Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort

Frontiers in Aging Neuroscience

Volumn 6, Issue JUL, 2014, Pages

  Aguilar, Carlos ^a   Muehlboeck, J Sebastian ^a,b   Mecocci, Patrizia ^c   Vellas, Bruno ^d   Tsolaki, Magda ^e   Kloszewska, Iwona ^f   Soininen, Hilkka ^g   Lovestone, Simon ^b,h   Wahlund, Lars Olof ^a   Simmons, Andrew ^b,h   Westman, Eric ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF PERUGIA   (Italy)

^d UNIVERSITÉ DE TOULOUSE   (France)

^e ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^f MEDICAL UNIVERSITY OF LODZ   (Poland)

^g KUOPIO UNIVERSITY HOSPITAL   (Finland)

^h King s College London   (United Kingdom)

Author keywords

Alzheimer disease; Apoe4; Mci; Mri; Multivariate statistics; Opls

Indexed keywords

ADULT; AGED; ALLELE; ALZHEIMER DISEASE; ALZHEIMER DISEASE ASSESSMENT SCALE; ALZHEIMER DISEASE ASSESSMENT SCALE COGNITVE SUBSCALE; AREA UNDER THE CURVE; ARTICLE; BRAIN ATROPHY; BRAIN REGION; CLINICAL DEMENTIA RATING; CLINICAL EVALUATION; COMPARATIVE STUDY; CONSORTIUM TO ESTABLISH A REGISTRY FOR ALZHEIMER DISEASE; CONTROLLED STUDY; CONVERSION DISORDER; CROSS-SECTIONAL STUDY; DISEASE CLASSIFICATION; FEMALE; FOLLOW UP; GENETIC RISK; HUMAN; IMAGE PROCESSING; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; MINI MENTAL STATE EXAMINATION; MORPHOMETRICS; NAMED INVENTORIES, QUESTIONNAIRES AND RATING SCALES; NEUROPSYCHOLOGICAL TEST; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; ORTHOGONAL PARTIAL LEAST SQUARE; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

EID: 84904641433     PISSN: None     EISSN: 16634365     Source Type: Journal    
DOI: 10.3389/fnagi.2014.00145     Document Type: Article

References (80)

1. Aguilar, C., Westman, E., Muehlboeck, J., Mecocci, P., Vellas, B., Tsolaki, M., et al. (2013). Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment. Psychiatry Res. 212, 89-98. doi: 10.1016/j.pscychresns.2012.11.005
2. Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., et al. (2011). 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. Alzheimer's Dement. 7, 270-79. doi: 10.1016/j.jalz.2011.03.008
3. Apostolova, L. G., Dutton, R. A., Dinov, I. D., Hayashi, K. M., Toga, A. W., Cummings, J. L., et al. (2006). Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch. Neurol. 63, 693. doi: 10.1001/archneur.63.5.693
4. Bogdan, R., Hyde, L. W., and Hariri, A. R. (2012). A neurogenetics approach to understanding individual differences in brain, behavior, and risk for psychopathology. Mol. Psychiatry 18, 288-299. doi: 10.1038/mp.2012.35
5. Burns, A., and Iliffe, S. (2009). Alzheimer's disease. BMJ 338, 467-471. doi: 10.1136/bmj.b158
6. Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., and Trygg, J. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J. Chemometr. 20, 341-351. doi: 10.1002/cem.1006
7. Clark, V. H., Resnick, S. M., Doshi, J., Beason-Held, L. L., Zhou, Y., Ferrucci, L., et al. (2012). Longitudinal imaging pattern analysis (SPARE-CD Index) detects early structural and functional changes before cognitive decline in healthy older adults. Neurobiol. Aging 33, 2733-2745. doi: 10.1016/j.neurobiolaging.2012.01.010
8. Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Rimmler, J. B., et al. (1995). Apolipoprotein E, survival in Alzheimer's Disease patients, and the competing risks of death and Alzheimer's disease. Neurology 45, 1323-1328. doi: 10.1212/WNL.45.7.1323
9. Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G., et al. (1993). Gene dose of apolipoprotein E Type 4 allele and the risk of alzheimer's disease in late onset families. Science 261, 921-923. doi: 10.1126/science.8346443
10. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.-O., et al. (2011). Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, 766-781. doi: 10.1016/j.neuroimage.2010.06.013
11. Cummings, J. L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D. A., and Gornbein, J. (1994). The neuropsychiatric inventory comprehensive assessment of psychopathology in dementia. Neurology 44, 2308. doi: 10.1212/WNL.44.12.2308
12. Dale, A. M., Fischl, B., and Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9, 179-194. doi: 10.1006/nimg.1998.0395
13. Dale, A. M., and Sereno, M. I. (1993). Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci. 5, 162-176. doi: 10.1162/jocn.1993.5.2.162
14. Davatzikos, C., Fan, Y., Wu, X., Shen, D., and Resnick, S. M. (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
15. Davatzikos, C., Xu, F., An, Y., Fan, Y., and Resnick, S. M. (2009). Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 132, 2026-2035. doi: 10.1093/brain/awp091
16. Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-80. doi: 10.1016/j.neuroimage.2006.01.021
17. Dukart, J., Schroeter, M. L., Mueller, K., and The Alzheimer's Disease Neuroimaging Initiative. (2011). Age correction in dementia - matching to a healthy brain. PLoS ONE 6:e22193. doi: 10.1371/journal.pone.0022193
18. Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikström, C, and Wold, S. (2006). Multi- and Megavariate Data Analysis Part I Basic Principles and Applications. Umeå: MKS Umetrics AB.
19. Fan, Y., Batmanghelich, N., Clark, C. M., and 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
20. Ferencz, B., Laukka, E. J., Lövdén, M., Kalpouzos, G., Keller, L., Graff, C., et al. (2013). The influence of APOE and TOMM40 polymorphisms on hippocampal volume and episodic memory in old age. Front. Hum. Neurosci. 7:198. doi: 10.3389/fnhum.2013.00198
21. Ferreira, D., Molina, Y., Machado, A., Westman, E., Wahlund, L.-O., Nieto, A., et al. (2014). Cognitive decline is mediated by gray matter changes during middle age. Neurobiol. Aging 35, 1086-1094. doi: 10.1016/j.neurobiolaging.2013.10.095
22. Fischl, B., and Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. 97, 11050-11055. doi: 10.1073/pnas.200033797
23. Fischl, B., Liu, A., and Dale, A. M. (2001). Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging 20, 70-80. doi: 10.1109/42.906426
24. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., et al. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341-355. doi: 10.1016/S0896-6273(02)00569-X
25. Fischl, B., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., et al. (2004). Sequence-independent segmentation of magnetic resonance images. Neuroimage 23, S69-S84. doi: 10.1016/j.neuroimage.2004.07.016
26. Fischl, B., Sereno, M. I., Tootell, R. B. H., and Dale, A. M. (1999). High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272-284.
27. Folstein, M. F., Folstein, S. E., and McHugh, P. R. (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
28. Galasko, D., Bennett, D., Sano, M., Ernesto, C., Thomas, R., Grundman, M., et al. (1997). An inventory to assess activities of daily living for clinical trials in alzheimer's disease. Alzheimer Dis. Assoc. Disord. 11, 33-39. doi: 10.1097/00002093-199700112-00005
29. Hachinski, V. C., Iliff, L. D., Zilhka, E., Du Boulay, G. H., McAllister, V. L., Marshall, J., et al. (1975). Cerebral blood flow in dementia. Arch. Neurol. 32, 632. doi: 10.1001/archneur.1975.00490510088009
30. Hampel, H., Frank, R., Broich, K., Teipel, S. J., Katz, R. G., Hardy, J., et al. (2010). Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat. Rev. Drug Discov. 9, 560-574. doi: 10.1038/nrd3115
31. Hughes, C. P., Berg, L., Danziger, W. L., Coben, L. A., and Martin, R. L. (1982). A new clinical scale for the staging of dementia. Br. J. Psychiatry 140, 566-572. doi: 10.1192/bjp.140.6.566
32. Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., et al. (2008). The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685-691. doi: 10.1002/jmri.21049
33. Jack, C. R. Jr., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., et al. (2013). Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207-216. doi: 10.1016/S1474-4422(12)70291-0
34. Khan, W., Giampietro, V., Ginestet, C., Dell'Acqua, F., Bouls, D., Newhouse, S., et al. (2014). No differences in hippocampal volume between carriers and non-carriers of the ApoE ε 4 and ε 2 alleles in young healthy adolescents. J. Alzheimer's Dis. 40, 37-43. doi: 10.3233/JAD-131841
35. Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., et al. (2008). Automatic classification of MR scans in Alzheimer's disease. Brain 131, 681-689. doi: 10.1093/brain/awm319
36. Koppel, J., and Goldberg, T. (2009). The genetics of episodic memory. Cogn. Neuropsychiatry 14, 356-376. doi: 10.1080/13546800902990438
37. Lambert, J.-C., and Amouyel, P. (2011). Genetics of Alzheimer's disease: new evidences for an old hypothesis? Curr. Opin. Genet. Dev. 21, 295-301. doi: 10.1016/j.gde.2011.02.002
38. Liu, Y., Paajanen, T., Westman, E., Wahlund, L.-O., Simmons, A., Tunnard, C., et al. (2010a). Effect of APOE ε 4 allele on cortical thicknesses and volumes: the AddNeuroMed study. J. Alzheimer's Dis. 21, 947-966. doi: 10.3233/JAD-2010-100201
39. Liu, Y., Paajanen, T., Westman, E., Zhang, Y., Wahlund, L.-O., Simmons, A., et al. (2010b). APOE ε 2 Allele is associated with larger regional cortical thicknesses and volumes. Dement. Geriatr. Cogn. Disord. 30, 229-237. doi: 10.1159/000320136
40. Liu, Y., Paajanen, T., Zhang, Y., Westman, E., Wahlund, L.-O., Simmons, A., et al. (2011). Combination analysis of neuropsychological tests and structural MRI measures in differentiating AD, MCI and Control groups-The AddNeuroMed study. Neurobiol. Aging 32, 1198-1206. doi: 10.1016/j.neurobiolaging.2009.07.008
41. Llano, D. A., Laforet, G., and Devanarayan, V. (2011). Derivation of a new ADAS-Cog composite using tree-based multivariate analysis: prediction of conversion from mild cognitive impairment to Alzheimer disease. Alzheimer Dis. Assoc. Disord. 25, 73-84. doi: 10.1097/WAD.0b013e3181f5b8d8
42. Magnin, B., Mesrob, L., Kinkingnéhun, S., Pélégrini-Issac, M., Colliot, O., Sarazin, M., et al. (2009). Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. Neuroradiology 51, 73-83. doi: 10.1007/s00234-008-0463-x
43. Maioli, S., Puerta, E., Merino-Serrais, P., Fusari, L., Gil-Bea, F., Rimondini, R., et al. (2012). Combination of apolipoprotein e4 and high carbohydrate diet reduces hippocampal BDNF and Arc levels and impairs memory in young mice. J. Alzheimer's Dis. 32, 341-355. doi: 10.3233/JAD-2012-120697
44. Mangialasche, F., Westman, E., Kivipelto, M., Muehlboeck, J.-S., Cecchetti, R., Baglioni, M., et al. (2013). Classification and prediction of clinical diagnosis of Alzheimer's disease based on MRI and plasma measures of A-/γ-Tocotrienols and Γ-Tocopherol. J. Int. Med. 273, 602-621. doi: 10.1111/joim.12037
45. Mattila, J., Koikkalainen, J., Virkki, A., Simonsen, A., van Gils, M., Waldemar, G., et al. (2011). A disease state fingerprint for evaluation of Alzheimer's disease. J. Alzheimer's Dis. 27, 163-176. doi: 10.3233/JAD-2011-110365
46. McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., and Stadlan, E. M. (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. doi: 10.1212/WNL.34.7.939
47. McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R. Jr., Kawas, C. H., et al. (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, 263-269. doi: 10.1016/j.jalz.2011.03.005
48. Misra, C., Fan, Y., and Davatzikos, C. (2009). 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 44, 1415-1422. doi: 10.1016/j.neuroimage.2008.10.031
49. Morris, J. C., Heyman, A., Mohs, R. C., Hughes, J. P., van Belle, G., Fillenbaum, G., et al. (1989). The consortium to establish a registry for Alzheimer's disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease. Neurology 39, 1159-1165. doi: 10.1212/WNL.39.9.1159
50. O'Brien, J. T. (2007). Role of imaging techniques in the diagnosis of dementia. Br. J. Radiol. 80, S71-S77. doi: 10.1259/bjr/33117326
51. Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourao-Miranda, J., et al. (2010). Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. Neuroimage 50, 162-174. doi: 10.1016/j.neuroimage.2009.11.046
52. Ries, M. L., Carlsson, C. M., Rowley, H. A., Sager, M. A., Gleason, C. E., Asthana, S., et al. (2008). Magnetic resonance imaging characterization of brain structure and function in mild cognitive impairment: a review. J. Am. Geriatr. Soc. 56, 920-934. doi: 10.1111/j.1532-5415.2008.01684.x
53. Rosen, W. G., Mohs, R. C., and Davis, K. L. (1984). A new rating scale for Alzheimer's disease. Am. J. Psychiatry 141, 1356-1364.
54. Segonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D., Hahn, H. K., et al. (2004). A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060-1075. doi: 10.1016/j.neuroimage.2004.03.032
55. Ségonne, F., Pacheco, J., and Fischl, B. (2007). Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans. Med. Imaging 26, 518-529. doi: 10.1109/TMI.2006.887364
56. Simmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B., Tsolaki, M., et al. (2009). MRI measures of Alzheimer's disease and the AddNeuroMed study. Ann. N.Y. Acad. Sci. 1180, 47-55. doi: 10.1111/j.1749-6632.2009.05063.x
57. Simmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B., Tsolaki, M., et al. (2011). The AddNeuroMed framework for multi-centre MRI assessment of Alzheimer's disease: experience from the first 24 months. Int. J. Geriatr. Psychiatry 26, 75-82. doi: 10.1002/gps.2491
58. Sled, J. G., Zijdenbos, A. P., and Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17, 87-97. doi: 10.1109/42.668698
59. Small, B. J., Rosnick, C. B., Fratiglioni, L., and Bäckman, L. (2004). Apolipoprotein E and cognitive performance: a meta-analysis. Psychol. Aging 19, 592. doi: 10.1037/0882-7974.19.4.592
60. Smith, C. D. (2012). Structural imaging in early pre-states of dementia. Biochim. Biophys. Acta 1822, 317-324. doi: 10.1016/j.bbadis.2011.07.002
61. Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., et al. (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, 280-292. doi: 10.1016/j.jalz.2011.03.003
62. Sperling, R. A., Karlawish, J., and Johnson, K. A. (2013). Preclinical Alzheimer disease-the challenges ahead. Nature reviews. Neurology 9, 54-58. doi: 10.1038/nrneurol.2012.241
63. Spulber, G., Simmons, A., Muehlboeck, J.-S., Mecocci, P., Vellas, B., Tsolaki, M., et al. (2013). An MRI-Based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment. J. Int. Med. 273, 396-409. doi: 10.1111/joim.12028
64. Sun, X., Nicholas, J., Walker, A., Wagner, M. T., and Bachman, D. (2012). APOE genotype in the diagnosis of Alzheimer's disease in patients with cognitive impairment. Am. J. Alzheimer's Dis. Other Dement. 27, 315-320. doi: 10.1177/1533317512452037
65. Teipel, S. J., Born, C., Ewers, M., Bokde, A. L. W., Reiser, M. F., Möller, H.-J., et al. (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
66. Thambisetty, M. (2008). Proteome-based identification of plasma proteins associated with hippocampal metabolism in early Alzheimer's disease. J. Neurol. 255, 1712-1720. doi: 10.1007/s00415-008-0006-8
67. Thambisetty, M., Simmons, A., Velayudhan, L., Hye, A., Campbell, J., Zhang, Y., et al. (2010). Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch. Gen. Psychiatry 67, 739. doi: 10.1001/archgenpsychiatry.2010.78
68. Tondelli, M., Wilcock, G. K., Nichelli, P., De Jager, C. A., Jenkinson, M., and Zamboni, G. (2012). Structural MRI changes detectable up to ten years before clinical Alzheimer's disease. Neurobiol. Aging 33, 825.e25-825.e36. doi: 10.1016/j.neurobiolaging.2011.05.018
69. Trygg, J., and Wold, S. (2002). Orthogonal Projections to Latent Structures (O-PLS). J. Chemometr. 16, 119-128. doi: 10.1002/cem.695
70. Velayudhan, L., Proitsi, P., Westman, E., Muehlboeck, J.-S., Mecocci, P., Vellas, B., et al. (2013). Entorhinal cortex thickness predicts cognitive decline in Alzheimer's disease. J. Alzheimer's Dis. 33, 755-766. doi: 10.3233/JAD-2012-121408
71. Vemuri, P., Gunter, J. L., Senjem, M. L., Whitwell, J. L., Kantarci, K., Knopman, D. S., et al. (2008). Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186-97. doi: 10.1016/j.neuroimage.2007.09.073
72. Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., et al. (2009). MRI and CSF biomarkers in Normal, MCI, and AD Subjects: diagnostic discrimination and cognitive correlations. Neurology 73, 287-293. doi: 10.1212/WNL.0b013e3181af79e5
73. Westman, E., Aguilar, C., Muehlboeck, J.-S., and Simmons, A. (2013). Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topogr. 26, 9-23. doi: 10.1007/s10548-012-0246-x
74. Westman, E., Muehlboeck, J., and Simmons, A. (2012). Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. Neuroimage 62, 229-238. doi: 10.1016/j.neuroimage.2012.04.056
75. Westman, E., Simmons, A., Zhang, Y., Muehlboeck, J., Tunnard, C., Liu, Y., et al. (2011). Multivariate analysis of MRI Data for Alzheimer's disease, mild cognitive impairment and healthy controls. Neuroimage 54, 1178-1187. doi: 10.1016/j.neuroimage.2010.08.044
76. Westman, E., Wahlund, L.-O., Foy, C., Poppe, M., Cooper, A., Murphy, D., et al. (2010). Combining MRI and MRS to distinguish between Alzheimer's disease and healthy controls. J. Alzheimer's Dis. 22, 171-181. doi: 10.3233/JAD-2010-100168
77. Whitwell, J. L., Dickson, D. W., Murray, M. E., Weigand, S. D., Tosakulwong, N., Senjem, M. L., et al. (2012). Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: a case-control study. Lancet Neurol. 11, 868-877. doi: 10.1016/S1474-4422(12)70200-4
78. Wiklund, S., Johansson, E., Sjöström, L., Mellerowicz, E. J., Edlund, U., Shockcor, J. P., et al. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 80, 115-122. doi: 10.1021/ac0713510
79. Wisdom, N. M., Callahan, J. L., and Hawkins, K. A. (2011). The effects of apolipoprotein E on non-impaired cognitive functioning: a meta-analysis. Neurobiol. Aging 32, 63-74. doi: 10.1016/j.neurobiolaging.2009.02.003
80. Yesavage, J. A., and Sheikh, J. I. (1986). 9/Geriatric Depression Scale (GDS) recent evidence and development of a shorter violence. Clin. Gerontol. 5, 165-173. doi: 10.1300/J018v05n01_09