Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

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 via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

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

Abbreviations

Aβ:

Amyloid-β

SVCI:

Subcortical vascular cognitive impairment

HV:

Hippocampal volume

AD:

Alzheimer’s disease

NIA-AA:

National Institute on Aging and Alzheimer’s Association

PET:

Positron emission tomography

CSF:

Cerebrospinal fluid

CSVD:

Cerebral small vessel disease

MCI:

Mild cognitive impairment

ADCI:

Alzheimer’s disease–related cognitive impairment

WMH:

White matter hyperintensities

MRI:

Magnetic resonance imaging

NC:

Normal control

ADD:

Alzheimer’s disease dementia

SUVR:

Standardized uptake value ratios

ROI:

Region of interest

PVE:

Partial volume effect

FLAIR:

Fluid-attenuated inversion recovery

GRE:

Gradient echo

CMBs:

Cerebral microbleeds

BAPL:

Brain Aβ plaque load

SNSB:

Seoul Neuropsychological Screening Battery

SVLT:

Seoul Verbal Learning Test

RCFT:

Rey–Osterrieth Complex Figure Test

KBNT:

Korean version of the Boston Naming Test

MMSE:

Mini-Mental State Examination

CDR-SB:

Clinical Dementia Rating sum of boxes

ICV:

Intracranial volume

ANOVA:

Analysis of variance

ANCOVA:

Analysis of covariance

References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Funding

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

Authors and Affiliations

  1. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Gangnam-gu, Seoul, 135-710, South Korea

    Hyemin Jang, Hee Jin Kim, Seongbeom Park, Yu Hyun Park, Yeongsim Choe, Seung Joo Kim, Jun Pyo Kim, Young Hee Jung, Duk L. Na & Sang Won Seo

  2. Neuroscience Center, Samsung Medical Center, Seoul, South Korea

    Hyemin Jang, Hee Jin Kim, Seongbeom Park, Yu Hyun Park, Yeongsim Choe, Seung Joo Kim, Jun Pyo Kim, Young Hee Jung, Duk L. Na & Sang Won Seo

  3. Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA

    Michael W. Weiner

  4. Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

    Hanna Cho & Chul Hyoung Lyoo

  5. Department of Biomedical Engineering, College of Health and Medical Science, Catholic University of Daegu, Gyeongsan, South Korea

    Uicheul Yoon

  6. Department of Neurology, Kyung Hee University Hospital, Seoul, South Korea

    Jin San Lee

  7. Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea

    Yeshin Kim

  8. Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

    Young Hoon Ryu

  9. Division of RI-Convergence Research, Korea Institute Radiological and Medical Sciences, Seoul, South Korea

    Jae Yong Choi

  10. Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

    Seung Hwan Moon

  11. School of Biomedical Engineering, Korea University, Seoul, South Korea

    Joon-Kyung Seong

  12. Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, USA

    Charles DeCarli

  13. Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Samuel N. Lockhart

  14. Department of Neurology, Chonnam National University Hospital, Gwangju, South Korea

    Soo Hyun Cho

  15. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea

    Duk L. Na

  16. Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea

    Duk L. Na & Sang Won Seo

  17. Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea

    Sang Won Seo

  18. Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea

    Sang Won Seo

Authors
  1. Hyemin Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hee Jin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seongbeom Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Hyun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yeongsim Choe
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hanna Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chul Hyoung Lyoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Uicheul Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jin San Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yeshin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Seung Joo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jun Pyo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Young Hee Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Young Hoon Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jae Yong Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Seung Hwan Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Joon-Kyung Seong
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Charles DeCarli
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Michael W. Weiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Samuel N. Lockhart
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Soo Hyun Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Duk L. Na
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Sang Won Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

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

ESM 1

(DOCX 34 kb).

ESM 2

(DOCX 35 kb).

Rights and permissions

Reprints and permissions

About this article

Check for updates. 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). https://doi.org/10.1007/s00259-019-04498-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-019-04498-y

Keywords

Search

Navigation