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PET imaging of neural activity, β-amyloid, and tau in normal brain aging

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Abstract

Normal brain aging is commonly associated with neural activity alteration, β-amyloid (Aβ) deposition, and tau aggregation, driving a progressive cognitive decline in normal elderly individuals. Positron emission tomography (PET) with radiotracers targeting these age-related changes has been increasingly employed to clarify the sequence of their occurrence and the evolution of clinically cognitive deficits. Herein, we reviewed recent literature on PET-based imaging of normal human brain aging in terms of neural activity, Aβ, and tau. Neural hypoactivity reflected by decreased glucose utilization with PET imaging has been predominately reported in the frontal, cingulate, and temporal lobes of the normal aging brain. Aβ PET imaging uncovers the pathophysiological association of Aβ deposition with cognitive aging, as well as the potential mechanisms. Tau-associated cognitive changes in normal aging are likely independent of but facilitated by Aβ as indicated by tau and Aβ PET imaging. Future longitudinal studies using multi-radiotracer PET imaging combined with other neuroimaging modalities, such as magnetic resonance imaging (MRI) morphometry, functional MRI, and magnetoencephalography, are essential to elucidate the neuropathological underpinnings and interactions in normal brain aging.

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References

  1. Kirkwood TB, Austad SN. Why do we age? Nature. 2000;408:233–8. https://doi.org/10.1038/35041682.

    Article  CAS  PubMed  Google Scholar 

  2. Campisi J. Senescent cells, tumor suppression, and organismal aging: good citizens, Bad Neighbors. Cell. 2005;120:513–22. https://doi.org/10.1016/j.cell.2005.02.003.

    Article  CAS  PubMed  Google Scholar 

  3. Lockhart SN, DeCarli C. Structural imaging measures of brain aging. Neuropsychol Rev. 2014;24:271–89. https://doi.org/10.1007/s11065-014-9268-3.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Weis S, Sonnberger M, Dunzinger A, Voglmayr E, Aichholzer M, Kleiser R, et al. Normal aging brain. Imaging Brain Diseases. Berlin: Springer; 2019. p. 871–95.

    Book  Google Scholar 

  5. Marks SM, Lockhart SN, Baker SL, Jagust WJ. Tau and beta-amyloid are associated with medial temporal lobe structure, function, and memory encoding in normal aging. J Neurosci. 2017;37:3192–201. https://doi.org/10.1523/JNEUROSCI.3769-16.2017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Caserta MT, Bannon Y, Fernandez F, Giunta B, Schoenberg MR, Tan J. Normal brain aging: clinical, immunological, neuropsychological, and neuroimaging features. Int Rev Neurobiol. 2009;84:1–19. https://doi.org/10.1016/S0074-7742(09)00401-2.

    Article  PubMed  Google Scholar 

  7. Gan KJ, Sudhof TC. Specific factors in blood from young but not old mice directly promote synapse formation and NMDA-receptor recruitment. Proc Natl Acad Sci U S A. 2019;116:12524–33. https://doi.org/10.1073/pnas.1902672116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. World Population Ageing. United Nations. 2017:2017 https://www.un.org/development/desa/pd/content/world-population-ageing-2017.

  9. Bloom DE, Chatterji S, Kowal P, Lloyd-Sherlock P, McKee M, Rechel B, et al. Macroeconomic implications of population ageing and selected policy responses. Lancet. 2015;385:649–57. https://doi.org/10.1016/s0140-6736(14)61464-1.

    Article  PubMed  Google Scholar 

  10. Colom M, Vidal B, Zimmer L. Is there a role for GPCR agonist radiotracers in PET neuroimaging? Front Mol Neurosci. 2019;12:255. https://doi.org/10.3389/fnmol.2019.00255.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Small GW, Bookheimer SY, Thompson PM, Cole GM, Huang SC, Kepe V, et al. Current and future uses of neuroimaging for cognitively impaired patients. Lancet Neurol. 2008;7:161–72. https://doi.org/10.1016/s1474-4422(08)70019-x.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Shen X, Liu H, Hu Z, Hu H, Shi P. The relationship between cerebral glucose metabolism and age: report of a large brain PET data set. PLoS One. 2012;7:e51517. https://doi.org/10.1371/journal.pone.0051517.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jeong H, Park J, Song I, Chung Y, Rhie S. Changes in cognitive function and brain glucose metabolism in elderly women with subjective memory impairment: a 24-month prospective pilot study. Acta Neurol Scand. 2017;135:108–14. https://doi.org/10.1111/ane.12569.

    Article  CAS  PubMed  Google Scholar 

  14. Yoshizawa H, Gazes Y, Stern Y, Miyata Y, Uchiyama S. Characterizing the normative profile of 18F-FDG PET brain imaging: sex difference, aging effect, and cognitive reserve. Psychiatry Res. 2014;221:78–85. https://doi.org/10.1016/j.pscychresns.2013.10.009.

    Article  PubMed  Google Scholar 

  15. Lecouvey G, Quinette P, Kalpouzos G, Guillery-Girard B, Bejanin A, Gonneaud J, et al. Binding in working memory and frontal lobe in normal aging: is there any similarity with autism? Front Hum Neurosci. 2015;9:90. https://doi.org/10.3389/fnhum.2015.00090.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Van Der Gucht A, Verger A, Guedj E, Malandain G, Hossu G, Yagdigul Y, et al. Age-related changes in FDG brain uptake are more accurately assessed when applying an adaptive template to the SPM method of voxel-based quantitative analysis. Ann Nucl Med. 2015;29:921–8. https://doi.org/10.1007/s12149-015-1022-2.

    Article  Google Scholar 

  17. Greve DN, Salat DH, Bowen SL, Izquierdo-Garcia D, Schultz AP, Catana C, et al. Different partial volume correction methods lead to different conclusions: an (18)F-FDG-PET study of aging. Neuroimage. 2016;132:334–43. https://doi.org/10.1016/j.neuroimage.2016.02.042.

    Article  PubMed  Google Scholar 

  18. Hsieh TC, Lin WY, Ding HJ, Sun SS, Wu YC, Yen KY, et al. Sex- and age-related differences in brain FDG metabolism of healthy adults: an SPM analysis. J Neuroimaging. 2012;22:21–7. https://doi.org/10.1111/j.1552-6569.2010.00543.x.

    Article  PubMed  Google Scholar 

  19. Bonte S, Vandemaele P, Verleden S, Audenaert K, Deblaere K, Goethals I, et al. Healthy brain ageing assessed with 18F-FDG PET and age-dependent recovery factors after partial volume effect correction. Eur J Nucl Med Mol Imaging. 2017;44:838–49. https://doi.org/10.1007/s00259-016-3569-0.

    Article  PubMed  Google Scholar 

  20. Ishibashi K, Onishi A, Fujiwara Y, Oda K, Ishiwata K, Ishii K. Longitudinal effects of aging on (18)F-FDG distribution in cognitively normal elderly individuals. Sci Rep. 2018;8:11557. https://doi.org/10.1038/s41598-018-29937-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Apostolova LG, Thompson PM, Rogers SA, Dinov ID, Zoumalan C, Steiner CA, et al. Surface feature-guided mapping of cerebral metabolic changes in cognitively normal and mildly impaired elderly. Mol Imaging Biol. 2010;12:218–24. https://doi.org/10.1007/s11307-009-0247-7.

    Article  PubMed  Google Scholar 

  22. Ewers M, Brendel M, Rizk-Jackson A, Rominger A, Bartenstein P, Schuff N, et al. Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects. Neuroimage Clin. 2014;4:45–52. https://doi.org/10.1016/j.nicl.2013.10.018.

    Article  PubMed  Google Scholar 

  23. Brugnolo A, Morbelli S, Arnaldi D, De Carli F, Accardo J, Bossert I, et al. Metabolic correlates of Rey auditory verbal learning test in elderly subjects with memory complaints. J Alzheimers Dis. 2014;39:103–13. https://doi.org/10.3233/JAD-121684.

    Article  PubMed  Google Scholar 

  24. Sakurai R, Ishii K, Yasunaga M, Takeuchi R, Murayama Y, Sakuma N, et al. The neural substrate of gait and executive function relationship in elderly women: a PET study. Geriatr Gerontol Int. 2017;17:1873–80. https://doi.org/10.1111/ggi.12982.

    Article  PubMed  Google Scholar 

  25. Chetelat G, Landeau B, Salmon E, Yakushev I, Bahri MA, Mezenge F, et al. Relationships between brain metabolism decrease in normal aging and changes in structural and functional connectivity. Neuroimage. 2013;76:167–77. https://doi.org/10.1016/j.neuroimage.2013.03.009.

    Article  PubMed  Google Scholar 

  26. Cross DJ, Anzai Y, Petrie EC, Martin N, Richards TL, Maravilla KR, et al. Loss of olfactory tract integrity affects cortical metabolism in the brain and olfactory regions in aging and mild cognitive impairment. J Nucl Med. 2013;54:1278–84. https://doi.org/10.2967/jnumed.112.116558.

    Article  CAS  PubMed  Google Scholar 

  27. Kakimoto A, Ito S, Okada H, Nishizawa S, Minoshima S, Ouchi Y. Age-related sex-specific changes in brain metabolism and morphology. J Nucl Med. 2016;57:221–5. https://doi.org/10.2967/jnumed.115.166439.

    Article  CAS  PubMed  Google Scholar 

  28. Curiati PK, Tamashiro-Duran JH, Duran FL, Buchpiguel CA, Squarzoni P, Romano DC, et al. Age-related metabolic profiles in cognitively healthy elders: results from a voxel-based [18F]fluorodeoxyglucose-positron-emission tomography study with partial volume effects correction. AJNR Am J Neuroradiol. 2011;32:560–5. https://doi.org/10.3174/ajnr.A2321.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9:119–28. https://doi.org/10.1016/s1474-4422(09)70299-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tracking Progress of Alzheimer’s Proteins in Aging Brains. Neuroscience News. 2016. https://neurosciencenews.com/pet-scan-alzheimers-3781/. Accessed 2 Mar 2016.

  31. Scholl M, Lockhart SN, Schonhaut DR, O'Neil JP, Janabi M, Ossenkoppele R, et al. PET imaging of tau deposition in the aging human brain. Neuron. 2016;89:971–82. https://doi.org/10.1016/j.neuron.2016.01.028.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Fleisher AS, Chen K, Liu X, Roontiva A, Thiyyagura P, Ayutyanont N, et al. Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Arch Neurol. 2011;68:1404–11. https://doi.org/10.1001/archneurol.2011.150.

    Article  PubMed  Google Scholar 

  33. Price JL, Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Ann Neurol. 1999;45:358–68. https://doi.org/10.1002/1531-8249(199903)45:3<358::aid-ana12>3.0.co;2-x.

    Article  CAS  PubMed  Google Scholar 

  34. Bennett D, Schneider J, Arvanitakis Z, Kelly J, Aggarwal N, Shah R, et al. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology. 2006;66:1837–44. https://doi.org/10.1212/01.wnl.0000219668.47116.e6.

    Article  CAS  PubMed  Google Scholar 

  35. Crystal H, Dickson D, Fuld P, Masur D, Scott R, Mehler M, et al. Clinico-pathologic studies in dementia: nondemented subjects with pathologically confirmed Alzheimer's disease. Neurology. 1988;38:1682. https://doi.org/10.1212/wnl.38.11.1682.

    Article  CAS  PubMed  Google Scholar 

  36. Hampel H. Amyloid-beta and cognition in aging and Alzheimer's disease: molecular and neurophysiological mechanisms. J Alzheimers Dis. 2013;33(Suppl 1):S79–86. https://doi.org/10.3233/JAD-2012-129003.

    Article  CAS  PubMed  Google Scholar 

  37. Bilgel M, An Y, Helphrey J, Elkins W, Gomez G, Wong DF, et al. Effects of amyloid pathology and neurodegeneration on cognitive change in cognitively normal adults. Brain. 2018;141:2475–85. https://doi.org/10.1093/brain/awy150.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Perrotin A, Mormino EC, Madison CM, Hayenga AO, Jagust WJ. Subjective cognition and amyloid deposition imaging: a Pittsburgh compound B positron emission tomography study in normal elderly individuals. Arch Neurol. 2012;69:223–9. https://doi.org/10.1001/archneurol.2011.666.

    Article  PubMed  PubMed Central  Google Scholar 

  39. McCluskey GE, Yates P, Villemagne VL, Rowe C, Szoeke CEI. Self-reported confusion is related to global and regional beta-amyloid: data from the Women's Healthy Ageing Project. Brain Imaging Behav. 2018;12:78–86. https://doi.org/10.1007/s11682-016-9668-5.

    Article  PubMed  Google Scholar 

  40. Jack CR Jr, Wiste HJ, Weigand SD, Knopman DS, Vemuri P, Mielke MM, et al. Age, sex, and APOE epsilon4 effects on memory, brain structure, and beta-amyloid across the adult life span. JAMA Neurol. 2015;72:511–9. https://doi.org/10.1001/jamaneurol.2014.4821.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Mattsson P, Forsberg A, Persson J, Nyberg L, Nilsson LG, Halldin C, et al. Beta-amyloid binding in elderly subjects with declining or stable episodic memory function measured with PET and [11C]AZD2184. Eur J Nucl Med Mol Imaging. 2015;42:1507–11. https://doi.org/10.1007/s00259-015-3103-9.

  42. Herrmann FR, Rodriguez C, Haller S, Garibotto V, Montandon ML, Giannakopoulos P. Gray matter densities in limbic areas and APOE4 independently predict cognitive decline in normal brain aging. Front Aging Neurosci. 2019;11:157. https://doi.org/10.3389/fnagi.2019.00157.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gottesman RF, Schneider AL, Zhou Y, Chen X, Green E, Gupta N, et al. The ARIC-PET amyloid imaging study: brain amyloid differences by age, race, sex, and APOE. Neurology. 2016;87:473–80. https://doi.org/10.1212/WNL.0000000000002914.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Joannette M, Bocti C, Dupont PS, Lavallee MM, Nikelski J, Vallet GT, et al. Education as a moderator of the relationship between episodic memory and amyloid load in normal aging. J Gerontol A Biol Sci Med Sci. 2020;75:1820–6. https://doi.org/10.1093/gerona/glz235.

    Article  CAS  PubMed  Google Scholar 

  45. Gabelle A, Jaussent I, Bouallegue FB, Lehmann S, Lopez R, Barateau L, et al. Reduced brain amyloid burden in elderly patients with narcolepsy type 1. Ann Neurol. 2019;85:74–83. https://doi.org/10.1002/ana.25373.

    Article  CAS  PubMed  Google Scholar 

  46. Sharma RA, Varga AW, Bubu OM, Pirraglia E, Kam K, Parekh A, et al. Obstructive sleep apnea severity affects amyloid burden in cognitively normal elderly. A longitudinal study. Am J Respir Crit Care Med. 2018;197:933–43. https://doi.org/10.1164/rccm.201704-0704OC.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. You JC, Jones E, Cross DE, Lyon AC, Kang H, Newberg AB, et al. Association of β-amyloid burden with sleep dysfunction and cognitive impairment in elderly individuals with cognitive disorders. JAMA Netw Open. 2019;2. https://doi.org/10.1001/jamanetworkopen.2019.13383.

  48. Rainey-Smith SR, Gu Y, Gardener SL, Doecke JD, Villemagne VL, Brown BM, et al. Mediterranean diet adherence and rate of cerebral Abeta-amyloid accumulation: data from the Australian imaging, Biomarkers and Lifestyle Study of Ageing. Transl Psychiatry. 2018;8:238. https://doi.org/10.1038/s41398-018-0293-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hill E, Clifton P, Goodwill AM, Dennerstein L, Campbell S, Szoeke C. Dietary patterns and beta-amyloid deposition in aging Australian women. Alzheimers Dement (N Y). 2018;4:535–41. https://doi.org/10.1016/j.trci.2018.09.007.

    Article  Google Scholar 

  50. Rabin JS, Schultz AP, Hedden T, Viswanathan A, Marshall GA, Kilpatrick E, et al. Interactive associations of vascular risk and beta-amyloid burden with cognitive decline in clinically normal elderly individuals: findings from the Harvard aging brain study. JAMA Neurol. 2018;75:1124–31. https://doi.org/10.1001/jamaneurol.2018.1123.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Krell-Roesch J, Lowe VJ, Neureiter J, Pink A, Roberts RO, Mielke MM, et al. Depressive and anxiety symptoms and cortical amyloid deposition among cognitively normal elderly persons: the Mayo Clinic study of aging. Int Psychogeriatr. 2018;30:245–51. https://doi.org/10.1017/S1041610217002368.

    Article  PubMed  Google Scholar 

  52. Hsu DC, Mormino EC, Schultz AP, Amariglio RE, Donovan NJ, Rentz DM, et al. Lower late-life body-mass index is associated with higher cortical amyloid burden in clinically normal elderly. J Alzheimers Dis. 2016;53:1097–105. https://doi.org/10.3233/JAD-150987.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Vemuri P, Lesnick TG, Knopman DS, Przybelski SA, Reid RI, Mielke MM, et al. Amyloid, vascular, and resilience pathways associated with cognitive aging. Ann Neurol. 2019;86:866–77. https://doi.org/10.1002/ana.25600.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Becker JA, Hedden T, Carmasin J, Maye J, Rentz DM, Putcha D, et al. Amyloid-beta associated cortical thinning in clinically normal elderly. Ann Neurol. 2011;69:1032–42. https://doi.org/10.1002/ana.22333.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Chetelat G, Villemagne VL, Villain N, Jones G, Ellis KA, Ames D, et al. Accelerated cortical atrophy in cognitively normal elderly with high beta-amyloid deposition. Neurology. 2012;78:477–84. https://doi.org/10.1212/WNL.0b013e318246d67a.

    Article  CAS  PubMed  Google Scholar 

  56. Hsu PJ, Shou H, Benzinger T, Marcus D, Durbin T, Morris JC, et al. Amyloid burden in cognitively normal elderly is associated with preferential hippocampal subfield volume loss. J Alzheimers Dis. 2015;45:27–33. https://doi.org/10.3233/JAD-141743.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chetelat G, Villemagne VL, Pike KE, Baron JC, Bourgeat P, Jones G, et al. Larger temporal volume in elderly with high versus low beta-amyloid deposition. Brain. 2010;133:3349–58. https://doi.org/10.1093/brain/awq187.

    Article  PubMed  Google Scholar 

  58. Vipin A, Ng KK, Ji F, Shim HY, Lim JKW, Pasternak O, et al. Amyloid burden accelerates white matter degradation in cognitively normal elderly individuals. Hum Brain Mapp. 2019;40:2065–75. https://doi.org/10.1002/hbm.24507.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Moscoso A, Rey-Bretal D, Silva-Rodriguez J, Aldrey JM, Cortes J, Pias-Peleteiro J, et al. White matter hyperintensities are associated with subthreshold amyloid accumulation. Neuroimage. 2020;218:116944. https://doi.org/10.1016/j.neuroimage.2020.116944.

    Article  CAS  PubMed  Google Scholar 

  60. Mormino EC, Brandel MG, Madison CM, Marks S, Baker SL, Jagust WJ. Abeta deposition in aging is associated with increases in brain activation during successful memory encoding. Cereb Cortex. 2012;22:1813–23. https://doi.org/10.1093/cercor/bhr255.

    Article  PubMed  Google Scholar 

  61. Oh H, Steffener J, Razlighi QR, Habeck C, Liu D, Gazes Y, et al. Abeta-related hyperactivation in frontoparietal control regions in cognitively normal elderly. Neurobiol Aging. 2015;36:3247–54. https://doi.org/10.1016/j.neurobiolaging.2015.08.016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Rieck JR, Rodrigue KM, Kennedy KM, Devous MD Sr, Park DC. The effect of beta-amyloid on face processing in young and old adults: a multivariate analysis of the BOLD signal. Hum Brain Mapp. 2015;36:2514–26. https://doi.org/10.1002/hbm.22788.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Foster CM, Kennedy KM, Horn MM, Hoagey DA, Rodrigue KM. Both hyper- and hypo-activation to cognitive challenge are associated with increased beta-amyloid deposition in healthy aging: a nonlinear effect. Neuroimage. 2018;166:285–92. https://doi.org/10.1016/j.neuroimage.2017.10.068.

    Article  CAS  PubMed  Google Scholar 

  64. Kennedy KM, Foster CM, Rodrigue KM. Increasing beta-amyloid deposition in cognitively healthy aging predicts nonlinear change in BOLD modulation to difficulty. Neuroimage. 2018;183:142–9. https://doi.org/10.1016/j.neuroimage.2018.08.017.

    Article  CAS  PubMed  Google Scholar 

  65. Lim HK, Nebes R, Snitz B, Cohen A, Mathis C, Price J, et al. Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects. Brain. 2014;137:3327–38. https://doi.org/10.1093/brain/awu271.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Mormino EC, Smiljic A, Hayenga AO, Onami SH, Greicius MD, Rabinovici GD, et al. Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cereb Cortex. 2011;21:2399–407. https://doi.org/10.1093/cercor/bhr025.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Kikuchi M, Hirosawa T, Yokokura M, Yagi S, Mori N, Yoshikawa E, et al. Effects of brain amyloid deposition and reduced glucose metabolism on the default mode of brain function in normal aging. J Neurosci. 2011;31:11193–9. https://doi.org/10.1523/JNEUROSCI.2535-11.2011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hahn A, Strandberg TO, Stomrud E, Nilsson M, van Westen D, Palmqvist S, et al. Association between earliest amyloid uptake and functional connectivity in cognitively unimpaired elderly. Cereb Cortex. 2019;29:2173–82. https://doi.org/10.1093/cercor/bhz020.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Steininger SC, Liu X, Gietl A, Wyss M, Schreiner S, Gruber E, et al. Cortical amyloid beta in cognitively normal elderly adults is associated with decreased network efficiency within the cerebro-cerebellar system. Front Aging Neurosci. 2014;6:52. https://doi.org/10.3389/fnagi.2014.00052.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Oh H, Jagust WJ. Frontotemporal network connectivity during memory encoding is increased with aging and disrupted by beta-amyloid. J Neurosci. 2013;33:18425–37. https://doi.org/10.1523/JNEUROSCI.2775-13.2013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Vogel JW, Doležalová MV, La Joie R, Marks SM, Schwimmer HD, Landau SM, et al. Subjective cognitive decline and β-amyloid burden predict cognitive change in healthy elderly. Neurology. 2017;89:2002–9. https://doi.org/10.1212/WNL.0000000000004627.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. van Bergen JMG, Li X, Quevenco FC, Gietl AF, Treyer V, Meyer R, et al. Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and β-amyloid as an indicator of cognitive performance at high age. NeuroImage. 2018;174:308–16. https://doi.org/10.1016/j.neuroimage.2018.03.021.

    Article  CAS  PubMed  Google Scholar 

  73. Knopman DS, Jack CR Jr, Wiste HJ, Weigand SD, Vemuri P, Lowe VJ, et al. Selective worsening of brain injury biomarker abnormalities in cognitively normal elderly persons with beta-amyloidosis. JAMA Neurol. 2013;70:1030–8. https://doi.org/10.1001/jamaneurol.2013.182.

    Article  PubMed  Google Scholar 

  74. Wirth M, Oh H, Mormino EC, Markley C, Landau SM, Jagust WJ. The effect of amyloid beta on cognitive decline is modulated by neural integrity in cognitively normal elderly. Alzheimers Dement. 2013;9:687–98 e1. https://doi.org/10.1016/j.jalz.2012.10.012.

    Article  PubMed  Google Scholar 

  75. Saha P, Sen N. Tauopathy: a common mechanism for neurodegeneration and brain aging. Mech Ageing Dev. 2019;178:72–9. https://doi.org/10.1016/j.mad.2019.01.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Davis D, Schmitt F, Wekstein D, Markesbery W. Alzheimer neuropathologic alterations in aged cognitively normal subjects. J Neuropathol Exp Neurol. 1999;58:376–88. https://doi.org/10.1097/00005072-199904000-00008.

    Article  CAS  PubMed  Google Scholar 

  77. Xia CF, Arteaga J, Chen G, Gangadharmath U, Gomez LF, Kasi D, et al. [(18)F]T807, a novel tau positron emission tomography imaging agent for Alzheimer's disease. Alzheimers Dement. 2013;9:666–76. https://doi.org/10.1016/j.jalz.2012.11.008.

    Article  PubMed  Google Scholar 

  78. Marquie M, Normandin MD, Vanderburg CR, Costantino IM, Bien EA, Rycyna LG, et al. Validating novel tau positron emission tomography tracer [F-18]-AV-1451 (T807) on postmortem brain tissue. Ann Neurol. 2015;78:787–800. https://doi.org/10.1002/ana.24517.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Maass A, Lockhart SN, Harrison TM, Bell RK, Mellinger T, Swinnerton K, et al. Entorhinal tau pathology, episodic memory decline, and neurodegeneration in aging. J Neurosci. 2018;38:530–43. https://doi.org/10.1523/JNEUROSCI.2028-17.2017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Pontecorvo MJ, Devous MD Sr, Navitsky M, Lu M, Salloway S, Schaerf FW, et al. Relationships between flortaucipir PET tau binding and amyloid burden, clinical diagnosis, age and cognition. Brain. 2017;140:748–63. https://doi.org/10.1093/brain/aww334.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Harrison TM, Maass A, Adams JN, Du R, Baker SL, Jagust WJ. Tau deposition is associated with functional isolation of the hippocampus in aging. Nat Commun. 2019;10:4900. https://doi.org/10.1038/s41467-019-12921-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Adams JN, Maass A, Harrison TM, Baker SL, Jagust WJ. Cortical tau deposition follows patterns of entorhinal functional connectivity in aging. Elife. 2019;8. https://doi.org/10.7554/eLife.49132.

  83. Hedden T, Schultz AP, Rieckmann A, Mormino EC, Johnson KA, Sperling RA, et al. Multiple brain markers are linked to age-related variation in cognition. Cereb Cortex. 2016;26:1388–400. https://doi.org/10.1093/cercor/bhu238.

    Article  PubMed  Google Scholar 

  84. Sepulcre J, Sabuncu MR, Li Q, El Fakhri G, Sperling R, Johnson KA. Tau and amyloid beta proteins distinctively associate to functional network changes in the aging brain. Alzheimers Dement. 2017;13:1261–9. https://doi.org/10.1016/j.jalz.2017.02.011.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Sepulcre J, Schultz AP, Sabuncu M, Gomez-Isla T, Chhatwal J, Becker A, et al. In vivo tau, amyloid, and gray matter profiles in the aging brain. J Neurosci. 2016;36:7364–74. https://doi.org/10.1523/JNEUROSCI.0639-16.2016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Lowe VJ, Weigand SD, Senjem ML, Vemuri P, Jordan L, Kantarci K, et al. Association of hypometabolism and amyloid levels in aging, normal subjects. Neurology. 2014;82:1959–67. https://doi.org/10.1212/WNL.0000000000000467.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Jack CR, Wiste HJ, Weigand SD, Rocca WA, Knopman DS, Mielke MM, et al. 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 Neurol. 2014;13:997–1005. https://doi.org/10.1016/s1474-4422(14)70194-2.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Baran TM, Lin FV. Alzheimer's disease neuroimaging I. Amyloid and FDG PET of successful cognitive aging: global and cingulate-specific differences. J Alzheimers Dis. 2018;66:307–18. https://doi.org/10.3233/JAD-180360.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Jack CR Jr, Wiste HJ, Weigand SD, Therneau TM, Knopman DS, Lowe V, et al. Age-specific and sex-specific prevalence of cerebral β-amyloidosis, tauopathy, and neurodegeneration in cognitively unimpaired individuals aged 50–95 years: a cross-sectional study. Lancet Neurol. 2017;16:435–44. https://doi.org/10.1016/S1474-4422(17)30077-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was funded by the grants from the National Key Research and the Development Program of China (No. 2016YFA0100900), the National Natural Science Foundation of China (NSFC) (No. 81725009, 81761148029, 21788102, 32027802, 82030049), and the Fundamental Research Funds for the Central Universities (2020FZZX001-05). In addition, we acknowledged the Japan Society for the Promotion of Science (JSPS) for their support on this work.

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Authors and Affiliations

  1. Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, 650-0047, Japan

    Kai Zhang, Hiroshi Mizuma, Kayo Takahashi, Yousuke Kanayama, Yuping Wu & Yasuyoshi Watanabe

  2. Interntional Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan

    Kai Zhang

  3. Department of Nuclear Medicine and PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China

    Xiaohui Zhang, Chentao Jin, Yuanxue Gao, Yuting Li, Lijuan Ma, Mei Tian & Hong Zhang

  4. Department of Nuclear Medicine, Zhejiang Province People’s Hospital, Hangzhou, Zhejiang, 310014, China

    Fahuan Song

  5. Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, 310007, China

    Hong Zhang

  6. The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, Zhejiang, 310007, China

    Hong Zhang

  7. Kavli Institute for the Physics and Mathematics of the Universe, The University of Tokyo, Chiba, Kashiwa, 277-8583, Japan

    Hiroshi Mizuma & Yousuke Kanayama

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  1. Kai Zhang
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Correspondence to Kai Zhang, Mei Tian, Hong Zhang or Yasuyoshi Watanabe.

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Zhang, K., Mizuma, H., Zhang, X. et al. PET imaging of neural activity, β-amyloid, and tau in normal brain aging. Eur J Nucl Med Mol Imaging 48, 3859–3871 (2021). https://doi.org/10.1007/s00259-021-05230-5

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