Predicting the development of mild cognitive impairment: A new use of pattern recognition

NeuroImage

Volumn 60, Issue 2, 2012, Pages 894-901

  Cui, Yue ^a,b,c   Sachdev, Perminder S ^b,c   Lipnicki, Darren M ^b,c   Jin, Jesse S ^a   Luo, Suhuai ^a   Zhu, Wanlin ^b,c   Kochan, Nicole A ^b,c   Reppermund, Simone ^c   Liu, Tao ^c,d   Trollor, Julian N ^c   Brodaty, Henry ^b,c,e   Wen, Wei ^b,c  

^a UNIVERSITY OF NEWCASTLE   (Australia)

^b PRINCE OF WALES HOSPITAL   (Australia)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

^d UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^e UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Aging; Cognition; Early diagnosis; Mild cognitive impairment; Neuroimaging

Indexed keywords

ACCURACY; AGED; ALGORITHM; ARTICLE; CLASSIFICATION; CLASSIFIER; COMMUNITY LIVING; CONTROLLED STUDY; FEMALE; HUMAN; MALE; MEMORY; MENTAL DETERIORATION; MENTAL PERFORMANCE; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NEUROPSYCHOLOGICAL TEST; PARIETAL LOBE; PATTERN RECOGNITION; PREDICTION; PREFRONTAL CORTEX; PRIORITY JOURNAL; PROGNOSIS; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

AGED; AGED, 80 AND OVER; DISEASE PROGRESSION; EARLY DIAGNOSIS; FEMALE; HUMANS; MALE; MILD COGNITIVE IMPAIRMENT; PATTERN RECOGNITION, PHYSIOLOGICAL;

EID: 84857022452     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.01.084     Document Type: Article

References (80)

1. Akbani R., Kwek S., Japkowicz N. Applying support vector machines to imbalanced datasets. Machine Learning: Ecml 2004. Proceedings 2004, 3201:39-50.
2. Anderson T.M., Sachdev P.S., Brodaty H., Trollor J.N., Andrews G. Effects of sociodemographic and health variables on Mini-Mental State Exam scores in older Australians. Am. J. Geriatr. Psychiatry 2007, 15:467-476.
3. Apostolova L.G., Mosconi L., Thompson P.M., Green A.E., Hwang K.S., Ramirez A., Mistur R., Tsui W.H., de Leon M.J. Subregional hippocampal atrophy predicts Alzheimer's dementia in the cognitively normal. Neurobiol. Aging 2010, 31:1077-1088.
4. Benton A.L. Problems of test construction in the field of aphasia. Cortex 1967, 3:32-58.
5. Benton A.L., Spreen O. Der Benton Test 1996, Verlag Hand Huber, Bern. 7th ed.
6. Bishop C.M. Neural Networks for Pattern Recognition 1995, Clarendon Press; Oxford University Press, Oxford, New York.
7. Blacker D., Lee H., Muzikansky A., Martin E.C., Tanzi R., McArdle J.J., Moss M., Albert M. Neuropsychological measures in normal individuals that predict subsequent cognitive decline. Arch. Neurol. 2007, 64:862-871.
8. Boyle P.A., Wilson R.S., Aggarwal N.T., Tang Y., Bennett D.A. Mild cognitive impairment: risk of Alzheimer disease and rate of cognitive decline. Neurology 2006, 67:441-445.
9. Braak H., Alafuzoff I., Arzberger T., Kretzschmar H., Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 2006, 112:389-404.
10. Bradley A.P. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997, 30:1145-1159.
11. Budson A.E., Price B.H. Memory dysfunction. N. Engl. J. Med. 2005, 352:692-699.
12. Chang Y.-W., Lin C.-J. Feature Ranking Using Linear SVM. JMLR Workshop and Conference Proceedings: Causation and Prediction Challenge 2008, 53-64. IEEE Press, Hong Kong.
13. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines. ACM Trans. Int. Syst. Technol. 2011, 2:27:21-27:27.
14. Chen Y.-W., Lin C.-J. Combining SVMs with Various Feature Selection Strategies. Feature extraction, foundations and applications 2006, Springer.
15. Cherbuin N., Reglade-Meslin C., Kumar R., Jacomb P., Easteal S., Christensen H., Sachdev P., Anstey K.J. Risk factors of transition from normal cognition to mild cognitive disorder: the PATH through Life Study. Dement. Geriatr. Cogn. Disord. 2009, 28:47-55.
16. Cherbuin N., Sachdev P., Anstey K.J. Neuropsychological predictors of transition from healthy cognitive aging to mild cognitive impairment: the PATH through life study. Am. J. Geriatr. Psychiatry 2010, 18:723-733.
17. Costafreda S.G., Dinov I.D., Tu Z., Shi Y., Liu C.Y., Kloszewska I., Mecocci P., Soininen H., Tsolaki M., Vellas B., Wahlund L.O., Spenger C., Toga A.W., Lovestone S., Simmons A. Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. Neuroimage 2011, 56:212-219.
18. Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehericy S., Habert M.O., Chupin M., Benali H., Colliot O. 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.
19. Dai Z., Yan C., Wang Z., Wang J., Xia M., Li K., He Y. Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3) 2011, NeuroImage.
20. Dale A.M., Fischl B., Sereno M.I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999, 9:179-194.
21. Davatzikos C. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. Neuroimage 2004, 23:17-20.
22. de Leon M.J., Convit A., Wolf O.T., Tarshish C.Y., DeSanti S., Rusinek H., Tsui W., Kandil E., Scherer A.J., Roche A., Imossi A., Thorn E., Bobinski M., Caraos C., Lesbre P., Schlyer D., Poirier J., Reisberg B., Fowler J. Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-d-glucose/poitron-emission tomography (FDG/PET). Proc. Natl. Acad. Sci. U.S.A. 2001, 98:10966-10971.
23. Destrieux C., Fischl B., Dale A., Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 2010, 53:1-15.
24. Devanand D.P., Pradhaban G., Liu X., Khandji A., De Santi S., Segal S., Rusinek H., Pelton G.H., Honig L.S., Mayeux R., Stern Y., Tabert M.H., de Leon M.J. Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology 2007, 68:828-836.
25. Ecker C., Marquand A., Mourao-Miranda J., Johnston P., Daly E.M., Brammer M.J., Maltezos S., Murphy C.M., Robertson D., Williams S.C., Murphy D.G. Describing the brain in autism in five dimensions-magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J. Neurosci. 2010, 30:10612-10623.
26. Ewers, M., Walsh, C., Trojanowski, J.Q., Shaw, L.M., Petersen, R.C., Jack, C.R., Jr., Feldman, H.H., Bokde, A.L., Alexander, G.E., Scheltens, P., Vellas, B., Dubois, B., Weiner, M., Hampel, H., in press. Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol. Aging. doi:10.1016/j.neurobiolaging.2010.10.019.
27. Fan Y., Batmanghelich N., Clark C.M., Davatzikos C. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008, 39:1731-1743.
28. Fan Y., Resnick S.M., Wu X., Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. Neuroimage 2008, 41:277-285.
29. Fennema-Notestine C., Hagler D.J., McEvoy L.K., Fleisher A.S., Wu E.H., Karow D.S., Dale A.M. Structural MRI biomarkers for preclinical and mild Alzheimer's disease. Hum. Brain Mapp. 2009, 30:3238-3253.
30. Fischl B., Dale A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 2000, 97:11050-11055.
31. Fischl B., Sereno M.I., Dale A.M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999, 9:195-207.
32. Folstein M.F., Folstein S.E., McHugh P.R. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975, 12:189-198.
33. Greene S.J., Killiany R.J. Subregions of the inferior parietal lobule are affected in the progression to Alzheimer's disease. Neurobiol. Aging 2010, 31:1304-1311.
34. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
35. Hindmarch I., Lehfeld H., de Jongh P., Erzigkeit H. The Bayer Activities of Daily Living Scale (B-ADL). Dement. Geriatr. Cogn. Disord. 1998, 9(Suppl 2):20-26.
36. Hinrichs C., Singh V., Xu G., Johnson S.C. Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. Neuroimage 2011, 55:574-589.
37. Ingalhalikar M., Kanterakis S., Gur R., Roberts T.P., Verma R. DTI based diagnostic prediction of a disease via pattern classification. Med. Image Comput. Comput. Assist. Interv. 2010, 13:558-565.
38. Jack C.R., Knopman D.S., Jagust W.J., Shaw L.M., Aisen P.S., Weiner M.W., Petersen R.C., Trojanowski J.Q. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010, 9:119-128.
39. Japkowicz N., Stephen S. The class imbalance problem: a systematic study. Intell. Data Anal. 2002, 6:429-449.
40. Kantarci K., Petersen R.C., Przybelski S.A., Weigand S.D., Shiung M.M., Whitwell J.L., Negash S., Ivnik R.J., Boeve B.F., Knopman D.S., Smith G.E., Jack C.R. Hippocampal volumes, proton magnetic resonance spectroscopy metabolites, and cerebrovascular disease in mild cognitive impairment subtypes. Arch. Neurol. 2008, 65:1621-1628.
41. Kaplan E. The Boston Naming Test 2001, Lippincott Williams Wilkins, Philadelphia.
42. Klove H. Clinical neuropsychology. Med. Clin. North Am. 1963, 47:1647-1658.
43. Larrieu S., Letenneur L., Orgogozo J.M., Fabrigoule C., Amieva H., Le Carret N., Barberger-Gateau P., Dartigues J.F. Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology 2002, 59:1594-1599.
44. Lemm S., Blankertz B., Dickhaus T., Muller K.R. Introduction to machine learning for brain imaging. Neuroimage 2011, 56:387-399.
45. Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 2005, 17:491-502.
46. Markesbery W.R., Schmitt F.A., Kryscio R.J., Davis D.G., Smith C.D., Wekstein D.R. Neuropathologic substrate of mild cognitive impairment. Arch. Neurol. 2006, 63:38-46.
47. Misra C., Fan Y., Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 2009, 44:1415-1422.
48. Morris J.C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993, 43:2412-2414.
49. Mosconi L., De Santi S., Li J., Tsui W.H., Li Y., Boppana M., Laska E., Rusinek H., de Leon M.J. Hippocampal hypometabolism predicts cognitive decline from normal aging. Neurobiol. Aging 2008, 29:676-692.
50. Müller K.-R., Krauledat M., Dornhege G., Curio G., Blankertz B. Machine learning techniques for brain-computer interfaces. Biomed. Eng. 2004, 49:11-22.
51. Nelson H.E., Willison J. National Adult Reading Test (NART): Test Manual 1991, NFER Nelson, Windsor. 2nd ed.
52. Nelson P.T., Abner E.L., Scheff S.W., Schmitt F.A., Kryscio R.J., Jicha G.A., Smith C.D., Patel E., Markesbery W.R. Alzheimer's-type neuropathology in the precuneus is not increased relative to other areas of neocortex across a range of cognitive impairment. Neurosci. Lett. 2009, 450:336-339.
53. Nestor S.M., Rupsingh R., Borrie M., Smith M., Accomazzi V., Wells J.L., Fogarty J., Bartha R. Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database. Brain 2008, 131:2443-2454.
54. Ohm T.G., Muller H., Braak H., Bohl J. Close-meshed prevalence rates of different stages as a tool to uncover the rate of Alzheimer's disease-related neurofibrillary changes. Neuroscience 1995, 64:209-217.
55. Ohnishi T., Matsuda H., Tabira T., Asada T., Uno M. Changes in brain morphology in Alzheimer disease and normal aging: is Alzheimer disease an exaggerated aging process?. AJNR Am. J. Neuroradiol. 2001, 22:1680-1685.
56. Petersen R.C. Clinical practice. Mild cognitive impairment. N. Engl. J. Med. 2011, 364:2227-2234.
57. Petersen R.C., Doody R., Kurz A., Mohs R.C., Morris J.C., Rabins P.V., Ritchie K., Rossor M., Thal L., Winblad B. Current concepts in mild cognitive impairment. Arch. Neurol. 2001, 58:1985-1992.
58. Raz N., Gunning F.M., Head D., Dupuis J.H., McQuain J., Briggs S.D., Loken W.J., Thornton A.E., Acker J.D. Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cereb. Cortex 1997, 7:268-282.
59. Reitan R.M., Wolfson D. The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation 1993, Neuropsychology Press, Tucson, AZ. 2nd ed.
60. Rey A. L'examen clinique en psychology 1964, Presses Uniersitaires de France, Paris.
61. Sachdev P.S., Brodaty H., Reppermund S., Kochan N.A., Trollor J.N., Draper B., Slavin M.J., Crawford J., Kang K., Broe G.A., Mather K.A., Lux O. The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70-90years. Int. Psychogeriatr. 2010, 22:1248-1264.
62. Smith C.D., Chebrolu H., Wekstein D.R., Schmitt F.A., Jicha G.A., Cooper G., Markesbery W.R. Brain structural alterations before mild cognitive impairment. Neurology 2007, 68:1268-1273.
63. Smith E.E., Egorova S., Blacker D., Killiany R.J., Muzikansky A., Dickerson B.C., Tanzi R.E., Albert M.S., Greenberg S.M., Guttmann C.R. Magnetic resonance imaging white matter hyperintensities and brain volume in the prediction of mild cognitive impairment and dementia. Arch. Neurol. 2008, 65:94-100.
64. 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.
65. Spreen O., Benton A.L. Neurosensory Center Comprehensive Examination for Aphasia: Manual of Instructions (NCCEA) 1969, University of Victoria, Victoria, BC.
66. Van Essen D.C., Drury H.A. Structural and functional analyses of human cerebral cortex using a surface-based atlas. J. Neurosci. 1997, 17:7079-7102.
67. van Norden A.G., de Laat K.F., Fick I., van Uden I.W., van Oudheusden L.J., Gons R.A., Norris D.G., Zwiers M.P., Kessels R.P., de Leeuw F.E. Diffusion tensor imaging of the hippocampus and verbal memory performance: The RUN DMC Study 2011, Hum Brain Mapp.
68. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer, New York.
69. Vemuri P., Gunter J.L., Senjem M.L., Whitwell J.L., Kantarci K., Knopman D.S., Boeve B.F., Petersen R.C., Jack C.R. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 2008, 39:1186-1197.
70. Vemuri P., Wiste H.J., Weigand S.D., Shaw L.M., Trojanowski J.Q., Weiner M.W., Knopman D.S., Petersen R.C., Jack C.R. MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology 2009, 73:294-301.
71. Walhovd K.B., Fjell A.M., Brewer J., McEvoy L.K., Fennema-Notestine C., Hagler D.J., Jennings R.G., Karow D., Dale A.M. Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. AJNR Am. J. Neuroradiol. 2010, 31:347-354.
72. Wechsler D. Wechsler Adult Intelligence Scale-Revised (WAIS-R) 1981, Psychological Corporation, Harcourt, Brace, Jovanovich, San Antonio, TX.
73. Wechsler D. Wechsler Adult Intelligence Scale-Version III (WAIS-III) 1997, Psychological Corporation; Harcourt Brace & Co., San Antonio, TX.
74. Wechsler D. Wechsler Memory Scale-Version III (WMS-III) 1997, Psychological Corporation; Harcourt Brace & Co., San Antonio, TX.
75. Wee C.Y., Yap P.T., Li W., Denny K., Browndyke J.N., Potter G.G., Welsh-Bohmer K.A., Wang L., Shen D. Enriched white matter connectivity networks for accurate identification of MCI patients. Neuroimage 2011, 54:1812-1822.
76. Whitwell J.L., Petersen R.C., Negash S., Weigand S.D., Kantarci K., Ivnik R.J., Knopman D.S., Boeve B.F., Smith G.E., Jack C.R. Patterns of atrophy differ among specific subtypes of mild cognitive impairment. Arch. Neurol. 2007, 64:1130-1138.
77. Wilson R.S., Schneider J.A., Arnold S.E., Tang Y., Boyle P.A., Bennett D.A. Olfactory identification and incidence of mild cognitive impairment in older age. Arch. Gen. Psychiatry 2007, 64:802-808.
78. Winblad B., Palmer K., Kivipelto M., Jelic V., Fratiglioni L., Wahlund L.O., Nordberg A., Backman 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 R.C. Mild cognitive impairment-beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 2004, 256:240-246.
79. Wolk D.A., Dickerson B.C. Fractionating verbal episodic memory in Alzheimer's disease. Neuroimage 2011, 54:1530-1539.
80. Zhang D., Wang Y., Zhou L., Yuan H., Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 2011, 55:856-867.