Application of an amyloid and tau classification system in subcortical vascular cognitive impairment patients



To apply an AT (Aβ/tau) classification system to subcortical vascular cognitive impairment (SVCI) patients following recently developed biomarker-based criteria of Alzheimer’s disease (AD), and to investigate its clinical significance.


We recruited 60 SVCI patients who underwent the neuropsychological tests, brain MRI, and 18F-florbetaben and 18F-AV1451 PET at baseline. As a control group, we further recruited 27 patients with AD cognitive impairment (ADCI; eight Aβ PET-positive AD dementia and 19 amnestic mild cognitive impairment). ADCI and SVCI patients were classified as having normal or abnormal Aβ (A−/A+) and tau (T−/T+) based on PET results. Across the three SVCI groups (A−, A+T−, and A+T+SVCI), we compared longitudinal changes in cognition, hippocampal volume (HV), and cortical thickness using linear mixed models.


Among SVCI patients, 33 (55%), 20 (33.3%), and seven (11.7%) patients were A−, A+T−, and A+T+, respectively. The frequency of T+ was lower in A+SVCI (7/27, 25.9%) than in A+ADCI (14/20, 70.0%, p = 0.003) which suggested that cerebral small vessel disease affected cognitive impairments independently of A+. A+T−SVCI had steeper cognitive decline than A−SVCI. A+T+SVCI also showed steeper cognitive decline than A+T−SVCI. Also, A+T−SVCI had steeper decrease in HV than A−SVCI, while cortical thinning did not differ between the two groups. A+T+SVCI had greater global cortical thinning compared with A+T−SVCI, while declines in HV did not differ between the two groups.


This study showed that the AT system successfully characterized SVCI patients, suggesting that the AT system may be usefully applied in a research framework for clinically diagnosed SVCI.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

Data availability

Anonymous data are available to qualified investigators upon request to the corresponding author.





Subcortical vascular cognitive impairment


Hippocampal volume


Alzheimer’s disease


National Institute on Aging and Alzheimer’s Association


Positron emission tomography


Cerebrospinal fluid


Cerebral small vessel disease


Mild cognitive impairment


Alzheimer’s disease–related cognitive impairment


White matter hyperintensities


Magnetic resonance imaging


Normal control


Alzheimer’s disease dementia


Standardized uptake value ratios


Region of interest


Partial volume effect


Fluid-attenuated inversion recovery


Gradient echo


Cerebral microbleeds


Brain Aβ plaque load


Seoul Neuropsychological Screening Battery


Seoul Verbal Learning Test


Rey–Osterrieth Complex Figure Test


Korean version of the Boston Naming Test


Mini-Mental State Examination


Clinical Dementia Rating sum of boxes


Intracranial volume


Analysis of variance


Analysis of covariance


  1. 1.

    Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, 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. 2011;7:270–9.

    Google Scholar 

  2. 2.

    McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack Jr CR, Kawas CH, 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. Alzheimer Dement. 2011;7:263–269.

    Google Scholar 

  3. 3.

    Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, 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. Alzheimer Dement. 2011;7:280–92.

    Google Scholar 

  4. 4.

    Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62.

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87:539–47.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Park JH, Seo SW, Kim C, Kim GH, Noh HJ, Kim ST, et al. Pathogenesis of cerebral microbleeds: in vivo imaging of amyloid and subcortical ischemic small vessel disease in 226 individuals with cognitive impairment. Ann Neurol. 2013;73:584–93.

    CAS  PubMed  Google Scholar 

  7. 7.

    Kalaria RN, Erkinjuntti T. Small vessel disease and subcortical vascular dementia. J Clin Neurol. 2006;2:1–11.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Hofman A, Ott A, Breteler MM, Bots ML, Slooter AJ, van Harskamp F, et al. Atherosclerosis, apolipoprotein E, and prevalence of dementia and Alzheimer’s disease in the Rotterdam Study. Lancet. 1997;349:151–4.

    CAS  PubMed  Google Scholar 

  9. 9.

    Roher AE, Tyas SL, Maarouf CL, Daugs ID, Kokjohn TA, Emmerling MR, et al. Intracranial atherosclerosis as a contributing factor to Alzheimer’s disease dementia. Alzheimers Dement. 2011;7:436–44.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Shah NS, Vidal JS, Masaki K, Petrovitch H, Ross GW, Tilley C, et al. Midlife blood pressure, plasma beta-amyloid, and the risk for Alzheimer disease: the Honolulu Asia Aging Study. Hypertension. 2012;59:780–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Schmidt R, Ropele S, Enzinger C, Petrovic K, Smith S, Schmidt H, et al. White matter lesion progression, brain atrophy, and cognitive decline: the Austrian stroke prevention study. Ann Neurol. 2005;58:610–6.

    PubMed  Google Scholar 

  12. 12.

    Lee JH, Kim SH, Kim GH, Seo SW, Park HK, Oh SJ, et al. Identification of pure subcortical vascular dementia using 11C-Pittsburgh compound B. Neurology. 2011;77:18–25.

    CAS  PubMed  Google Scholar 

  13. 13.

    Lee MJ, Seo SW, Na DL, Kim C, Park JH, Kim GH, et al. Synergistic effects of ischemia and beta-amyloid burden on cognitive decline in patients with subcortical vascular mild cognitive impairment. JAMA Psychiatry. 2014;71:412–22.

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Ye BS, Seo SW, Kim GH, Noh Y, Cho H, Yoon CW, et al. Amyloid burden, cerebrovascular disease, brain atrophy, and cognition in cognitively impaired patients. Alzheimers Dement. 2015;11:494–503.e3.

    PubMed  Google Scholar 

  15. 15.

    Kim JP, Seo SW, Shin HY, Ye BS, Yang JJ, Kim C, et al. Effects of education on aging-related cortical thinning among cognitively normal individuals. Neurology. 2015;85:806–12.

    PubMed  Google Scholar 

  16. 16.

    Kim HJ, Yang JJ, Kwon H, Kim C, Lee JM, Chun P, et al. Relative impact of amyloid-beta, lacunes, and downstream imaging markers on cognitive trajectories. Brain. 2016;139:2516–27.

    PubMed  Google Scholar 

  17. 17.

    Kim HJ, Park S, Cho H, Jang YK, San Lee J, Jang H, et al. Assessment of extent and role of tau in subcortical vascular cognitive impairment using 18F-AV1451 positron emission tomography imaging. JAMA Neurol 2018;75:999–1007.

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology. 1993;43:1683–9.

    CAS  PubMed  Google Scholar 

  19. 19.

    Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-beta PET with florbetaben (F-18) in patients with Alzheimer’s disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol. 2011;10:424–35.

    CAS  PubMed  Google Scholar 

  20. 20.

    Kim HJ, Park JY, Seo SW, Jung YH, Kim Y, Jang H, et al. Cortical atrophy pattern-based subtyping predicts prognosis of amnestic MCI: an individual-level analysis. Neurobiol Aging. 2019;74:38–45.

    PubMed  Google Scholar 

  21. 21.

    Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12:822–38.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Greenberg SM, Vernooij MW, Cordonnier C, Viswanathan A, Al-Shahi Salman R, Warach S, et al. Cerebral microbleeds: a guide to detection and interpretation. Lancet Neurol. 2009;8:165–74.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Jeon S, Yoon U, Park J-S, Seo SW, Kim J-H, Kim ST, et al. Fully automated pipeline for quantification and localization of white matter hyperintensity in brain magnetic resonance image. Int J Imaging Syst Technol. 2011;21:193–200.

    Google Scholar 

  24. 24.

    Schöll 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.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Van Essen DC. A population-average, landmark-and surface-based (PALS) atlas of human cerebral cortex. Neuroimage. 2005;28:635–62.

    PubMed  Google Scholar 

  26. 26.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Kang Y, Na DL. Seoul Neuropsychological Screening Battery (SNSB). Human Brain Research & Consulting Co., Seoul; 2003.

  28. 28.

    Ye BS, Seo SW, Cho H, Kim SY, Lee JS, Kim EJ, et al. Effects of education on the progression of early- versus late-stage mild cognitive impairment. Int Psychogeriatr. 2013;25:597–606.

    PubMed  Google Scholar 

  29. 29.

    Seo SW, Im K, Lee JM, Kim YH, Kim ST, Kim SY, et al. Cortical thickness in single- versus multiple-domain amnestic mild cognitive impairment. Neuroimage. 2007;36:289–97.

    PubMed  Google Scholar 

  30. 30.

    Zijdenbos AP, Forghani R, Evans AC. Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging. 2002;21:1280–91.

    PubMed  Google Scholar 

  31. 31.

    Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18:192–205.

    CAS  PubMed  Google Scholar 

  32. 32.

    Kwak K, Yoon U, Lee D-K, Kim GH, Seo SW, Na DL, et al. Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening. Magn Reson Imaging. 2013;31:1190–6.

    PubMed  Google Scholar 

  33. 33.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Park JH, Seo SW, Kim C, Kim SH, Kim GH, Kim ST, et al. Effects of cerebrovascular disease and amyloid beta burden on cognition in subjects with subcortical vascular cognitive impairment. Neurobiol Aging. 2014;35:254–60.

    CAS  PubMed  Google Scholar 

  35. 35.

    Johnson KA, Schultz A, Betensky RA, Becker JA, Sepulcre J, Rentz D, et al. Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann Neurol. 2016;79:110–9.

    PubMed  Google Scholar 

  36. 36.

    Hesse C, Rosengren L, Vanmechelen E, Vanderstichele H, Jensen C, Davidsson P, et al. Cerebrospinal fluid markers for Alzheimer’s disease evaluated after acute ischemic stroke. J Alzheimers Dis. 2000;2:199–206.

    CAS  PubMed  Google Scholar 

  37. 37.

    Thal DR, Attems J, Ewers M. Spreading of amyloid, tau, and microvascular pathology in Alzheimer’s disease: findings from neuropathological and neuroimaging studies. J Alzheimers Dis. 2014;42:S421–S9.

    PubMed  Google Scholar 

  38. 38.

    Hartz AM, Bauer B, Soldner EL, Wolf A, Boy S, Backhaus R, et al. Amyloid-beta contributes to blood-brain barrier leakage in transgenic human amyloid precursor protein mice and in humans with cerebral amyloid angiopathy. Stroke. 2012;43:514–23.

    CAS  PubMed  Google Scholar 

  39. 39.

    Montine TJ, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Dickson DW, et al. National Institute on Aging–Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach. Acta Neuropathol. 2012;123:1–11.

    CAS  PubMed  Google Scholar 

  40. 40.

    Kim HJ, Cho H, Werring DJ, Jang YK, Kim YJ, Lee JS, et al. 18F-AV-1451 PET imaging in three patients with probable cerebral amyloid angiopathy. J Alzheimers Dis. 2017;57:711–6.

    CAS  PubMed  Google Scholar 

  41. 41.

    Vos SJ, Xiong C, Visser PJ, Jasielec MS, Hassenstab J, Grant EA, et al. Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study. Lancet Neurol. 2013;12:957–65.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Burnham SC, Bourgeat P, Dore V, Savage G, Brown B, Laws S, et al. Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer’s disease pathophysiology (SNAP) or Alzheimer’s disease pathology: a longitudinal study. Lancet Neurol. 2016;15:1044–53.

    PubMed  Google Scholar 

  43. 43.

    van Rossum IA, Vos SJ, Burns L, Knol DL, Scheltens P, Soininen H, et al. Injury markers predict time to dementia in subjects with MCI and amyloid pathology. Neurology. 2012;79:1809–16.

    PubMed  PubMed Central  Google Scholar 

Download references


This research was supported by funds (2018-ER6202-01) from Research of Korea Centers for Disease Control and Prevention; the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2016M3C7A1913844); NRF grant funded by the Korea government (2017R1A2B2005081)

Author information



Corresponding author

Correspondence to Sang Won Seo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Role of the funder

The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Neurology

Electronic supplementary material


(DOCX 34 kb).


(DOCX 35 kb).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jang, H., Kim, H.J., Park, S. et al. Application of an amyloid and tau classification system in subcortical vascular cognitive impairment patients. Eur J Nucl Med Mol Imaging 47, 292–303 (2020).

Download citation


  • Amyloid-β
  • Tau
  • Classification
  • Subcortical vascular cognitive impairment
  • Longitudinal changes