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Reduced [18F]flortaucipir retention in white matter hyperintensities compared to normal-appearing white matter

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Abstract

Purpose

Recent research has suggested the use of white matter (WM) reference regions for longitudinal tau-PET imaging. However, tau tracers display affinity for the β-sheet structure formed by myelin, and thus WM lesions might influence tracer retention. Here, we explored whether the tau-sensitive tracer [18F]flortaucipir shows reduced retention in WM hyperintensities (WMH) and how this retention changes over time.

Methods

We included 707 participants from the Alzheimer’s Disease Neuroimaging Initiative with available [18F]flortaucipir-PET and structural and FLAIR MRI scans. WM segments and WMH were automatically delineated in the structural MRI and FLAIR scans, respectively. [18F]flortaucipir standardized uptake value ratios (SUVR) of WMH and normal-appearing WM (NAWM) were calculated using the inferior cerebellar grey matter as reference region, and a 3-mm erosion was applied to the combined NAWM and WMH masks to avoid partial volume effects. Longitudinal [18F]flortaucipir SUVR changes in NAWM and WMH were estimated using linear mixed models. The percent variance of WM-referenced cortical [18F]flortaucipir SUVRs explained by longitudinal changes in the WM reference region was estimated with the R2 coefficient.

Results

Compared to NAWM, WMH areas displayed significantly reduced [18F]flortaucipir SUVR, independent of cognitive impairment or Aβ status (mean difference = 0.14 SUVR, p < 0.001). Older age was associated with lower [18F]flortaucipir SUVR in both NAWM (− 0.002 SUVR/year, p = 0.005) and WMH (− 0.004 SUVR/year, p < 0.001). Longitudinally, [18F]flortaucipir SUVR decreased in NAWM (− 0.008 SUVR/year, p = 0.03) and even more so in WMH (− 0.02 SUVR/year, p < 0.001). Between 17% and 66% of the variance of longitudinal changes in cortical WM-referenced [18F]flortaucipir SUVRs were explained by longitudinal changes in the reference region.

Conclusions

[18F]flortaucipir retention in the WM decreases over time and is influenced by the presence of WMH, supporting the hypothesis that [18F]flortaucipir retention in the WM is partially myelin-dependent. These findings have implications for the use of WM reference regions for [18F]flortaucipir-PET imaging.

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Data availability

All the data used in this study is publicly available at the Laboratory of Neuro Imaging (LONI) server of the Alzheimer’s Disease Neuroimaging Initiative.

References

  1. Fleisher AS, Pontecorvo MJ, Devous MD Sr, Lu M, Arora AK, Truocchio SP, et al. Positron emission tomography imaging with [18F]flortaucipir and postmortem assessment of Alzheimer disease neuropathologic changes. JAMA Neurol. 2020. https://doi.org/10.1001/jamaneurol.2020.0528.

  2. Lowe VJ, Lundt ES, Albertson SM, Min HK, Fang P, Przybelski SA, et al. Tau-positron emission tomography correlates with neuropathology findings. Alzheimers Dement. 2020;16:561–71. https://doi.org/10.1016/j.jalz.2019.09.079.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Smith R, Wibom M, Pawlik D, Englund E, Hansson O. Correlation of in vivo [18F]Flortaucipir with postmortem Alzheimer disease tau pathology. JAMA Neurol. 2019;76:310–7. https://doi.org/10.1001/jamaneurol.2018.3692.

    Article  PubMed  Google Scholar 

  4. Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimers Dement. 2012;8:1–13. https://doi.org/10.1016/j.jalz.2011.10.007.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Nelson PT, Alafuzoff I, Bigio EH, Bouras C, Braak H, Cairns NJ, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71:362–81. https://doi.org/10.1097/NEN.0b013e31825018f7.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hanseeuw BJ, Betensky RA, Jacobs HIL, Schultz AP, Sepulcre J, Becker JA, et al. Association of amyloid and tau with cognition in preclinical Alzheimer disease: a longitudinal study. JAMA Neurol. 2019. https://doi.org/10.1001/jamaneurol.2019.1424.

  7. La Joie R, Visani AV, Baker SL, Brown JA, Bourakova V, Cha J, et al. Prospective longitudinal atrophy in Alzheimer’s disease correlates with the intensity and topography of baseline tau-PET. Sci Transl Med. 2020;12. https://doi.org/10.1126/scitranslmed.aau5732.

  8. Aschenbrenner AJ, Gordon BA, Benzinger TLS, Morris JC, Hassenstab JJ. Influence of tau PET, amyloid PET, and hippocampal volume on cognition in Alzheimer disease. Neurology. 2018;91:e859–e66. https://doi.org/10.1212/WNL.0000000000006075.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 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 

  10. Brier MR, Gordon B, Friedrichsen K, McCarthy J, Stern A, Christensen J, et al. Tau and abeta imaging, CSF measures, and cognition in Alzheimer’s disease. Sci Transl Med. 2016;8:338ra66. https://doi.org/10.1126/scitranslmed.aaf2362.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ossenkoppele R, Smith R, Ohlsson T, Strandberg O, Mattsson N, Insel PS, et al. Associations between tau, Abeta, and cortical thickness with cognition in Alzheimer disease. Neurology. 2019;92:e601–e12. https://doi.org/10.1212/WNL.0000000000006875.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lowe VJ, Bruinsma TJ, Wiste HJ, Min HK, Weigand SD, Fang P, et al. Cross-sectional associations of tau-PET signal with cognition in cognitively unimpaired adults. Neurology. 2019;93:e29–39. https://doi.org/10.1212/WNL.0000000000007728.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gauthier S, Albert M, Fox N, Goedert M, Kivipelto M, Mestre-Ferrandiz J, et al. Why has therapy development for dementia failed in the last two decades? Alzheimers Dement. 2016;12:60–4. https://doi.org/10.1016/j.jalz.2015.12.003.

    Article  PubMed  Google Scholar 

  14. Congdon EE, Sigurdsson EM. Tau-targeting therapies for Alzheimer disease. Nat Rev Neurol. 2018;14:399–415. https://doi.org/10.1038/s41582-018-0013-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Jack CR Jr, Wiste HJ, Schwarz CG, Lowe VJ, Senjem ML, Vemuri P, et al. Longitudinal tau PET in ageing and Alzheimer’s disease. Brain. 2018;141:1517–28. https://doi.org/10.1093/brain/awy059.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Pontecorvo MJ, Devous MD, Kennedy I, Navitsky M, Lu M, Galante N, et al. A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer’s disease dementia. Brain. 2019;142:1723–35. https://doi.org/10.1093/brain/awz090.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Baek MS, Cho H, Lee HS, Choi JY, Lee JH, Ryu YH, et al. Temporal trajectories of in vivo tau and amyloid-beta accumulation in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2020. https://doi.org/10.1007/s00259-020-04773-3.

  18. Hansson O, Mormino EC. Is longitudinal tau PET ready for use in Alzheimer’s disease clinical trials? Brain. 2018;141:1241–4. https://doi.org/10.1093/brain/awy065.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Harrison TM, La Joie R, Maass A, Baker SL, Swinnerton K, Fenton L, et al. Longitudinal tau accumulation and atrophy in aging and Alzheimer disease. Ann Neurol. 2019;85:229–40. https://doi.org/10.1002/ana.25406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Southekal S, Devous MD Sr, Kennedy I, Navitsky M, Lu M, Joshi AD, et al. Flortaucipir F 18 quantitation using parametric estimation of reference signal intensity. J Nucl Med. 2018;59:944–51. https://doi.org/10.2967/jnumed.117.200006.

    Article  CAS  PubMed  Google Scholar 

  21. Stankoff B, Freeman L, Aigrot MS, Chardain A, Dolle F, Williams A, et al. Imaging central nervous system myelin by positron emission tomography in multiple sclerosis using [methyl-(1)(1)C]-2-(4'-methylaminophenyl)- 6-hydroxybenzothiazole. Ann Neurol. 2011;69:673–80. https://doi.org/10.1002/ana.22320.

    Article  CAS  PubMed  Google Scholar 

  22. Faria Dde P, Copray S, Sijbesma JW, Willemsen AT, Buchpiguel CA, Dierckx RA, et al. PET imaging of focal demyelination and remyelination in a rat model of multiple sclerosis: comparison of [11C]MeDAS, [11C]CIC and [11C]PIB. Eur J Nucl Med Mol Imaging. 2014;41:995–1003. https://doi.org/10.1007/s00259-013-2682-6.

    Article  PubMed  Google Scholar 

  23. Leuzy A, Chiotis K, Lemoine L, Gillberg PG, Almkvist O, Rodriguez-Vieitez E, et al. Tau PET imaging in neurodegenerative tauopathies-still a challenge. Mol Psychiatry. 2019;24:1112–34. https://doi.org/10.1038/s41380-018-0342-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pietroboni AM, Carandini T, Colombi A, Mercurio M, Ghezzi L, Giulietti G, et al. Amyloid PET as a marker of normal-appearing white matter early damage in multiple sclerosis: correlation with CSF beta-amyloid levels and brain volumes. Eur J Nucl Med Mol Imaging. 2019;46:280–7. https://doi.org/10.1007/s00259-018-4182-1.

    Article  CAS  PubMed  Google Scholar 

  25. Bodini B, Veronese M, Garcia-Lorenzo D, Battaglini M, Poirion E, Chardain A, et al. Dynamic imaging of individual remyelination profiles in multiple sclerosis. Ann Neurol. 2016;79:726–38. https://doi.org/10.1002/ana.24620.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Matias-Guiu JA, Cabrera-Martin MN, Matias-Guiu J, Oreja-Guevara C, Riola-Parada C, Moreno-Ramos T, et al. Amyloid PET imaging in multiple sclerosis: an (18)F-florbetaben study. BMC Neurol. 2015;15:243. https://doi.org/10.1186/s12883-015-0502-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zeydan B, Lowe VJ, Schwarz CG, Przybelski SA, Tosakulwong N, Zuk SM, et al. Pittsburgh compound-B PET white matter imaging and cognitive function in late multiple sclerosis. Mult Scler. 2018;24:739–49. https://doi.org/10.1177/1352458517707346.

    Article  CAS  PubMed  Google Scholar 

  28. Glodzik L, Rusinek H, Li J, Zhou C, Tsui W, Mosconi L, et al. Reduced retention of Pittsburgh compound B in white matter lesions. Eur J Nucl Med Mol Imaging. 2015;42:97–102. https://doi.org/10.1007/s00259-014-2897-1.

    Article  PubMed  Google Scholar 

  29. Zeydan B, Schwarz CG, Lowe VJ, Reid RI, Przybelski SA, Lesnick TG, et al. Investigation of white matter PiB uptake as a marker of white matter integrity. Ann Clin Transl Neurol. 2019;6:678–88. https://doi.org/10.1002/acn3.741.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Goodheart AE, Tamburo E, Minhas D, Aizenstein HJ, McDade E, Snitz BE, et al. Reduced binding of Pittsburgh Compound-B in areas of white matter hyperintensities. Neuroimage Clin. 2015;9:479–83. https://doi.org/10.1016/j.nicl.2015.09.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Simpson JE, Fernando MS, Clark L, Ince PG, Matthews F, Forster G, et al. White matter lesions in an unselected cohort of the elderly: astrocytic, microglial and oligodendrocyte precursor cell responses. Neuropathol Appl Neurobiol. 2007;33:410–9. https://doi.org/10.1111/j.1365-2990.2007.00828.x.

    Article  CAS  PubMed  Google Scholar 

  32. Tang Y, Nyengaard JR, Pakkenberg B, Gundersen HJ. Age-induced white matter changes in the human brain: a stereological investigation. Neurobiol Aging. 1997;18:609–15. https://doi.org/10.1016/s0197-4580(97)00155-3.

    Article  CAS  PubMed  Google Scholar 

  33. Klosinski LP, Yao J, Yin F, Fonteh AN, Harrington MG, Christensen TA, et al. White matter lipids as a ketogenic fuel supply in aging female brain: implications for Alzheimer’s disease. EBioMedicine. 2015;2:1888–904. https://doi.org/10.1016/j.ebiom.2015.11.002.

    Article  PubMed  PubMed Central  Google Scholar 

  34. He Q, Luo Y, Lv F, Xiao Q, Chao F, Qiu X, et al. Effects of estrogen replacement therapy on the myelin sheath ultrastructure of myelinated fibers in the white matter of middle-aged ovariectomized rats. J Comp Neurol. 2018;526:790–802. https://doi.org/10.1002/cne.24366.

    Article  CAS  PubMed  Google Scholar 

  35. Landau SM, Breault C, Joshi AD, Pontecorvo M, Mathis CA, Jagust WJ, et al. Amyloid-beta imaging with Pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods. J Nucl Med. 2013;54:70–7. https://doi.org/10.2967/jnumed.112.109009.

    Article  CAS  PubMed  Google Scholar 

  36. Jagust WJ, Landau SM, Koeppe RA, Reiman EM, Chen K, Mathis CA, et al. The Alzheimer’s Disease Neuroimaging Initiative 2 PET Core: 2015. Alzheimers Dement. 2015;11:757–71. https://doi.org/10.1016/j.jalz.2015.05.001.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Jack CR Jr, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, et al. Magnetic resonance imaging in Alzheimer’s Disease Neuroimaging Initiative 2. Alzheimers Dement. 2015;11:740–56. https://doi.org/10.1016/j.jalz.2015.05.002.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Schmidt P, Gaser C, Arsic M, Buck D, Forschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage. 2012;59:3774–83. https://doi.org/10.1016/j.neuroimage.2011.11.032.

    Article  PubMed  Google Scholar 

  39. 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 

  40. Sudre CH, Cardoso MJ, Ourselin S. Alzheimer's Disease Neuroimaging I. Longitudinal segmentation of age-related white matter hyperintensities. Med Image Anal. 2017;38:50–64. https://doi.org/10.1016/j.media.2017.02.007.

    Article  PubMed  Google Scholar 

  41. Maass A, Landau S, Baker SL, Horng A, Lockhart SN, La Joie R, et al. Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer’s disease. Neuroimage. 2017;157:448–63. https://doi.org/10.1016/j.neuroimage.2017.05.058.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Baker SL, Maass A, Jagust WJ. Considerations and code for partial volume correcting [(18)F]-AV-1451 tau PET data. Data Brief. 2017;15:648–57. https://doi.org/10.1016/j.dib.2017.10.024.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Meltzer CC, Leal JP, Mayberg HS, Wagner HN Jr, Frost JJ. Correction of PET data for partial volume effects in human cerebral cortex by MR imaging. J Comput Assist Tomogr. 1990;14:561–70. https://doi.org/10.1097/00004728-199007000-00011.

    Article  CAS  PubMed  Google Scholar 

  44. Jack CR Jr, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS, et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimers Dement. 2017;13:205–16. https://doi.org/10.1016/j.jalz.2016.08.005.

    Article  PubMed  Google Scholar 

  45. Landau SM, Fero A, Baker SL, Koeppe R, Mintun M, Chen K, et al. Measurement of longitudinal beta-amyloid change with 18F-florbetapir PET and standardized uptake value ratios. J Nucl Med. 2015;56:567–74. https://doi.org/10.2967/jnumed.114.148981.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Graff-Radford J, Arenaza-Urquijo EM, Knopman DS, Schwarz CG, Brown RD, Rabinstein AA, et al. White matter hyperintensities: relationship to amyloid and tau burden. Brain. 2019;142:2483–91. https://doi.org/10.1093/brain/awz162.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Pytel V, Matias-Guiu JA, Matias-Guiu J, Cortes-Martinez A, Montero P, Moreno-Ramos T, et al. Amyloid PET findings in multiple sclerosis are associated with cognitive decline at 18 months. Mult Scler Relat Disord. 2020;39:101926. https://doi.org/10.1016/j.msard.2020.101926.

    Article  PubMed  Google Scholar 

  48. Murray ME, Vemuri P, Preboske GM, Murphy MC, Schweitzer KJ, Parisi JE, et al. A quantitative postmortem MRI design sensitive to white matter hyperintensity differences and their relationship with underlying pathology. J Neuropathol Exp Neurol. 2012;71:1113–22. https://doi.org/10.1097/NEN.0b013e318277387e.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Hasan KM, Kamali A, Abid H, Kramer LA, Fletcher JM, Ewing-Cobbs L. Quantification of the spatiotemporal microstructural organization of the human brain association, projection and commissural pathways across the lifespan using diffusion tensor tractography. Brain Struct Funct. 2010;214:361–73. https://doi.org/10.1007/s00429-009-0238-0.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Westlye LT, Walhovd KB, Dale AM, Bjornerud A, Due-Tonnessen P, Engvig A, et al. Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cereb Cortex. 2010;20:2055–68. https://doi.org/10.1093/cercor/bhp280.

    Article  PubMed  Google Scholar 

  51. Baker SL, Harrison TM, Maass A, La Joie R, Jagust WJ. Effect of off-target binding on (18)F-Flortaucipir variability in healthy controls across the life span. J Nucl Med. 2019;60:1444–51. https://doi.org/10.2967/jnumed.118.224113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kantarci K, Tosakulwong N, Lesnick TG, Zuk SM, Lowe VJ, Fields JA, et al. Brain structure and cognition 3 years after the end of an early menopausal hormone therapy trial. Neurology. 2018;90:e1404–e12. https://doi.org/10.1212/WNL.0000000000005325.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Boyle CP, Raji CA, Erickson KI, Lopez OL, Becker JT, Gach HM, et al. Estrogen, brain structure, and cognition in postmenopausal women. Hum Brain Mapp. 2020. https://doi.org/10.1002/hbm.25200.

  54. Lopez-Gonzalez FJ, Moscoso A, Efthimiou N, Fernandez-Ferreiro A, Pineiro-Fiel M, Archibald SJ, et al. Spill-in counts in the quantification of (18)F-florbetapir on Abeta-negative subjects: the effect of including white matter in the reference region. EJNMMI Phys. 2019;6:27. https://doi.org/10.1186/s40658-019-0258-7.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Habes M, Erus G, Toledo JB, Zhang T, Bryan N, Launer LJ, et al. White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain. 2016;139:1164–79. https://doi.org/10.1093/brain/aww008.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Gouw AA, Seewann A, Vrenken H, van der Flier WM, Rozemuller JM, Barkhof F, et al. Heterogeneity of white matter hyperintensities in Alzheimer’s disease: post-mortem quantitative MRI and neuropathology. Brain. 2008;131:3286–98. https://doi.org/10.1093/brain/awn265.

    Article  CAS  PubMed  Google Scholar 

  57. Gootjes L, Teipel SJ, Zebuhr Y, Schwarz R, Leinsinger G, Scheltens P, et al. Regional distribution of white matter hyperintensities in vascular dementia, Alzheimer’s disease and healthy aging. Dement Geriatr Cogn Disord. 2004;18:180–8. https://doi.org/10.1159/000079199.

    Article  CAS  PubMed  Google Scholar 

  58. Carmichael O, Schwarz C, Drucker D, Fletcher E, Harvey D, Beckett L, et al. Longitudinal changes in white matter disease and cognition in the first year of the Alzheimer disease neuroimaging initiative. Arch Neurol. 2010;67:1370–8. https://doi.org/10.1001/archneurol.2010.284.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Caballero MAA, Song Z, Rubinski A, Duering M, Dichgans M, Park DC, et al. Age-dependent amyloid deposition is associated with white matter alterations in cognitively normal adults during the adult life span. Alzheimers Dement. 2020;16:651–61. https://doi.org/10.1002/alz.12062.

    Article  PubMed  Google Scholar 

  60. Jack CR, Wiste HJ, Botha H, Weigand SD, Therneau TM, Knopman DS, et al. The bivariate distribution of amyloid-beta and tau: relationship with established neurocognitive clinical syndromes. Brain. 2019;142:3230–42. https://doi.org/10.1093/brain/awz268.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Choi JY, Cho H, Ahn SJ, Lee JH, Ryu YH, Lee MS, et al. Off-target (18)F-AV-1451 binding in the basal ganglia correlates with age-related iron accumulation. J Nucl Med. 2018;59:117–20. https://doi.org/10.2967/jnumed.117.195248.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Funding

MJG is supported by the “Miguel Servet” program (CP19/00031) of the Spanish Instituto de Salud Carlos III (ISCIII-FEDER). MS is supported by the Knut and Alice Wallenberg Foundation (Wallenberg Centre for Molecular and Translational Medicine; KAW 2014.0363), the Swedish Research Council (#2017-02869), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-813971), and the Swedish Alzheimer Foundation (#AF-740191).

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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or the writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Moscoso, A., Grothe, M.J., Schöll, M. et al. Reduced [18F]flortaucipir retention in white matter hyperintensities compared to normal-appearing white matter. Eur J Nucl Med Mol Imaging 48, 2283–2294 (2021). https://doi.org/10.1007/s00259-021-05195-5

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