2003, 2:605-613.
12. Braak H., Braak E. Evolution of neuronal changes in the course of Alzheimer's disease. J. Neural Transm.-Supp. 1998, 53:127-140.
13. Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
14. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
15. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth, Belmont.
16. Chincarini A., Bosco P., Calvini P., Gemme G., Esposito M., Olivieri C., Rei L., Squarcia S., Rodriguez G., Bellotti R., Cerello P., De Mitri I., Retico A., Nobili F. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease. Neuroimage 2011, 58:469-480.
17. Clarkson M.J., Ourselin S., Nielsen C., Leung K.K., Barnes J., Whitwell J.L., Gunter J.L., Hill D.L.G., Weiner M.W., Jack C.R., Fox N.C., the Alzheimer's Disease Neuroimaging Initiative Comparison of phantom and registration scaling corrections using the ADNI cohort. Neuroimage 2009, 47:1506-1513.
18. Corder E.H., Saunders A.M., Strittmatter W.J., Schmechel D.E., Gaskell P.C., Small G.W., Roses A.D., Haines J.L., Pericak-Vance M.A. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 1993, 261:921-923.
19. Cox T.F., Cox M.A.A. Multidimensional Scaling 2001, Chapman and Hall.
20. Criminisi A., Shotton J., Konukoglu E. Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends Comput. Graph. Vis. 2012, 7:81-227.
21. Cuingnet R., Gérardin E., Tessieras J., Auzias G., Lehéricy S., Habert M., Chupin M., Benali H., Colliot O., the Alzheimer's Disease Neuroimaging Initiative Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011, 56:766-781.
22. Neurobiology of Alzheimer's Disease 2007, Oxford University Press, New York. 3rd edition. D. Dawbarn, S.J. Allen (Eds.).
23. Freeborough P.A., Fox N.C., Kitney R.I. Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans. Comput. Methods Programs Biomed. 1997, 53:15-25.
24. Gerber S., Tasdizen T., Fletcher T.P., Whitaker R., the Alzheimer's Disease Neuroimaging Initiative Manifold modeling for brain population analysis. Med. Image Anal. 2010, 14:643-653.
25. Geremia E., Clatz O., Menze B.H., Konukoglu E., Criminisi A., Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage 2011, 57:378-390.
26. Gousias I.S., Rueckert D., Heckemann R.A., Dyet L.E., Boardman J.P., Edwards A.D., Hammers A. Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. Neuroimage 2008, 40:672-684.
27. Gray K.R., Aljabar P., Heckemann R.A., Hammers A., Rueckert D. Random forest-based manifold learning for classification of imaging data in dementia. Machine Learning in Medical Imaging Workshop at 14th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'11) 2011, 159-166.
28. Gray K.R., Wolz R., Heckemann R.A., Aljabar P., Hammers A., Rueckert D., for the Alzheimer's Disease Neuroimaging Initiative Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease. Neuroimage 2012, 60:221-229.
29. Hammers A., Allom R., Koepp M.J., Free S.L., Myers R., Lemieux L., Mitchell T.N., Brooks D.J., Duncan J.S. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum. Brain Mapp. 2003, 19:224-247.
30. Hampel H., Burger K., Teipel S.J., Bokde A.L., Zetterberg H., Blennow K. Core candidate neurochemical and imaging biomarkers of Alzheimer's disease. Alzheimers Dement. 2008, 4:38-48.
31. Hansson O., Zetterberg H., Buchlave P., Londos E., Blennow K., Minthon L. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 2006, 5:228-234.
32. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2011, Springer, New York, (corrected 5th printing of 2nd edition).
33. Heckemann R.A., Keihaninejad S., Aljabar P., Rueckert D., Hajnal J.V., Hammers A., the Alzheimer's Disease Neuroimaging Initative Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. Neuroimage 2010, 51:221-227.
34. Heckemann R.A., Keihaninejad S., Aljabar P., Gray K.R., Nielsen C., Rueckert D., Hajnal J.V., Hammers A., the Alzheimer's Disease Neuroimaging Initiative Automatic morphometry in Alzheimer's disease and mild cognitive impairment. Neuroimage 2011, 56:2024-2037.
35. Herholz K., Salmon E., Perani D., Baron J.C., 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 W.D. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 2002, 17:302-316.
36. Hinrichs C., Singh V., Mukherjee L., Xu G.F., Chung M.K., Johnson S.C., the Alzheimer's Disease Neuroimaging Initiative Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 2009, 48:138-149.
37. Hinrichs C., Singh V., Xu G., Johnson S.C., the Alzheimer's Disease Neuroimaging Initiative Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 2011, 55:574-589.
38. Ho T. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20:832-844.
39. Hoffman E.J., Cutler P.D., Digby W.M., Mazziotta J.C. 3-D phantom to simulate cerebral blood-flow and metabolic images for PET. IEEE Trans. Nucl. Sci. 1990, 37:616-620.
40. Hoffman J.M., Welsh-Bohmer K.A., Hanson M., Crain B., Hulette C., Earl N., Coleman R.E. FDG PET imaging in patients with pathologically verified dementia. J. Nucl. Med. 2000, 41:1920-1928.
41. Hope T.A., Gregson P.H., Linney N.C., Schmidt M.H., Abdolell M. Selecting and assessing quantitative early ultrasound texture measures for their association with cerebral palsy. IEEE Trans. Med. Imaging 2008, 27:228-236.
42. Iglesias J.E., Liu C.Y., Thompson P.M., Tu Z. Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans. Med. Imaging 2011, 30:1617-1634.
43. Joshi A., Koeppe R.A., Fessler J.A. Reducing between scanner differences in multi-center PET studies. Neuroimage 2009, 46:154-159.
44. Keihaninejad S., Heckemann R.A., Gousias I.S., Hajnal J.V., Duncan J.S., Aljabar P., Rueckert D., Hammers A. Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation. PLoS One 2012, 7:e33096.
45. Klunk W.E., Engler H., Nordberg A., Wang Y.M., Blomqvist G., Holt D.P., Bergstrom M., Savitcheva I., Huang G.F., Estrada S., Ausén B., Debnath M.L., Barletta J., Price J.C., Sandell J., Lopresti B.J., Wall A., Koivisto P., Antoni G., Mathis C.A., Långström B. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann. Neurol. 2004, 55:306-319.
46. Landau S.M., Harvey D., Madison C.M., Reiman E.M., Foster N.L., Aisen P.S., Petersen R.C., Shaw L.M., Trojanowski J.Q., Jack C.R., Weiner M.W., Jagust W.J. Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 2010, 75:230-238.
47. Langbaum J.B.S., Chen K., Lee W., Reschke C., Bandy D., Fleisher A.S., Alexander G.E., Foster N.L., Weiner M.W., Koeppe R.A., Jagust W.J., Reiman E.M., the Alzheimer's Disease Neuroimaging Initiative Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Neuroimage 2009, 45:1107-1116.
48. Leung K.K., Barnes J., Modat M., Ridgway G.R., Bartlett J.W., Fox N.C., Ourselin S., the Alzheimer's Disease Neuroimaging Initiative Brain MAPS: an automated, accurate and robust brain extraction technique using a template library. Neuroimage 2011, 55:1091-1108.
49. Liaw A., Wiener M. Classification and regression by randomForest. R News 2002, 2:18-22.
50. Mardia K.V., Kent J.T., Bibby J.M. Multivariate Analysis 1979, Academic Press, London. 5th edition.
51. McKhann G.M., Knopman D.S., Chertkow H., Hyman B.T., Jack C.R., Kawas C.H., Klunk W.E., Koroshetz W.J., Manly J.J., Mayeux R., Mohs R.C., Morris J.C., Rossor M.N., Scheltens P., Carrillo M.C., Thies B., Weintraub S., Phelps C.H. 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. Alzheimers Dement. 2011, 7:263-269.
52. 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. Reduction of beta-amyloid peptide(42) in the cerebrospinal fluid of patients with Alzheimer's disease. Ann. Neurol. 1995, 38:643-648.
53. Patwardhan M.B., McCrory D.C., Matchar D.B., Samsa G.P., Rutschmann O.T. Alzheimer disease: operating characteristics of PET - a meta-analysis. Radiology 2004, 231:73-80.
54. Petersen R.C. Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 2004, 256:183-194.
55. Petersen R.C., Smith G.E., Waring S.C., Ivnik R.J., Tangalos E.G., Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 1999, 56:303-308.
56. Rocca W.A., Hofman A., Brayne C., Breteler M.M.B., Clarke M., Copeland J.R.M., Dartigues J.F., Engedal K., Hagnell O., Heeren T.J., Jonker C., Lindesay J., Lobo A., Mann A.H., Mölsä P.K., Morgan K., O'Connor D.W., Droux A.d.S., Sulkava R., Kay D.W.K., Amaducci L. Frequency and distribution of Alzheimer's disease in Europe: a collaborative study of 1980-1990 prevalence findings. Ann. Neurol. 1991, 30:381-390.
57. Roses A.D., Saunders A.M. ApoE, Alzheimer's disease, and recovery from brain stress. Cerebrovasc. Pathol. Alzheimers Dis. 1997, 826:200-212.
58. Schuff N., Woerner N., Boreta L., Kornfield T., Shaw L.M., Trojanowski J.Q., Thompson P.M., Jack C.R., Weiner M.W., the Alzheimer's Disease Neuroimaging Initiative MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers. Brain 2009, 132:1067-1077.
59. Selkoe D.J. The molecular pathology of Alzheimer's disease. Neuron 1991, 6:487-498.
60. Shi T., Horvath S. Unsupervised learning with random forest predictors. J. Comput. Graph. Stat. 2006, 15:118-138.
61. Shi T., Seligson D., Belldegrun A.S., Palotie A., Horvath S. Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma. Mod. Pathol. 2005, 18:547-557.
62. Silverman D.H.S., Small G.W., Chang C.Y., Lu C.S., de Aburto M.A.K., Chen W., Czernin J., Rapoport S.I., Pietrini P., Alexander G.E., Schapiro M.B., Jagust W.J., Hoffman J.M., Welsh-Bohmer K.A., Alavi A., Clark C.M., Salmon E., de Leon M.J., Mielke R., Cummings J.L., Kowell A.P., Gambhir S.S., Hoh C.K., Phelps M.E. Positron emission tomography in evaluation of dementia - regional brain metabolism and long-term outcome. JAMA 2001, 286:2120-2127.
63. Sperling R.A., Aisen P.S., Beckett L.A., Bennett D.A., Craft S., Fagan A.M., Iwatsubo T., Jack C.R., Kaye J., Montine T.J., Park D.C., Reiman E.M., Rowe C.C., Siemers E., Stern Y., Yaffe K., Carrillo M.C., Thies B., Morrison-Bogorad M., Wagster M.V., Phelps C.H. 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. Alzheimers Dement. 2011, 7:280-292.
64. Tapiola T., Pirttila T., Mehta P.D., Alafuzoff I., Lehtovirta M., Soininen H. Relationship between apoE genotype and CSF beta-amyloid (1-42) and tau in patients with probably and definite Alzheimer's disease. Neurobiol. Aging 2000, 21:735-740.
65. Tenenbaum J., de Silva V., Langford J. A global geometric framework for non-linear dimensionality reduction. Science 2000, 290:2319-2323.
66. Torgerson W.S. Multidimensional scaling: I. Theory and method. Psychometrika 1952, 17:401-419.
67. Trojanowski J.Q., Vandeerstichele H., Korecka M., Clark C.M., Aisen P.S., Petersen R.C., Blennow K., Soares H., Simon A., Lewczuk P., Dean R., Siemers E., Potter W.Z., Weiner M.W., Jack C.R., Jagust W., Toga A.W., Lee V.M.Y., Shaw L.M. Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects. Alzheimers Dement. 2010, 6:230-238.
68. van de Pol L.A., van der Flier W.M., Korf E.S.C., Fox N.C., Barkhof F., Scheltens P. Baseline predictors of rates of hippocampal atrophy in mild cognitive impairment. Neurology 2007, 69:1491-1497.
69. Vandermeeren M., Mercken M., Vanmechelen E., Six J., Vandevoorde A., Martin J.J., Cras P. Detection of tau proteins in normal and Alzheimer's disease cerebrospinal fluid with a sensitive sandwich enzyme-linked immunosorbent assay. J. Neurochem. 1993, 61:1828-1834.
70. Walhovd K.B., Fjell A.M., Dale A.M., McEvoy L.K., Brewer J., Karow D.S., Salmon D.P., Fennema-Notestine C. Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol. Aging 2010, 31:1107-1121.
71. Weiner M.W., Veitch D.P., Aisen P.S., Beckett L.A., Cairns N.J., Green R.C., Harvey D., Jack C.R., Jagust W., Liu E., Morris J.C., Petersen R.C., Saykin A.J., Schmidt M.E., Shaw L., Siuciak J.A., Soares H., Toga A.W., Trojanowski J.Q., the Alzheimer's Disease Neuroimaging Initiative The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement. 2012, 8:S1-S68.
72. Yakushev I., Hammers A., Fellgiebel A., Schmidtmann I., Scheurich A., Buchholz H.G., Peters J., Bartenstein P., Lieb K., Schreckenberger M. SPM-based count normalization provides excellent discrimination of mild Alzheimer's disease and amnestic mild cognitive impairment from healthy aging. Neuroimage 2009, 44:43-50.
73. Zhang D., Wang Y., Zhou L., Yuan H., Shen D., the Alzheimer's Disease Neuroimaging Initiative Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 2011, 55:856-867.
74. Zhang D., Shen D., the Alzheimer's Disease Neuroimaging Initiative Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS One 2012, 7:e33182.