Advances in MRI biomarkers for the diagnosis of Alzheimer's disease

Biomarkers in Medicine

Volumn 8, Issue 9, 2014, Pages 1151-1169

  Kehoe, Elizabeth G ^a,b   McNulty, Jonathan P ^c   Mullins, Paul G ^d   Bokde, Arun L W ^a,b  

^a TRINITY COLLEGE DUBLIN   (Ireland)

^b TRINITY COLLEGE DUBLIN   (Ireland)

^c UNIVERSITY COLLEGE DUBLIN   (Ireland)

^d BANGOR UNIVERSITY   (United Kingdom)

Author keywords

Alzheimer's disease; Brain connectivity; Early diagnosis; Magnetic resonance spectroscopy; MRI; Neuroimaging biomarkers

Indexed keywords

AMYLOID; BIOLOGICAL MARKER;

ALZHEIMER DISEASE; BOLD SIGNAL; BRAIN BLOOD FLOW; CONNECTOME; DIFFUSION WEIGHTED IMAGING; FRACTIONAL ANISOTROPY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HIPPOCAMPUS; HUMAN; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; RESTING STATE NETWORK; REVIEW; RISK ASSESSMENT; STANDARDIZATION; WHITE MATTER; FUNCTIONAL NEUROIMAGING; METABOLISM; PROCEDURES; RADIOGRAPHY; TEMPORAL LOBE; TRENDS;

ALZHEIMER DISEASE; BIOLOGICAL MARKERS; FUNCTIONAL NEUROIMAGING; HIPPOCAMPUS; HUMANS; MAGNETIC RESONANCE IMAGING; TEMPORAL LOBE;

EID: 84911471062     PISSN: 17520363     EISSN: 17520371     Source Type: Journal    
DOI: 10.2217/BMM.14.42     Document Type: Review

References (230)

1. Jack CR Jr, Knopman DS, Jagust WJ et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 9(1), 119-128 (2010).
2. Sperling RA, Aisen PS, Beckett LA et al. 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. 7(3), 280-292 (2011).
3. Langbaum JB, Fleisher AS, Chen K et al. Ushering in the study and treatment of preclinical Alzheimer disease. Nat. Rev. Neurol. 9(7), 371-381 (2013).
4. Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. 10(9), 819-828 (2011).
5. Jack CR Jr, Knopman DS, Jagust WJ et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12(2), 207-216 (2013).
6. Ewers M, Frisoni GB, Teipel SJ et al. Staging Alzheimer's disease progression with multimodality neuroimaging. Prog. Neurobiol. 95(4), 535-546 (2011).
7. McKhann GM, Knopman DS, Chertkow H et al. 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. 7(3), 263-269 (2011).
8. Dubois B, Feldman HH, Jacova C et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 6(8), 734-746 (2007).
9. Albert MS, Dekosky ST, Dickson D et al. 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. 7(3), 270-279 (2011).
10. Cummings JL. Biomarkers in Alzheimer's disease drug development. Alzheimers Dement. 7(3), e13-e44 (2011).
11. Hampel H, Wilcock G, Andrieu S et al. Biomarkers for Alzheimer's disease therapeutic trials. Prog. Neurobiol. 95(4), 579-593 (2011).
12. Hampel H, Frank R, Broich K et al. Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat. Rev. Drug Discov. 9(7), 560-574 (2010).
13. Gili T, Cercignani M, Serra L et al. Regional brain atrophy and functional disconnection across Alzheimer's disease evolution. J. Neurol. Neurosurg. Psych. 82(1), 58-66 (2011).
14. Bozzali M, Padovani A, Caltagirone C, Borroni B. Regional grey matter loss and brain disconnection across Alzheimer disease evolution. Curr. Med. Chem. 18(16), 2452-2458 (2011).
15. Osten P, Margrie TW. Mapping brain circuitry with a light microscope. Nat. Methods 10(6), 515-523 (2013).
16. Giorgio A, De Stefano N. Clinical use of brain volumetry. J. Magnet. Reson. Imaging 37(1), 1-14 (2013).
17. Appel J, Potter E, Shen Q et al. A comparative analysis of structural brain MRI in the diagnosis of Alzheimer's disease. Behav. Neurol. 21(1), 13-19 (2009).
18. Bozzali M, Filippi M, Magnani G et al. The contribution of voxel-based morphometry in staging patients with mild cognitive impairment. Neurology 67(3), 453-460 (2006).
19. Dickerson BC, Feczko E, Augustinack JC et al. Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area. Neurobiol. Ag. 30(3), 432-440 (2009).
20. Feczko E, Augustinack JC, Fischl B, Dickerson BC. An MRI-based method for measuring volume, thickness and surface area of entorhinal, perirhinal, and posterior parahippocampal cortex. Neurobiol. Ag. 30(3), 420-431 (2009).
21. Ferreira LK, Diniz BS, Forlenza OV, Busatto GF, Zanetti MV. Neurostructural predictors of Alzheimer's disease: a meta-analysis of VBM studies. Neurobiol. Ag. 32(10), 1733-1741 (2011).
22. Im K, Lee JM, Seo SW et al. Variations in cortical thickness with dementia severity in Alzheimer's disease. Neurosci. Lett. 436(2), 227-231 (2008).
23. Karas GB, Burton EJ, Rombouts SA et al. A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry. NeuroImage 18(4), 895-907 (2003).
24. Lerch JP, Pruessner JC, Zijdenbos A, Hampel H, Teipel SJ, Evans AC. Focal decline of cortical thickness in Alzheimer's disease identified by computational neuroanatomy. Cereb. Cortex 15(7), 995-1001 (2005).
25. Plant C, Teipel SJ, Oswald A et al. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. NeuroImage 50(1), 162-174 (2010).
26. Richards BA, Chertkow H, Singh V et al. Patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. Neurobiol. Ag. 30(10), 1626-1636 (2009).
27. Whitwell JL, Shiung MM, Przybelski SA et al. MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology 70(7), 512-520 (2008).
28. Barnes J, Lewis EB, Scahill RI et al. Automated measurement of hippocampal atrophy using fluid-registered serial MRI in AD and controls. J. Comput. Assist. Tomogr. 31(4), 581-587 (2007).
29. Bishop CA, Jenkinson M, Andersson J, Declerck J, Merhof D. Novel Fast Marching for Automated Segmentation of the Hippocampus (FMASH): method and validation on clinical data. NeuroImage 55(3), 1009-1019 (2011).
30. Ferrarini L, Frisoni GB, Pievani M, Reiber JH, Ganzola R, Milles J. Morphological hippocampal markers for automated detection of Alzheimer's disease and mild cognitive impairment converters in magnetic resonance images. J. Alzheimer's Dis. 17(3), 643-659 (2009).
31. Firbank MJ, Barber R, Burton EJ, O'brien JT. Validation of a fully automated hippocampal segmentation method on patients with dementia. Hum. Brain Map. 29(12), 1442-1449 (2008).
32. Khan AR, Wang L, Beg MF. FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping. NeuroImage 41(3), 735-746 (2008).
33. Konrad C, Ukas T, Nebel C, Arolt V, Toga AW, Narr KL. Defining the human hippocampus in cerebral magnetic resonance images - an overview of current segmentation protocols. NeuroImage 47(4), 1185-1195 (2009).
34. Li S, Shi F, Pu F et al. Hippocampal shape analysis of Alzheimer disease based on machine learning methods. Am. J. Neurorad. 28(7), 1339-1345 (2007).
35. Morra JH, Tu Z, Apostolova LG et al. Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls. Hum. Brain Map. 30(9), 2766-2788 (2009).
36. Morra JH, Tu Z, Apostolova LG et al. Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer's disease mild cognitive impairment, and elderly controls. NeuroImage 43(1), 59-68 (2008).
37. Nugent AC, Luckenbaugh DA, Wood SE, Bogers W, Zarate CA Jr, Drevets WC. Automated subcortical segmentation using FIRST: test-retest reliability, interscanner reliability, and comparison to manual segmentation. Hum. Brain Map. 34(9), 2313-2329 (2013).
38. Shen L, Saykin AJ, Kim S et al. Comparison of manual and automated determination of hippocampal volumes in MCI and early AD. Brain Imaging Behav. 4(1), 86-95 (2010).
39. Arimura H, Yoshiura T, Kumazawa S et al. Automated method for identification of patients with Alzheimer's disease based on three-dimensional MR images. Acad. Radiol. 15(3), 274-284 (2008).
40. Diciotti S, Ginestroni A, Bessi V et al. Identification of mild Alzheimer's disease through automated classification of structural MRI features. Conference Proc. 2012, 428-431 (2012).
41. Dukart J, Mueller K, Barthel H, Villringer A, Sabri O, Schroeter ML. Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI. Psych. Res. 212(3), 230-236 (2013).
42. Frisoni GB, Beltramello A, Weiss C, Geroldi C, Bianchetti A, Trabucchi M. Usefulness of simple measures of temporal lobe atrophy in probable Alzheimer's disease. Dementia 7(1), 15-22 (1996).
43. Lebedev AV, Westman E, Beyer MK et al. Multivariate classification of patients with Alzheimer's and dementia with Lewy bodies using high-dimensional cortical thickness measurements: an MRI surface-based morphometric study. J. Neurol. 260(4), 1104-1115 (2013).
44. Lerch JP, Pruessner J, Zijdenbos AP et al. Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. Neurobiol. Ag. 29(1), 23-30 (2008).
45. Li H, Liu Y, Gong P, Zhang C, Ye J. Hierarchical interactions model for predicting mild cognitive impairment (MCI) to Alzheimer's disease (AD) conversion. PLoS ONE 9(1), e82450 (2014).
46. Peters F, Villeneuve S, Belleville S. Predicting progression to dementia in elderly subjects with mild cognitive impairment using both cognitive and neuroimaging predictors. J. Alzheimer's Dis. 38(2), 307-318 (2014).
47. Thompson PM, Apostolova LG. Computational anatomical methods as applied to ageing and dementia. Br. J. Rad. 80, S78-S91 (2007).
48. Yang ST, Lee JD, Chang TC et al. Discrimination between Alzheimer's disease and mild cognitive impairment using SOM and PSO-SVM. Comput. Math. Methods Med. 2013, 253670 (2013).
49. Urs R, Potter E, Barker W et al. Visual rating system for assessing magnetic resonance images: a tool in the diagnosis of mild cognitive impairment and Alzheimer disease. J. Comput. Assist. Tomogr. 33(1), 73-78 (2009).
50. Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67-77 (2010).
51. Jack CR Jr, Barkhof F, Bernstein MA et al. Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer's disease. Alzheimers Dement. 7(4), 474-485.e474 (2011).
52. Reiman EM, Jagust WJ. Brain imaging in the study of Alzheimer's disease. NeuroImage 61(2), 505-516 (2012).
53. Hua X, Lee S, Hibar DP et al. Mapping Alzheimer's disease progression in 1309 MRI scans: power estimates for different inter-scan intervals. NeuroImage 51(1), 63-75 (2010).
54. Jack CR Jr, Bernstein MA, Borowski BJ et al. Update on the magnetic resonance imaging core of the Alzheimer's disease neuroimaging initiative. Alzheimers Dement. 6(3), 212-220 (2010).
55. Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathologica 112(4), 389-404 (2006).
56. Vemuri P, Jack CR Jr. Role of structural MRI in Alzheimer's disease. Alzheimers Res. Ther. 2(4), 23 (2010).
57. Reiman EM, Uecker A, Caselli RJ et al. Hippocampal volumes in cognitively normal persons at genetic risk for Alzheimer's disease. Ann. Neurol. 44(2), 288-291 (1998).
58. Stephan BC, Hunter S, Harris D et al. The neuropathological profile of mild cognitive impairment (MCI): a systematic review. Mol. Psych. 17(11), 1056-1076 (2012).
59. Risacher SL, Shen L, West JD et al. Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol. Ag. 31(8), 1401-1418 (2010).
60. Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br. J. Psych. 188, 510-518 (2006).
61. Arnone D, McIntosh AM, Ebmeier KP, Munafo MR, Anderson IM. Magnetic resonance imaging studies in unipolar depression: systematic review and meta-regression analyses. Eur. Neuropsychopharmacol. 22(1), 1-16 (2012).
62. Kessing LV. Depression and the risk for dementia. Curr. Opin. Psych. 25(6), 457-461 (2012).
63. Lee GJ, Lu PH, Hua X et al. Depressive symptoms in mild cognitive impairment predict greater atrophy in Alzheimer's disease-related regions. Biol. Psych. 71(9), 814-821 (2012).
64. Choo IH, Lee DY, Oh JS et al. Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer's disease. Neurobiol. Ag. 31(5), 772-779 (2010).
65. Jacobs HI, Van Boxtel MP, Jolles J, Verhey FR, Uylings HB. Parietal cortex matters in Alzheimer's disease: an overview of structural, functional and metabolic findings. Neurosci. Biobehav. Rev. 36(1), 297-309 (2012).
66. Shiino A, Watanabe T, Maeda K, Kotani E, Akiguchi I, Matsuda M. Four subgroups of Alzheimer's disease based on patterns of atrophy using VBM and a unique pattern for early onset disease. NeuroImage 33(1), 17-26 (2006).
67. Fouquet M, Desgranges B, Landeau B et al. Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer's disease. Brain 132(Pt 8), 2058-2067 (2009).
68. Greene SJ, Killiany RJ. Hippocampal subregions are differentially affected in the progression to Alzheimer's disease. Anat. Rec. 295(1), 132-140 (2012).
69. Kerchner GA, Deutsch GK, Zeineh M, Dougherty RF, Saranathan M, Rutt BK. Hippocampal CA1 apical neuropil atrophy and memory performance in Alzheimer's disease. NeuroImage 63(1), 194-202 (2012).
70. Kerchner GA, Hess CP, Hammond-Rosenbluth KE et al. Hippocampal CA1 apical neuropil atrophy in mild Alzheimer disease visualized with 7-T MRI. Neurology 75(15), 1381-1387 (2010).
71. Liu CC, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms, and therapy. Nat. Rev. Neurol. 9(2), 106-118 (2013).
72. Kerchner GA, Berdnik D, Shen JC et al. APOE {varepsilon}4 worsens hippocampal CA1 apical neuropil atrophy and episodic memory. Neurology 82(8), 691-697 (2014).
73. Mueller SG, Schuff N, Yaffe K, Madison C, Miller B, Weiner MW. Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease. Hum. Brain Map. 31(9), 1339-1347 (2010).
74. Yassa MA, Stark SM, Bakker A, Albert MS, Gallagher M, Stark CE. High-resolution structural and functional MRI of hippocampal CA3 and dentate gyrus in patients with amnestic mild cognitive impairment. NeuroImage 51(3), 1242-1252 (2010).
75. Apostolova LG, Dutton RA, Dinov ID et al. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch. Neurol. 63(5), 693-699 (2006).
76. Apostolova LG, Mosconi L, Thompson PM et al. Subregional hippocampal atrophy predicts Alzheimer's dementia in the cognitively normal. Neurobiol. Ag. 31(7), 1077-1088 (2010).
77. Van Leemput K, Bakkour A, Benner T et al. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19(6), 549-557 (2009).
78. Knight WD, Kim LG, Douiri A, Frost C, Rossor MN, Fox NC. Acceleration of cortical thinning in familial Alzheimer's disease. Neurobiol. Ag. 32(10), 1765-1773 (2011).
79. Spulber G, Niskanen E, Macdonald S et al. Evolution of global and local grey matter atrophy on serial MRI scans during the progression from MCI to AD. Curr. Alzheimer Res. 9(4), 516-524 (2012).
80. Yao Z, Hu B, Liang C, Zhao L, Jackson M. A longitudinal study of atrophy in amnestic mild cognitive impairment and normal aging revealed by cortical thickness. PLoS ONE 7(11), e48973 (2012).
81. Tondelli M, Wilcock GK, Nichelli P, De Jager CA, Jenkinson M, Zamboni G. Structural MRI changes detectable up to ten years before clinical Alzheimer's disease. Neurobiol. Ag. 33(4), 825 e825-e836 (2012).
82. Holland D, Dale AM. Nonlinear registration of longitudinal images and measurement of change in regions of interest. Med. Image Anal. 15(4), 489-497 (2011).
83. Hua X, Hibar DP, Ching CR et al. Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials. NeuroImage 66, 648-661 (2013).
84. Freeborough PA, Fox NC. The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Trans. Med. Imaging 16(5), 623-629 (1997).
85. Leung KK, Ridgway GR, Ourselin S, Fox NC. Consistent multi-time-point brain atrophy estimation from the boundary shift integral. NeuroImage 59(4), 3995-4005 (2012).
86. Burton EJ, Barber R, Mukaetova-Ladinska EB et al. Medial temporal lobe atrophy on MRI differentiates Alzheimer's disease from dementia with Lewy bodies and vascular cognitive impairment: a prospective study with pathological verification of diagnosis. Brain 132(Pt 1), 195-203 (2009).
87. Teipel SJ, Born C, Ewers M et al. Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment. NeuroImage 38(1), 13-24 (2007).
88. Davatzikos C, Ruparel K, Fan Y et al. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 28(3), 663-668 (2005).
89. Decarli C, Murphy DG, McIntosh AR, Teichberg D, Schapiro MB, Horwitz B. Discriminant analysis of MRI measures as a method to determine the presence of dementia of the Alzheimer type. Psych. Res. 57(2), 119-130 (1995).
90. Fan Y, Batmanghelich N, Clark CM, Davatzikos C. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39(4), 1731-1743 (2008).
91. Fan Y, Resnick SM, Wu X, Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. NeuroImage 41(2), 277-285 (2008).
92. Vemuri P, Whitwell JL, Kantarci K et al. Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage. NeuroImage 42(2), 559-567 (2008).
93. Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage 55(3), 856-867 (2011).
94. 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 44(4), 1415-1422 (2009).
95. Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol. Ag. 29(4), 514-523 (2008).
96. Da X, Toledo JB, Zee J et al. Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage Clin. 4, 164-173 (2013).
97. Ewers M, Walsh C, Trojanowski JQ et al. Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol. Ag. 33(7), 1203-1214 (2012).
98. Casanova R, Hsu FC, Sink KM et al. Alzheimer's disease risk assessment using large-scale machine learning methods. PLoS ONE 8(11), e77949 (2013).
99. Chincarini A, Bosco P, Calvini P et al. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease. NeuroImage 58(2), 469-480 (2011).
100. Dubois B, Feldman HH, Jacova C et al. Revising the definition of Alzheimer's disease: a new lexicon. Lancet Neurol. 9(11), 1118-1127 (2010).
101. Liu Y, Mattila J, Ruiz MA et al. Predicting AD conversion: comparison between prodromal AD guidelines and computer assisted PredictAD tool. PLoS ONE 8(2), e55246 (2013).
102. Mattila J, Koikkalainen J, Virkki A et al. A disease state fingerprint for evaluation of Alzheimer's disease. J. Alzheimers Dis. 27(1), 163-176 (2011).
103. Cho Y, Seong JK, Jeong Y, Shin SY. Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data. NeuroImage 59(3), 2217-2230 (2012).
104. Quigley H, Colloby SJ, O'Brien JT. PET imaging of brain amyloid in dementia: a review. Int. J. Geriatr. Psychiatry 26(10), 991-999 (2011).
105. Vandenberghe R, Adamczuk K, Dupont P, Laere KV, Chetelat G. Amyloid PET in clinical practice: its place in the multidimensional space of Alzheimer's disease. Neuroimage Clin. 2, 497-511 (2013).
106. Vlassenko AG, Benzinger TL, Morris JC. PET amyloid-beta imaging in preclinical Alzheimer's disease. Biochim. Biophys. Acta 1822(3), 370-379 (2012).
107. Storandt M, Mintun MA, Head D, Morris JC. Cognitive decline and brain volume loss as signatures of cerebral amyloid-beta peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Abeta deposition. Arch. Neurol. 66(12), 1476-1481 (2009).
108. Wolk DA, Klunk W. Update on amyloid imaging: from healthy aging to Alzheimer's disease. Curr. Neurol. Neurosci. Rep. 9(5), 345-352 (2009).
109. Dickerson BC, Bakkour A, Salat DH et al. The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb. Cortex 19(3), 497-510 (2009).
110. Fotenos AF, Mintun MA, Snyder AZ, Morris JC, Buckner RL. Brain volume decline in aging: evidence for a relation between socioeconomic status, preclinical Alzheimer disease, and reserve. Arch. Neurol. 65(1), 113-120 (2008).
111. Mormino EC, Kluth JT, Madison CM et al. Episodic memory loss is related to hippocampal-mediated betaamyloid deposition in elderly subjects. Brain 132(Pt 5), 1310-1323 (2009).
112. Becker JA, Hedden T, Carmasin J et al. Amyloid-beta associated cortical thinning in clinically normal elderly. Ann. Neurol. 69(6), 1032-1042 (2011).
113. Bourgeat P, Chetelat G, Villemagne VL et al. Beta-amyloid burden in the temporal neocortex is related to hippocampal atrophy in elderly subjects without dementia. Neurology 74(2), 121-127 (2010).
114. Whitwell JL, Tosakulwong N, Weigand SD et al. Does amyloid deposition produce a specific atrophic signature in cognitively normal subjects? Neuroimage Clin. 2, 249-257 (2013).
115. Frisoni GB, Bocchetta M, Chetelat G et al. Imaging markers for Alzheimer disease: which vs how. Neurology 81(5), 487-500 (2013).
116. Frisoni GB, Jack CR. Harmonization of magnetic resonancebased manual hippocampal segmentation: a mandatory step for wide clinical use. Alzheimers Dement. 7(2), 171-174 (2011).
117. Boccardi M, Bocchetta M, Apostolova LG et al. Establishing magnetic resonance images orientation for the EADC-ADNI manual hippocampal segmentation protocol. J. Neuroimag. doi:10.1111/jon.12065 (2013) (Epub ahead of print).
118. Boccardi M, Bocchetta M, Ganzola R et al. Operationalizing protocol differences for EADC-ADNI manual hippocampal segmentation. Alzheimers Dement. doi:10.1016/j.jalz.2013.03.001 (2013) (Epub ahead of print).
119. Boccardi M, Ganzola R, Bocchetta M et al. Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol. J. Alzheimers Dis. 26(Suppl. 3), 61-75 (2011).
120. Weiner MW, Veitch DP, Aisen PS et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement. 8(Suppl. 1), S1-S68 (2012).
121. Jack CR Jr, Bernstein MA, Fox NC et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685-691 (2008).
122. Gunter JL, Bernstein MA, Borowski BJ et al. Measurement of MRI scanner performance with the ADNI phantom. Med. Phys. 36(6), 2193-2205 (2009).
123. Kruggel F, Turner J, Muftuler LT. Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort. Neuroimage 49(3), 2123-2133 (2010).
124. Jovicich J, Czanner S, Han X et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 46(1), 177-192 (2009).
125. Mortamet B, Bernstein MA, Jack CR Jr et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn. Reson. Med. 62(2), 365-372 (2009).
126. Robitaille N, Mouiha A, Crepeault B, Valdivia F, Duchesne S; The Alzheimer's Disease Neuroimaging I. Tissue-based MRI intensity standardization: application to multicentric datasets. Int. J. Biomed. Imaging 2012, 347120 (2012).
127. Herholz K. Use of FDG PET as an imaging biomarker in clinical trials of Alzheimer's disease. Biomarkers Med. 6(4), 431-439 (2012).
128. Park HJ, Friston K. Structural and functional brain networks: from connections to cognition. Science 342(6158), 1238411 (2013).
129. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843), 150-157 (2001).
130. Raichle ME, MacLeod AM, Synder AZ, Powers WJ, Gusnard DA. A default mode of brain function. Proc. Natl Acad. Sci. USA 98(2), 676-682 (2001).
131. Walker LC, Jucker M. Amyloid by default. Nature Neurosci. 14(6), 669-670 (2011).
132. Cole DM, Smith SM, Beckmann CF. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front. Syst. Neurosci. 4, 8 (2010).
133. Beason-Held LL. Dementia and the default mode. Curr. Alzheimer Res. 8(4), 361-365 (2011).
134. Jacobs HI, Radua J, Luckmann HC, Sack AT. Meta-analysis of functional network alterations in Alzheimer's disease: toward a network biomarker. Neurosci. Biobehav. Rev. 37(5), 753-765 (2013).
135. Sheline YI, Raichle ME. Resting state functional connectivity in preclinical Alzheimer's disease. Biol. Psych. 74(5), 340-347 (2013).
136. Greicius MD, Srivastava G, Reiss AL, Menon V. Defaultmode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc. Natl Acad. Sci. USA 101(13), 4637-4642 (2004).
137. Koch W, Teipel S, Mueller S et al. Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease. Neurobiol. Ag. 33(3), 466-478 (2012).
138. Drzezga A, Becker JA, Van Dijk KR et al. Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain 134(Pt 6), 1635-1646 (2011).
139. Sheline YI, Raichle ME, Snyder AZ et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol. Psych. 67(6), 584-587 (2010).
140. Sperling RA, Laviolette PS, O'Keefe K et al. Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63(2), 178-188 (2009).
141. Balthazar ML, De Campos BM, Franco AR, Damasceno BP, Cendes F. Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease. Psych. Res.221, 37-42 (2013).
142. Brier MR, Thomas JB, Snyder AZ et al. Loss of intranetwork and internetwork resting state functional connections with Alzheimer's disease progression. J. Neurosci. 32(26), 8890-8899 (2012).
143. Jones DK, Knosche TR, Turner R. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage 73, 239-254 (2013).
144. Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of the brain. Neurotherapeutics 4(3), 316-329 (2007).
145. Mori S, Crain BJ, Chacko VP, Van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45(2), 265-269 (1999).
146. Catheline G, Periot O, Amirault M et al. Distinctive alterations of the cingulum bundle during aging and Alzheimer's disease. Neurobiol. Ag. 31(9), 1582-1592 (2010).
147. Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51(5), 527-539 (2006).
148. Rose SE, Chen F, Chalk JB et al. Loss of connectivity in Alzheimer's disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J. Neurol. Neurosurg. Psych. 69(4), 528-530 (2000).
149. Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF, Schoenberg SO. White matter damage in Alzheimer disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology 243(2), 483-492 (2007).
150. Fellgiebel A, Yakushev I. Diffusion tensor imaging of the hippocampus in MCI and early Alzheimer's disease. J. Alzheimer's Dis. 26(Suppl. 3), 257-262 (2011).
151. Muller MJ, Greverus D, Weibrich C et al. Diagnostic utility of hippocampal size and mean diffusivity in amnestic MCI. Neurobiol. Aging 28(3), 398-403 (2007).
152. Naggara O, Oppenheim C, Rieu D et al. Diffusion tensor imaging in early Alzheimer's disease. Psych. Res. 146(3), 243-249 (2006).
153. Palesi F, Vitali P, Chiarati P et al. DTI and MR volumetry of hippocampus-PC/PCC circuit: in search of early micro- and macrostructural signs of Alzheimers's disease. Neurol. Res. Int. 2012, 517876 (2012).
154. Muller MJ, Greverus D, Weibrich C et al. Diagnostic utility of hippocampal size and mean diffusivity in amnestic MCI. Neurobiol. Ag. 28(3), 398-403 (2007).
155. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron 62(1), 42-52 (2009).
156. Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease progression in dementia. Neuron 73(6), 1204-1215 (2012).
157. Frost B, Diamond MI. Prion-like mechanisms in neurodegenerative diseases. Nat. Rev. Neurosci. 11(3), 155-159 (2010).
158. Zarei M, Patenaude B, Damoiseaux J et al. Combining shape and connectivity analysis: an MRI study of thalamic degeneration in Alzheimer's disease. NeuroImage 49(1), 1-8 (2010).
159. Stricker NH, Salat DH, Foley JM et al. Decreased white matter integrity in neuropsychologically defined mild cognitive impairment is independent of cortical thinning. J. Int. Neuropsychol. Soc. 19(8), 925-937 (2013).
160. Keihaninejad S, Zhang H, Ryan NS et al. An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease. NeuroImage 72, 153-163 (2013).
161. Nowrangi MA, Lyketsos CG, Leoutsakos JM et al. Longitudinal, region-specific course of diffusion tensor imaging measures in mild cognitive impairment and Alzheimer's disease. Alzheimers Dement. 9(5), 519-528 (2013).
162. Clerx L, Visser PJ, Verhey F, Aalten P. New MRI markers for Alzheimer's disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. J. Alzheimer's Dis. 29(2), 405-429 (2012).
163. Zhang Y, Schuff N, Camacho M et al. MRI markers for mild cognitive impairment: comparisons between white matter integrity and gray matter volume measurements. PLoS ONE 8(6), e66367 (2013).
164. Douaud G, Jbabdi S, Behrens TE et al. DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. NeuroImage 55(3), 880-890 (2011).
165. Teipel SJ, Grothe MJ, Filippi M et al. Fractional anisotropy changes in Alzheimer's disease depend on the underlying fiber tract architecture: a multiparametric DTI study using joint independent component analysis. J. Alzheimer's Dis. 41(1), 69-83 (2014).
166. Machulda MM, Ward HA, Borowski B et al. Comparison of memory fMRI response among normal, MCI, and Alzheimer's patients. Neurology 61(4), 500-506 (2003).
167. Rombouts SA, Barkhof F, Veltman DJ et al. Functional MR imaging in Alzheimer's disease during memory encoding. Am. J. Neuroradiol. 21(10), 1869-1875 (2000).
168. Schroder J, Buchsbaum MS, Shihabuddin L et al. Patterns of cortical activity and memory performance in Alzheimer's disease. Biol. Psych. 49(5), 426-436 (2001).
169. Small SA, Perera GM, Delapaz R, Mayeux R, Stern Y. Differential regional dysfunction of the hippocampal formation among elderly with memory decline and Alzheimer's disease. Ann. Neurol. 45(4), 466-472 (1999).
170. Sperling RA, Bates JF, Chua EF et al. fMRI studies of associative encoding in young and elderly controls and mild Alzheimer's disease. J. Neurol. Neurosurg. Psych. 74(1), 44-50 (2003).
171. Schwindt GC, Black SE. Functional imaging studies of episodic memory in Alzheimer's disease: a quantitative meta-analysis. NeuroImage 45(1), 181-190 (2009).
172. Forster S, Vaitl A, Teipel SJ et al. Functional representation of olfactory impairment in early Alzheimer's disease. J. Alzheimer's Dis. 22(2), 581-591 (2010).
173. Bokde AL, Karmann M, Born C et al. Altered brain activation during a verbal working memory task in subjects with amnestic mild cognitive impairment. J. Alzheimer's Dis. 21(1), 103-118 (2010).
174. Veltman DJ, Rombouts SA, Dolan RJ. Maintenance versus manipulation in verbal working memory revisited: an fMRI study. NeuroImage 18(2), 247-256. (2003).
175. Bokde AL, Lopez-Bayo P, Born C et al. Alzheimer disease: functional abnormalities in the dorsal visual pathway. Radiology 254(1), 219-226 (2010).
176. Bokde ALW, Lopez-Bayo P, Born C et al. Functional abnormalities of the visual processing system in subjects with mild cognitive impairment: an fMRI study. Psychiatr. Res. Neuroimaging 163(3), 248-259. (2008).
177. Bokde ALW, Lopez-Bayo P, Meindl T et al. Functional connectivity of the Fusiform Gyrus during a face matching task in subjects with mild cognitive impairment. Brain 129(5), 1113-1124 (2006).
178. Prvulovic D, Hubl D, Sack AT et al. Functional imaging of visuospatial processing in Alzheimer's disease. NeuroImage 17(3), 1403-1414. (2002).
179. Teipel SJ, Bokde AL, Born C et al. Morphological substrate of face matching in healthy ageing and mild cognitive impairment: a combined MRI-fMRI study. Brain 130(Pt 7), 1745-1758 (2007).
180. Bakker A, Krauss GL, Albert MS et al. Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron 74(3), 467-474 (2012).
181. Putcha D, Brickhouse M, O'Keefe K et al. Hippocampal hyperactivation associated with cortical thinning in Alzheimer's disease signature regions in non-demented elderly adults. J. Neurosci. 31(48), 17680-17688 (2011).
182. Dickerson BC, Salat DH, Bates JF et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann. Neurol. 56(1), 27-35 (2004).
183. Dickerson BC, Salat DH, Greve DN et al. Increased hippocampal activation in mild cognitive impairment compared with normal aging and AD. Neurology 65(3), 404-411 (2005).
184. Miller SL, Celone K, Depeau K et al. Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation. Proc. Natl Acad. Sci. USA 105(6), 2181-2186 (2008).
185. O'Brien JL, O'Keefe KM, Laviolette PS et al. Longitudinal fMRI in elderly reveals loss of hippocampal activation with clinical decline. Neurology 74(24), 1969-1976 (2010).
186. Johnson SC, Schmitz TW, Moritz CH et al. Activation of brain regions vulnerable to Alzheimer's disease: the effect of mild cognitive impairment. Neurobiol. Ag. 27(11), 1604-1612 (2006).
187. Johnson SC, Schmitz TW, Trivedi MA et al. The influence of Alzheimer disease family history and apolipoprotein E epsilon4 on mesial temporal lobe activation. J. Neurosci. 26(22), 6069-6076 (2006).
188. Bookheimer SY, Strojwas MH, Cohen MS et al. Patterns of brain activation in people at risk for Alzheimer's disease. N. Engl. J. Med. 343(7), 450-456. (2000).
189. Celone KA, Calhoun VD, Dickerson BC et al. Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: an independent component analysis. J. Neurosci. 26(40), 10222-10231 (2006).
190. Vannini P, Hedden T, Sullivan C, Sperling RA. Differential functional response in the posteromedial cortices and hippocampus to stimulus repetition during successful memory encoding. Hum. Brain Map. 34(7), 1568-1578 (2013).
191. O'Brien JL, O'Keefe KM, Laviolette PS et al. Longitudinal fMRI in elderly reveals loss of hippocampal activation with clinical decline. Neurology 74(24), 1969-1976 (2010).
192. Hamalainen A, Pihlajamaki M, Tanila H et al. Increased fMRI responses during encoding in mild cognitive impairment. Neurobiol. Ag. 28(12), 1889-1903 (2007).
193. Sager TN, Topp S, Torup L, Hanson LG, Egestad B, Moller A. Evaluation of CA1 damage using single-voxel 1H-MRS and un-biased stereology: can non-invasive measures of N-acetyl-asparate following global ischemia be used as a reliable measure of neuronal damage? Brain Res. 892(1), 166-175 (2001).
194. Chang L, Ernst T, Osborn D, Seltzer W, Leonido-Yee M, Poland RE. Proton spectroscopy in myotonic dystrophy: correlations with CTG repeats. Arch. Neurol. 55(3), 305-311 (1998).
195. Kantarci K. 1H magnetic resonance spectroscopy in dementia. Br. J. Rad. 80(2), S146-S152 (2007).
196. Kaiser LG, Schuff N, Cashdollar N, Weiner MW. Age-related glutamate and glutamine concentration changes in normal human brain: 1H MR spectroscopy study at 4 T. Neurobiol. Ag. 26(5), 665-672 (2005).
197. Rupsingh R, Borrie M, Smith M, Wells JL, Bartha R. Reduced hippocampal glutamate in Alzheimer disease. Neurobiol. Ag. 32(5), 802-810 (2011).
198. Foy CM, Daly EM, Glover A et al. Hippocampal proton MR spectroscopy in early Alzheimer's disease and mild cognitive impairment. Brain Topogr. 24(3-4), 316-322 (2011).
199. Wang Z, Zhao C, Yu L, Zhou W, Li K. Regional metabolic changes in the hippocampus and posterior cingulate area detected with 3-Tesla magnetic resonance spectroscopy in patients with mild cognitive impairment and Alzheimer disease. Acta Radiolog. 50(3), 312-319 (2009).
200. Chen W, Song X, Beyea S, D'Arcy R, Zhang Y, Rockwood K. Advances in perfusion magnetic resonance imaging in Alzheimer's disease. Alzheimers Dement. 7(2), 185-196 (2011).
201. Nicolakakis N, Hamel E. Neurovascular function in Alzheimer's disease patients and experimental models. J. Cereb. Blood Flow Metabol. 31(6), 1354-1370 (2011).
202. Alsop DC, Detre JA, Grossman M. Assessment of cerebral blood flow in Alzheimer's disease by spin-labeled magnetic resonance imaging. Ann. Neurol. 47(1), 93-100 (2000).
203. Ishii K, Sasaki M, Yamaji S, Sakamoto S, Kitagaki H, Mori E. Demonstration of decreased posterior cingulate perfusion in mild Alzheimer's disease by means of H215O positron emission tomography. Eur. J. Nucl. Med. 24(6), 670-673 (1997).
204. Harris GJ, Lewis RF, Satlin A et al. Dynamic susceptibility contrast MR imaging of regional cerebral blood volume in Alzheimer disease: a promising alternative to nuclear medicine. Am. J. Neuroradiol. 19(9), 1727-1732 (1998).
205. Johnson NA, Jahng GH, Weiner MW et al. Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spin-labeling MR imaging: initial experience. Radiology 234(3), 851-859 (2005).
206. Du AT, Jahng GH, Hayasaka S et al. Hypoperfusion in frontotemporal dementia and Alzheimer disease by arterial spin labeling MRI. Neurology 67(7), 1215-1220 (2006).
207. Schuff N, Matsumoto S, Kmiecik J et al. Cerebral blood flow in ischemic vascular dementia and Alzheimer's disease, measured by arterial spin-labeling magnetic resonance imaging. Alzheimers Dement. 5(6), 454-462 (2009).
208. Costanza A, Xekardaki A, Kovari E, Gold G, Bouras C, Giannakopoulos P. Microvascular burden and Alzheimer-type lesions across the age spectrum. J. Alzheimer's Dis. 32(3), 643-652 (2012).
209. Iadecola C. The overlap between neurodegenerative and vascular factors in the pathogenesis of dementia. Acta Neuropathol. 120(3), 287-296 (2010).
210. Kalaria RN. Risk factors and neurodegenerative mechanisms in stroke related dementia. Panminerva Med. 54(3), 139-148 (2012).
211. Kim HA, Miller AA, Drummond GR et al. Vascular cognitive impairment and Alzheimer's disease: role of cerebral hypoperfusion and oxidative stress. Naunyn-Schmiedeberg's Archives Pharmacol. 385(10), 953-959 (2012).
212. Pimentel-Coelho PM, Rivest S. The early contribution of cerebrovascular factors to the pathogenesis of Alzheimer's disease. Eur. J. Neurosci. 35(12), 1917-1937 (2012).
213. Snowdon DA, Greiner LH, Mortimer JA, Riley KP, Greiner PA, Markesbery WR. Brain infarction and the clinical expression of Alzheimer disease: the Nun Study. JAMA 277(10), 813-817 (1997).
214. Jellinger KA, Attems J. Prevalence of dementia disorders in the oldest-old: an autopsy study. Acta Neuropathol. 119(4), 421-433 (2010).
215. Newman AB, Fitzpatrick AL, Lopez O et al. Dementia and Alzheimer's disease incidence in relationship to cardiovascular disease in the Cardiovascular Health Study cohort. J. Am. Geriatr. Soc. 53(7), 1101-1107 (2005).
216. Riekse RG, Leverenz JB, McCormick W et al. Effect of vascular lesions on cognition in Alzheimer's disease: a community-based study. J. Am. Geriatr. Soc. 52(9), 1442-1448 (2004).
217. Decarli C, Mungas D, Harvey D et al. Memory impairment, but not cerebrovascular disease, predicts progression of MCI to dementia. Neurology 63(2), 220-227 (2004).
218. Gorelick PB, Scuteri A, Black SE et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 42(9), 2672-2713 (2011).
219. Haight TJ, Landau SM, Carmichael O, Schwarz C, Decarli C, Jagust WJ. Dissociable effects of Alzheimer disease and white matter hyperintensities on brain metabolism. JAMA Neurol. 70(8), 1039-1045 (2013).
220. He J, Farias S, Martinez O, Reed B, Mungas D, Decarli C. Differences in brain volume, hippocampal volume, cerebrovascular risk factors, and apolipoprotein E4 among mild cognitive impairment subtypes. Arch. Neurol. 66(11), 1393-1399 (2009).
221. Kozachuk WE, Decarli C, Schapiro MB, Wagner EE, Rapoport SI, Horwitz B. White matter hyperintensities in dementia of Alzheimer's type and in healthy subjects without cerebrovascular risk factors: a magnetic resonance imaging study. Arch. Neurol. 47(12), 1306-1310 (1990).
222. Lee DY, Fletcher E, Martinez O et al. Regional pattern of white matter microstructural changes in normal aging, MCI, and AD. Neurology 73(21), 1722-1728 (2009).
223. Maillard P, Carmichael O, Fletcher E, Reed B, Mungas D, Decarli C. Coevolution of white matter hyperintensities and cognition in the elderly. Neurology 79(5), 442-448 (2012).
224. Marchant NL, Reed BR, Decarli CS et al. Cerebrovascular disease, beta-amyloid, and cognition in aging. Neurobiol. Ag. 33(5), 1006 e1025-e1036 (2012).
225. Hedden T, Van Dijk KR, Shire EH, Sperling RA, Johnson KA, Buckner RL. Failure to modulate attentional control in advanced aging linked to white matter pathology. Cereb. Cortex 22(5), 1038-1051 (2012).
226. Marchant NL, Reed BR, Sanossian N et al. The aging brain and cognition: contribution of vascular injury and abeta to mild cognitive dysfunction. JAMA Neurol. 70(4), 488-495 (2013).
227. Pluta R, Ulamek M, Jablonski M. Alzheimer's mechanisms in ischemic brain degeneration. Anat. Rec. 292(12), 1863-1881 (2009).
228. Reed BR, Marchant NL, Jagust WJ, Decarli CC, Mack W, Chui HC. Coronary risk correlates with cerebral amyloid deposition. Neurobiol. Ag. 33(9), 1979-1987 (2012).
229. Sagare AP, Bell RD, Zlokovic BV. Neurovascular defects and faulty amyloid-beta vascular clearance in Alzheimer's disease. J. Alzheimer's Dis. 33(Suppl. 1), S87-S100 (2013).
230. Grimmer T, Faust M, Auer F et al. White matter hyperintensities predict amyloid increase in Alzheimer's disease. Neurobiol. Ag. 33(12), 2766-2773 (2012).