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Evaluation of amyloid status in a cohort of elderly individuals with memory complaints: validation of the method of quantification and determination of positivity thresholds

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Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Objective

Our aim is to validate the process steps implemented by the French CATI platform to assess amyloid status, obtained from 18F-Florbetapir PET scans, in a cohort of 318 cognitively normal subjects participating in the INSIGHT-preAD study. Our objective was to develop a method with partial volume effect correction (PVEC) on untransformed PET images, using an automated pipeline (“RACHEL”) adapted to large series of patients and including quality checks of results.

Methods

We compared RACHEL using different options (with and without PVEC, different sets of regions of interest), to two other methods validated in the literature, referred as the “AVID” and “CAEN” methods. A standard uptake value ratio (SUVR) was obtained with the different methods for participants to another French study, IMAP, including 26 normal elderly controls (NEC), 11 patients with mild cognitive impairment (MCI) and 16 patients with Alzheimer’s disease (AD). We determined two cutoffs for RACHEL method by linear correlation with the other methods and applied them to the INSIGHT-preAD subjects.

Results

RACHEL including PVEC and a combination of the whole cerebellum and the pons as a reference region allowed the best discrimination between NEC and AD participants. A strong linear correlation was found between RACHEL and the other two methods and yielded the two cutoffs of 0.79 and 0.88. According to the more conservative threshold, 19.8% of the INSIGHT-preAD subjects would be considered amyloid positive, and 27.7% according to the more liberal threshold.

Conclusions

With our method, we clearly discriminated between NEC with negative amyloid status and patients with clinical AD. Using a linear correlation with other validated cutoffs, we could infer our own positivity thresholds and apply them to an independent population. This method might be useful to the community, especially when the optimal cutoff could not be obtained from a population of healthy young adults or from correlation with post-mortem results.

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References

  1. Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292–323.

    Article  PubMed  Google Scholar 

  3. Operto G, Chupin M, Batrancourt B, Habert MO, Colliot O, Benali H, et al. CATI: A large distributed infrastructure for the neuroimaging of cohorts. Neuroinformatics. 2016;14(3):253–64.

    Article  PubMed  Google Scholar 

  4. Wong DF, Rosenberg PB, Zhou Y, Kumar A, Raymont V, Ravert HT, et al. In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (Florbetapir [corrected] F 18). J Nucl Med. 2010;51(6):913–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Besson FL, La Joie R, Doeuvre L, Gaubert M, Mézenge F, Egret S, et al. Cognitive and brain profiles associated with current neuroimaging biomarkers of preclinical Alzheimer’s disease. J Neurosci. 2015;35(29):10402–11.

    Article  CAS  PubMed  Google Scholar 

  6. La Joie R, Landeau B, Perrotin A, Bejanin A, Egret S, Pélerin A, et al. Intrinsic connectivity identifies the hippocampus as a main crossroad between Alzheimer’s and semantic dementia-targeted networks. Neuron. 2014;81(6):1417–28.

    Article  PubMed  Google Scholar 

  7. Habert MO, Marie S, Bertin H, Reynal M, Martini JB, Diallo M, et al. Optimization of brain PET imaging for a multicentre trial: the French CATI experience. EJNMMI Phys. 2016;3(1):6.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Clark CM, Schneider JA, Bedell BJ, Beach TG, Bilker WB, Mintun MA, et al. Use of Florbetapir-PET for imaging beta-amyloid pathology. JAMA. 2011;305(3):275–83.

    Article  CAS  PubMed  Google Scholar 

  9. Clark CM, Pontecorvo MJ, Beach TG, Bedell BJ, Coleman RE, Doraiswamy PM, et al. Cerebral PET with Florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: a prospective cohort study. Lancet Neurol. 2012;11(8):669–78.

    Article  CAS  PubMed  Google Scholar 

  10. Joshi AD, Pontecorvo MJ, Clark CM, Carpenter AP, Jennings DL, Sadowsky CH, et al. Performance characteristics of amyloid PET with Florbetapir F 18 in patients with Alzheimer’s disease and cognitively normal subjects. J Nucl Med. 2012;53(3):378–84.

    Article  CAS  PubMed  Google Scholar 

  11. Chételat G, La Joie R, Villain N, Perrotin A, de La Sayette V, Eustache F, Vandenberghe R. Amyloid imaging in cognitively normal individuals, at-risk populations and preclinical Alzheimer’s disease. Neuroimage Clin. 2013;2:356–65.

    Article  PubMed  PubMed Central  Google Scholar 

  12. La Joie R, Perrotin A, Barré L, Hommet C, Mézenge F, Ibazizene M, et al. Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (aβ) load in Alzheimer’s disease dementia. J Neurosci. 2012;32(46):16265–73.

    Article  PubMed  Google Scholar 

  13. Thomas BA, Erlandsson K, Modat M, Thurfjell L, Vandenberghe R, Ourselin S, Hutton BF. The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2011;38(6):1104–19.

    Article  PubMed  Google Scholar 

  14. Brendel M, Högenauer M, Delker A, Sauerbeck J, Bartenstein P, Seibyl J, et al. Improved longitudinal [(18)F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction. Neuroimage. 2015;108:450–9.

    Article  PubMed  Google Scholar 

  15. Chen K, Roontiva A, Thiyyagura P, Lee W, Liu X, Ayutyanont N, et al. Improved power for characterizing longitudinal amyloid-β PET changes and evaluating amyloid-modifying treatments with a cerebral white matter reference region. J Nucl Med. 2015;56(4):560–6.

    Article  CAS  PubMed  Google Scholar 

  16. Landau SM, Thomas BA, Thurfjell L, Schmidt M, Margolin R, Mintun M, et al. Amyloid PET imaging in Alzheimer’s disease: A comparison of three radiotracers. Eur J Nucl Med Mol Imaging. 2014;41(7):1398–407.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Thurfjell L, Lilja J, Lundqvist R, Buckley C, Smith A, Vandenberghe R, Sherwin P. Automated quantification of 18f-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads. J Nucl Med. 2014;55(10):1623–8.

    Article  CAS  PubMed  Google Scholar 

  18. Vandenberghe R, Van Laere K, Ivanoiu A, Salmon E, Bastin C, Triau E, et al. 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial. Ann Neurol. 2010;68(3):319–29.

    Article  PubMed  Google Scholar 

  19. Villemagne VL, Mulligan RS, Pejoska S, Ong K, Jones G, O’Keefe G, et al. Comparison of 11c-pib and 18f-florbetaben for aβ imaging in ageing and Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2012;39(6):983–9.

    Article  CAS  PubMed  Google Scholar 

  20. Jagust WJ. Amyloid imaging: Liberal or conservative? Let the data decide. Arch Neurol. 2011;68(11):1377–8.

    Article  PubMed  Google Scholar 

  21. Villeneuve S, Rabinovici GD, Cohn-Sheehy BI, Madison C, Ayakta N, Ghosh PM, et al. Existing Pittsburgh compound-b positron emission tomography thresholds are too high: statistical and pathological evaluation. Brain. 2015;138(Pt 7):2020–33.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mormino EC, Brandel MG, Madison CM, Rabinovici GD, Marks S, Baker SL, Jagust WJ. Not quite PIB-positive, not quite PIB-negative: Slight PIB elevations in elderly normal control subjects are biologically relevant. Neuroimage. 2012;59(2):1152–60.

    Article  PubMed  Google Scholar 

  23. Pike KE, Savage G, Villemagne VL, Ng S, Moss SA, Maruff P, et al. Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer’s disease. Brain. 2007;130(Pt 11):2837–44.

    Article  PubMed  Google Scholar 

  24. Aizenstein HJ, Nebes RD, Saxton JA, Price JC, Mathis CA, Tsopelas ND, et al. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol. 2008;65(11):1509–17.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rowe CC, Ellis KA, Rimajova M, Bourgeat P, Pike KE, Jones G, et al. Amyloid imaging results from the Australian imaging, biomarkers and lifestyle (AIBL) study of aging. Neurobiol Aging. 2010;31(8):1275–83.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We sincerely thank Alain Giron for helping with the statistical analyses and Anne Bertrand for her input on the manuscript. INSIGHT-AD study group Audrain C, Bakardjian H, Benali H, Bertin, H, Boukadida L, Cacciamani F, Causse-Lemercier V, Cavedo E, Chiesa P, Colliot O, Dos Santos A, Dubois B, Durrleman S, Epelbaum S, Gagliardi G, Genthon R, Habert M-O, Hampel H, Jungalee N, Kas A, Lehericy S, Lamari F, Letondor C, Levy M, Lista S, Mochel F, Nyasse F, Poisson C, Potier MC, Revillon M, Rojkova K, Roy P, Santos-Andrade K, Santos A, Simon V, Sole M, Tandetnik C, Thiebaud De Schotten M.

Author information

Authors and Affiliations

  1. Centre pour l’Acquisition et le Traitement des Images, Saclay, Paris, France

    Marie-Odile Habert, Hugo Bertin, Mickael Labit, Mamadou Diallo, Sullivan Marie, Kelly Martineau, Aurélie Kas & Jean-François Mangin

  2. Département de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France

    Marie-Odile Habert, Aurélie Kas & Valérie Causse-Lemercier

  3. Laboratoire d’Imagerie Biomédicale, Inserm U 1146, CNRS UMR 7371, Sorbonne Universités, UPMC Univ Paris 06, Paris, France

    Marie-Odile Habert & Aurélie Kas

  4. Département de Neurologie, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), AP-HP, Paris, France

    Hovagim Bakardjian, Stéphane Epelbaum, Marion Houot, Harald Hampel & Bruno Dubois

  5. Institut Cerveau Moelle (ICM) UMR S 1127, Frontlab, Paris, France

    Hovagim Bakardjian, Stéphane Epelbaum, Harald Hampel & Bruno Dubois

  6. INSERM U1077, Caen, France

    Gael Chételat

  7. Université de Caen Basse-Normandie UMR-S1077, Caen, France

    Gael Chételat

  8. Ecole Pratique des Hautes Etudes UMR-S1077, Caen, France

    Gael Chételat

  9. CHU de Caen, U1077, Caen, France

    Gael Chételat

  10. AXA Research Fund and UPMC Chair, Sorbonne Universities, Pierre and Marie Curie University, Paris 06, France

    Harald Hampel

  11. NeuroSpin, I2BM, Commissariat à l’Energie Atomique, Saclay, France

    Jean-François Mangin

Authors
  1. Marie-Odile Habert
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  2. Hugo Bertin
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  3. Mickael Labit
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  4. Mamadou Diallo
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  5. Sullivan Marie
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  6. Kelly Martineau
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  7. Aurélie Kas
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  8. Valérie Causse-Lemercier
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  9. Hovagim Bakardjian
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  10. Stéphane Epelbaum
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  11. Gael Chételat
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  12. Marion Houot
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  13. Harald Hampel
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  14. Bruno Dubois
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Consortia

INSIGHT-AD study group

  • C. Audrain
  • , H. Bakardjian
  • , H. Benali
  • , H. Bertin
  • , L. Boukadida
  • , F. Cacciamani
  • , V. Causse-Lemercier
  • , E. Cavedo
  • , P. Chiesa
  • , O. Colliot
  • , A. Dos Santos
  • , B. Dubois
  • , S. Durrleman
  • , S. Epelbaum
  • , G. Gagliardi
  • , R. Genthon
  • , M.-O. Habert
  • , H. Hampel
  • , N. Jungalee
  • , A. Kas
  • , S. Lehericy
  • , F. Lamari
  • , C. Letondor
  • , M. Levy
  • , S. Lista
  • , F. Mochel
  • , F. Nyasse
  • , C. Poisson
  • , M. C. Potier
  • , M. Revillon
  • , K. Rojkova
  • , P. Roy
  • , K. Santos-Andrade
  • , A. Santos
  • , V. Simon
  • , M. Sole
  • , C. Tandetnik
  •  & M. Thiebaud De Schotten

Corresponding author

Correspondence to Marie-Odile Habert.

Ethics declarations

Research involving human participants

A national ethics committee approved the INSIGHT study (ANSM 130134B-31), and all subjects gave written informed consent before their inclusion in the study. The IMAP study was approved by a regional ethics committee (Comité de Protection des Personnes Nord-Ouest III) and all participants gave written informed consent to the study prior to the investigation.

Funding

The INSIGHT-preAD study is supported by the IHU-A-ICM, Investissement d’Avenir from the French Ministry of Health, the French Foundation Plan-Alzheimer, Pfizer and AVID/Lilly companies. The French Foundation Plan-Alzheimer supports the CATI. Harald Hampel is supported by the AXA Research Fund, the Fondation Université Pierre et Marie Curie and the “Fondation pour la Recherche sur Alzheimer”, Paris, France. The research leading to these results has received funding from the program “Investissements d’avenir” ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6).

Additional information

Members of INSIGHT-AD study group are listed in acknowledgements.

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Habert, MO., Bertin, H., Labit, M. et al. Evaluation of amyloid status in a cohort of elderly individuals with memory complaints: validation of the method of quantification and determination of positivity thresholds. Ann Nucl Med 32, 75–86 (2018). https://doi.org/10.1007/s12149-017-1221-0

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  • DOI: https://doi.org/10.1007/s12149-017-1221-0

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