Detectable Neuropsychological Differences in Early Preclinical Alzheimer’s Disease: A Meta-Analysis

  • S. Duke Han
  • Caroline P. Nguyen
  • Nikki H. Stricker
  • Daniel A. Nation
Review

Abstract

The development of methods for in vivo detection of cerebral beta amyloid retention and tau accumulation have been increasingly useful in characterizing preclinical Alzheimer’s disease (AD). While the association between these biomarkers and eventual AD has been demonstrated among cognitively intact older adults, the link between biomarkers and neurocognitive ability remains unclear. We conducted a meta-analysis to test the hypothesis that cognitively intact older adults would show statistically discernable differences in neuropsychological performance by amyloid status (amyloid negative = A-, amyloid positive = A+). We secondarily hypothesized a third group characterized by either CSF tau pathology or neurodegeneration, in addition to amyloidosis (A+/N+ or Stage 2), would show lower neuropsychology scores than the amyloid positive group (A+/N- or Stage 1) when compared to the amyloid negative group. Pubmed, PsychINFO, and other sources were searched for relevant articles, yielding 775 total sources. After review for inclusion/exclusion criteria, duplicates, and risk of bias, 61 studies were utilized in the final meta-analysis. Results showed A+ was associated with poorer performance in the domains of global cognitive function, memory, language, visuospatial ability, processing speed, and attention/working memory/executive functions when compared to A-. A+/N+ showed lower performances on memory measures when compared to A+/N- in secondary analyses based on a smaller subset of studies. Results support the notion that neuropsychological measures are sensitive to different stages of preclinical AD among cognitively intact older adults. Further research is needed to determine what constitutes meaningful differences in neuropsychological performance among cognitively intact older adults.

Keywords

Cognition Preclinical Alzheimer’s disease Biomarker Meta-analysis 

References

  1. Aizenstein, H. J., Nebes, R. D., Saxton, J. A., et al. (2008). Frequent amyloid deposition without significant cognitive impairment among the elderly. Archives of Neurology, 65(11), 1509–1517.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Alcolea, D., Martínez-Lage, P., Sánchez-Juan, P., et al. (2015). Amyloid precursor protein metabolism and inflammation markers in preclinical Alzheimer disease. Neurology, 85(7), 626–633.CrossRefPubMedGoogle Scholar
  3. Amariglio, R. E., Becker, J. A., Carmasin, J., et al. (2012). Subjective cognitive complaints and amyloid burden in cognitively normal older individuals. Neuropsychologia, 50, 2880–2886.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Amariglio, R. E., Mormino, E. C., Pietras, A. C., et al. (2015). Subjective cognitive concerns, amyloid-β, and neurodegeneration in clinically normal elderly. Neurology, 85(1), 56–62.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Andreasen, N., Minthon, L., Davidsson, P., et al. (2001). Evaluation of CSF-tau and CSF-Aβ42 as diagnostic markers for Alzheimer disease in clinical practice. Archives of Neurology, 58, 373–379.CrossRefPubMedGoogle Scholar
  6. Ayutyanont, N., Langbaum, J. B., Hendrix, S. B., et al. (2014). The Alzheimer’s Precention initiative composite cognitive test score: Sample size estimates for the evaluation of preclinical Alzheimer’s disease treatments in presenilin 1 E280A mutation carriers. The Journal of Clinical Psychiatry, 75, 652–660.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Besson, F. L., La Joie, R., Doeuvre, L., et al. (2015). Cognitive and brain profiles associated with current neuroimaging biomarkers of preclinical Alzheimer's disease. The Journal of Neuroscience, 35(29), 10402–10411.CrossRefPubMedGoogle Scholar
  8. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. West Sussex: Wiley.CrossRefGoogle Scholar
  9. Braak, H., & Del Tredici, K. (2015). The preclinical phase of the pathological process underlying sporadic Alzheimer’s disease. Brain, 138, 2814–2833.CrossRefPubMedGoogle Scholar
  10. Buckley, R. F., Maruff, P., Ames, D., et al. (2016). Subjective memory decline predicts greater rates of clinical progression in preclinical Alzheimer's disease. Alzheimer’s & Dementia, 12(7), 796–804.CrossRefGoogle Scholar
  11. Chen, K., Roontiva, A., Thiyyagura, P., et al. (2015). Improved power for characterizing longitudinal amyloid-β PET changes and evaluating amyloid-modifying treatments with a cerebral white matter reference region. The Journal of Nuclear Medicine, 56(4), 560–566.CrossRefPubMedGoogle Scholar
  12. Chételat, G., Villemagne, V. L., Pike, K. E., et al. (2010). Larger temporal volume in elderly with high versus low beta-amyloid deposition. Brain, 133(11), 3349–3358.CrossRefPubMedGoogle Scholar
  13. Chételat, G., Villemagne, V. L., Villain, N., et al. (2012). Accelerated cortical atrophy in cognitively normal elderly with high β-amyloid deposition. Neurology, 78(7), 477–484.CrossRefPubMedGoogle Scholar
  14. Cochran, W. G. (1954). The combination of estimates from different experiments. Biometrics, 1, 101–129.CrossRefGoogle Scholar
  15. Donohue, M. C., Sperling, R. A., Salmon, D. P., et al. (2014). The preclinical Alzheimer cognitive composite: Measuring amyloid-related decline. JAMA Neurology, 71(8), 961–970.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Doraiswamy, P. M., Sperling, R. A., Coleman, R. E., et al. (2012). Amyloid-β assessed by florbetapir F 18 PET and 18-month cognitive decline: A multicenter study. Neurology, 79(16), 1636–1644.CrossRefPubMedGoogle Scholar
  17. Doraiswamy, P. M., Sperling, R. A., Johnson, K., et al. (2014). Florbetapir F 18 amyloid PET and 36-month cognitive decline: A prospective multicenter study. Molecular Psychiatry, 19(9), 1044–1051.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., Jack, C. R. (2016). Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimer’s and Dementia, 12, 292–323.Google Scholar
  19. Duff, K., Norman, N. L., & Hoffman, J. M. (2014). Practice effects and amyloid deposition: Preliminary data on a method for enriching samples in clinical trials. Alzheimer’s Dis Assoc Disord, 28, 247–252.CrossRefGoogle Scholar
  20. Edmonds, E. C., Delano-Wood, L., Galasko, D. R., et al. (2015). Subtle cognitive decline and biomarker staging in preclinical Alzheimer’s disease. Journal of Alzheimer’s Disease, 47, 231–242.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Elman, J. A., Oh, H., Madison, C. M., et al. (2014). Neural compensation in older people with brain amyloid-β deposition. Nature Neuroscience, 17(10), 1316–1318.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Fortea, J., Sala-Llonch, R., Bartrés-Faz, D., et al. (2011). Cognitively preserved subjects with transitional cerebrospinal fluid ß-amyloid 1-42 values have thicker cortex in Alzheimer's disease vulnerable areas. Biological Psychiatry, 70(2), 183–190.CrossRefPubMedGoogle Scholar
  23. Fripp, J., Bourgeat, P., Acosta, O., et al. (2008). Appearance modeling of 11C PiB PET images: Characterizing amyloid deposition in Alzheimer's disease, mild cognitive impairment and healthy aging. NeuroImage, 43(3), 430–439.CrossRefPubMedGoogle Scholar
  24. Gidicsin, C. M., Maye, J. E., Locascio, J. J., et al. (2015). Cognitive activity relates to cognitive performance but not to Alzheimer disease biomarkers. Neurology, 85(1), 48–55.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Gietl, A. F., Warnock, G., Riese, F., et al. (2015). Regional cerebral blood flow estimated by early PiB uptake is reduced in mild cognitive impairment and associated with age in an amyloid-dependent manner. Neurobiology of Aging, 36(4), 1619–1628.CrossRefPubMedGoogle Scholar
  26. Goldman, W. P., Price, J. L., Storandt, M., et al. (2001). Absence of cognitive impairment or decline in preclinical Alzheimer’s disease. Neurology, 56, 361–367.CrossRefPubMedGoogle Scholar
  27. Gu, Y., Razlighi, Q. R., Zahodne, L. B., et al. (2015). Brain amyloid deposition and longitudinal cognitive decline in Nondemented older subjects: Results from a multi-ethnic population. PloS One, 10(7), e0123743.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Hardy, J. A., & Higgins, G. A. (1992). Alzheimer’s disease: The amyloid cascade hypothesis. Science, 256, 184–185.CrossRefPubMedGoogle Scholar
  29. Hardy, J., & Selkoe, D. J. (2002). The amyloid hypothesis of Alzheimer’s disease: Progress and problems on the road to therapeutics. Science, 297, 353–356.CrossRefPubMedGoogle Scholar
  30. Harrington, K. D., Gould, E., Lim, Y. Y., et al. (2016). Amyloid burden and incident depressive symptoms in cognitively normal older adults. International Journal of Geriatric Psychiatry. Advance online publication. doi:10.1002/gps.4489.Google Scholar
  31. Hassenstab, J., Monsell, S. E., Mock, C., et al. (2015). Neuropsychological markers of cognitive decline in persons with Alzheimer disease neuropathology. J Neuropath Exp Neurol, 74, 1086–1092.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Hassenstab, J., Chasse, R., Grabow, P., et al. (2016). Certified normal: Alzheimer’s disease biomarkers and normative estimates of cognitive functioning. Neurobiology of Aging, 43, 23–33.CrossRefPubMedGoogle Scholar
  33. Hatashita, S., & Yamasaki, H. (2010). Clinically different stages of Alzheimer's disease associated by amyloid deposition with [11C]-PIB PET imaging. Journal of Alzheimer’s Disease, 21(3), 995–1003.CrossRefPubMedGoogle Scholar
  34. Hedden, T., Oh, H., Younger, A. P., & Patel, T. A. (2013). Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology, 80(14), 1341–1348.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107–128.CrossRefGoogle Scholar
  36. Hedges, L. V., & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 4, 486–504.CrossRefGoogle Scholar
  37. Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analysis. BMJ, 327, 557–560.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Holland, D., McEvoy, L. K., Desikan, R. S., et al. (2012). Enrichment and stratification for Predementia Alzheimer disease clinical trials. PloS One, 7(10), e47739.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Hsu, P. J., Shou, H., Benzinger, T., et al. (2014). Amyloid burden in cognitively normal elderly is associated with preferential hippocampal subfield volume loss. Journal of Alzheimer’s Disease, 45(1), 27–33.Google Scholar
  40. Huijbers, W., Mormino, E. C., Wigman, S. E., et al. (2014). Amyloid deposition is linked to aberrant entorhinal activity among cognitively normal older adults. The Journal of Neuroscience, 34(15), 5200–5210.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Iturria-Medina, Y., Sotero, R. C., & Toussaint, P. J. (2016). Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nature Communications, 7, article number: 11934.Google Scholar
  42. Jack Jr., C. R., Knopman, D. S., Jagust, W. J., et al. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurology, 9(1), 119–128.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Jack Jr., C. R., Knopman, D. S., Weigand, S. D., et al. (2012). An operational approach to National Institute on Aging-Alzheimer's Association criteria for preclinical Alzheimer disease. Annals of Neurology, 71, 765–775.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Jack Jr., C. R., Knopman, D. S., Jagust, W. J., et al. (2013a). Update on hypothetical model of Alzheimer’s disease biomarkers. Lancet Neurology, 12(2), 207–216.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Jack Jr., C. R., Wiste, H. J., Weigand, S. D., et al. (2013b). Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity. Neurology, 81(20), 1732–1740.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Jack Jr., C. R., Wiste, H. J., Weigand, S. D., et al. (2014). Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50-89 years: A cross-sectional study. Lancet Neurology, 13(10), 997–1005.CrossRefPubMedGoogle Scholar
  47. Jansen, W. J., Ossenkoppele, R., Knol, D. L., Tijms, B. M., Scheltens, P., Verhey, F. R. J., Visser, P. J., & Amyloid Biomarker Study Group. (2015). Prevalence of cerebral amyloid pathology in persons without dementia: A meta-analysis. JAMA, 313(19), 1924–1938.CrossRefPubMedPubMedCentralGoogle Scholar
  48. Jedynak, B. M., Lang, A., Liu, B., et al. (2012). A computational neurodegenerative diease progression score: Method and results with the Alzheimer’s Disease Neuroimaging Initiative cohort. NeuroImage, 63, 1478–1486.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Jessen, F., Amariglio, R. E., van Boxtel, M., et al. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s & Dementia, 10, 844–852.CrossRefGoogle Scholar
  50. Knopman, D. S., Jack Jr., C. R., Wiste, H. J., et al. (2012). Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease. Neurology, 78(20), 1576–1582.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Knopman, D. S., Beiser, A., Machulda, M. M., et al. (2015). Spectrum of cognition short of dementia: Framingham heart study and Mayo Clinic study of aging. Neurology, 85, 1712–1721.CrossRefPubMedPubMedCentralGoogle Scholar
  52. Lamar, M., Resnick, S. M., & Zonderman, A. B. (2003). Longitudinal changes in verbal memory in older adults. Neurology, 60, 82–86.CrossRefPubMedGoogle Scholar
  53. Langbaum, J. B., Hendrix, S. B., Ayutyanont, N., et al. (2014). An empirically derived composite cognitive test score with improved power to track and evaluate treatments for preclinical Alzheimer’s disease. Alzheimer’s & Dementia, 10, 666–674.CrossRefGoogle Scholar
  54. Langbaum, J. B., Hendrix, S. B., Ayutyanont, N., et al. (2015). Establishing composite cognitive endpoints for use in preclinical Alzheimer’s disease trials. The Journal of Prevention of Alzheimer’s Disease, 2(1), 2–3.PubMedPubMedCentralGoogle Scholar
  55. Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLOS Medicine, 6, e1000100.Google Scholar
  56. Lim, H. K., Nebes, R., Snitz, B., et al. (2014). Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects. Brain, 137, 3327–3338.CrossRefPubMedPubMedCentralGoogle Scholar
  57. Lim, Y. Y., Maruff, P., Schindler, R., et al. (2015). Disruption of cholinergic neurotransmission exacerbates Aβ-related cognitive impairment in preclinical Alzheimer’s disease. Neurobiology of Aging, 36, 2709–2715.CrossRefPubMedGoogle Scholar
  58. Lim, Y. Y., Snyder, P. J., Pietrzak, R. H., et al. (2016). Sensitivity of composite scores to amyloid burden in preclinical Alzheimer's disease: Introducing the Z-scores of attention, verbal fluency, and episodic memory for Nondemented older adults composite score. Alzheimer’s & Dementia, 2, 19–26.Google Scholar
  59. Llado-Saz, S., Atienzam, M., & Cantero, J. L. (2015). Increased levels of plasma amyloid-beta are related to cortical thinning and cognitive decline in cognitively normal elderly subjects. Neurobiology of Aging, 36(10), 2791–2797.CrossRefPubMedGoogle Scholar
  60. Machulda, M. M., Hagen, C. E., Wiste, H. J., et al. (in press). Practice effects and longitudinal cognitive change in clinically normal older adutls differ by Alzheimer imaging biomarker status. The Clinical Neuropsychologist. doi:10.1080/13854046.2016.1241303.
  61. Marchant, N. L., Reed, B. R., Sanossian, N., et al. (2013). The aging brain and cognition: Contribution of vascular injury and aβ to mild cognitive dysfunction. JAMA Neurology, 70(4), 488–495.CrossRefPubMedPubMedCentralGoogle Scholar
  62. Mathis, C. A., Kuller, L. H., Klunk, W. E., et al. (2013). In vivo assessment of amyloid-β deposition in nondemented very elderly subjects. Annals of Neurology, 73, 751–761.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Molinuevo, J. L., Ripolles, P., Simó, M., et al. (2014). White matter changes in preclinical Alzheimer's disease: A magnetic resonance imaging-diffusion tensor imaging study on cognitively normal older people with positive amyloid β protein 42 levels. Neurobiology of Aging, 35(12), 2671–2680.CrossRefPubMedGoogle Scholar
  64. Mormino, E. C., Brandel, M. G., Madison, C. M., et al. (2012). Not quite PIB-positive, not quite PIB-negative: Slight PIB elevations in elderly normal control subjects are biologically relevant. NeuroImage, 59, 1152–1160.CrossRefPubMedGoogle Scholar
  65. Nelson, P. T., Alafuzoff, I., Bigio, E. H., Bouras, C., Braak, H., Cairns, N. J., et al. (2012). Correlation of Alzheimer disease neuropathologic changes with cognitive status: A review of the literature. Journal of Neuropathology & Experimental Neurology, 71, 362–381.CrossRefGoogle Scholar
  66. Oh, H., Mormino, E. C., Madison, C., et al. (2010). β-amyloid affects frontal and posterior brain networks in normal aging. NeuroImage, 54, 1887–1895.CrossRefPubMedPubMedCentralGoogle Scholar
  67. Oh, H., Madison, C., Haight, T. J., et al. (2012). Effects of age and β-amyloid on cognitive changes in normal elderly people. Neurobiology of Aging, 33(12), 2746–2755.CrossRefPubMedPubMedCentralGoogle Scholar
  68. Oh, H., Steffener, J., Razlighi, Q. R., et al. (2015). Aβ-related hyperactivation in frontoparietal control regions in cognitively normal elderly. Neurobiology of Aging, 36(12), 3247–3254.CrossRefPubMedPubMedCentralGoogle Scholar
  69. Oh, H., Steffener, J., Razlighi, Q. R., et al. (2016). β-amyloid deposition is associated with decreased right prefrontal activation during task switching among cognitively normal elderly. Journal of Neuroscience, 36(6), 1962–1970.CrossRefPubMedPubMedCentralGoogle Scholar
  70. Ossenkoppele, R., Madison, C., Oh, H., et al. (2014). Is verbal episodic memory in elderly with amyloid deposits preserved through altered neuronal function? Cerebral Cortex, 24(8), 2210–2218.CrossRefPubMedGoogle Scholar
  71. Petersen, R. C., Wiste, H. J., Weigand, S. D., et al. (2016). Association of Elevated Amyloid Levels with Cognition and Biomarkers in cognitively normal people from the community. JAMA Neurology, 73(1), 85–92.CrossRefPubMedPubMedCentralGoogle Scholar
  72. Pike, K. E., Ellis, K. A., Villemagne, V. L., et al. (2011). Cognition and beta-amyloid in preclinical Alzheimer's disease: Data from the AIBL study. Neuropsychologia, 49(9), 2384–2390.CrossRefPubMedGoogle Scholar
  73. Rentz, D. M., Locascio, J. J., Becker, J. A., et al. (2010). Cognition, reserve, and amyloid deposition in normal aging. Annals of Neurology, 67, 353–364.PubMedGoogle Scholar
  74. Schott, J. M., Bartlett, J. W., Fox, N. C., & Barnes, J. (2010). Increased brain atrophy rates in cognitively normal older adults with low cerebrospinal fluid Aβ1-42. Annals of Neurology, 68(6), 825–834.CrossRefPubMedGoogle Scholar
  75. Snitz, B. E., Weissfeld, L. A., Lopez, O. L., et al. (2013). Cognitive trajectories associated with β-amyloid deposition in the oldest-old without dementia. Neurology, 80(15), 1378–1384.CrossRefPubMedPubMedCentralGoogle Scholar
  76. Soldan, A., Pettigrew, C., Cai, Q., et al. (2016). Hypothetical preclinical Alzheimer disease groups and longitudinal cognitive change. JAMA Neurology, 73(6), 698–705.CrossRefPubMedGoogle Scholar
  77. Sperling, R. A., Aisen, P. S., Beckett, L. A., 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 & Dementia, 7(3), 280–292.CrossRefGoogle Scholar
  78. Sperling, R. A., Johnson, K. A., Doraiswamy, P. M., et al. (2013). Amyloid deposition detected with florbetapir F 18 ((18)F-AV-45) is related to lower episodic memory performance in clinically normal older individuals. Neurobiology of Aging, 34(3), 822–831.CrossRefPubMedGoogle Scholar
  79. Stark, S. L., Roe, C. M., Grant, E. A., et al. (2013). Preclinical Alzheimer disease and risk of falls. Neurology, 81, 437–443.CrossRefPubMedPubMedCentralGoogle Scholar
  80. Susanto, T. A., Pua, E. P., & Zhou, J. (2015). Cognition, brain atrophy, and cerebrospinal fluid biomarkers changes from preclinical to dementia stage of Alzheimer's disease and the influence of apolipoprotein e. Journal of Alzheimer’s Disease, 45(1), 253–268.PubMedGoogle Scholar
  81. Thai, C., Lim, Y. Y., Villemagne, V. L., et al. (2015). Amyloid-related memory decline in preclinical Alzheimer's disease is dependent on APOE ε4 and is detectable over 18-months. PloS One, 10(10), e0139082.CrossRefPubMedPubMedCentralGoogle Scholar
  82. Vemuri, P., Lesnick, T. G., Przybelski, S. A., et al. (2015). Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain, 138, 761–771.CrossRefPubMedPubMedCentralGoogle Scholar
  83. Villemagne, V. L., Burnham, S., Bourgeat, P., et al. (2013). Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: A prospective cohort study. Lancet Neurology, 12(4), 357–367.CrossRefPubMedGoogle Scholar
  84. Villeneuve, S., Reed, B. R., Wirth, M., et al. (2014). Cortical thickness mediates the effect of β-amyloid on episodic memory. Neurology, 82(9), 761–767.CrossRefPubMedPubMedCentralGoogle Scholar
  85. Viola, K. L., & Klein, W. L. (2015). Amyloid B oligomers in Alzheimer’s disease pathogenesis, treatment, and diagnosis. Acta Neuropathologica, 129, 183–206.CrossRefPubMedPubMedCentralGoogle Scholar
  86. Vlassenko, A. G., McCue, L., Jasielec, M. S., et al. (2016). Imaging and cerebrospinal fluid biomarkers in early preclinical alzheimer disease. Annals of Neurology, 80(3), 379–387.CrossRefPubMedGoogle Scholar
  87. Voevodskaya, O., Sundgren, P. C., Strandberg, O., et al. (2016). Myo-inositol changes precede amyloid pathology and relate to APOE genotype in Alzheimer disease. Neurology, 86(19), 1754–1761.CrossRefPubMedPubMedCentralGoogle Scholar
  88. Vos, S. J., Xiong, C., Visser, P. J., et al. (2013). Preclinical Alzheimer's disease and its outcome: A longitudinal cohort study. Lancet Neurology, 12(10), 957–965.CrossRefPubMedPubMedCentralGoogle Scholar
  89. Vos, S. J., Gordon, B. A., Su, Y., et al. (2016). NIA-AA staging of preclinical Alzheimer disease: Discordance and concordance of CSF and imaging biomarkers. Neurobiology of Aging, 44, 1–8.CrossRefPubMedGoogle Scholar
  90. Wirth, M., Madison, C. M., Rabinovici, G. D., et al. (2013a). Alzheimer’s disease neurodegenerative biomarkers are associated with decreased cognitive function but not β-amyloid in cognitively normal older individuals. Neurobiology of Disease, 33(13), 5553–5563.Google Scholar
  91. Wirth, M., Oh, H., Mormino, E. C., et al. (2013b). The effect of amyloid β on cognitive decline is modulated by neural integrity in cognitively normal elderly. Alzheimer’s & Dementia, 9(6), 687–698.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • S. Duke Han
    • 1
    • 2
    • 3
    • 4
  • Caroline P. Nguyen
    • 1
  • Nikki H. Stricker
    • 5
  • Daniel A. Nation
    • 3
  1. 1.Department of Family MedicineUSC Keck School of MedicineAlhambraUSA
  2. 2.Department of NeurologyUSC Keck School of MedicineLos AngelesUSA
  3. 3.Department of PsychologyUSC Dornsife CollegeLos AngelesUSA
  4. 4.USC School of GerontologyLos AngelesUSA
  5. 5.Department of Psychiatry and PsychologyMayo ClinicRochesterUSA

Personalised recommendations