Advertisement

Insight on AV-45 binding in white and grey matter from histogram analysis: a study on early Alzheimer’s disease patients and healthy subjects

  • Federico Nemmi
  • Laure Saint-Aubert
  • Djilali Adel
  • Anne-Sophie Salabert
  • Jérémie Pariente
  • Emmanuel J. Barbeau
  • Pierre Payoux
  • Patrice Péran
Original Article

Abstract

Purpose

AV-45 amyloid biomarker is known to show uptake in white matter in patients with Alzheimer’s disease (AD), but also in the healthy population. This binding, thought to be of a non-specific lipophilic nature, has not yet been investigated. The aim of this study was to determine the differential pattern of AV-45 binding in white matter in healthy and pathological populations.

Methods

We recruited 24 patients presenting with AD at an early stage and 17 matched, healthy subjects. We used an optimized positron emission tomography-magnetic resonance imaging (PET-MRI) registration method and an approach based on an intensity histogram using several indices. We compared the results of the intensity histogram analyses with a more canonical approach based on target-to-cerebellum Standard Uptake Value (SUVr) in white and grey matter using MANOVA and discriminant analyses. A cluster analysis on white and grey matter histograms was also performed.

Results

White matter histogram analysis revealed significant differences between AD and healthy subjects, which were not revealed by SUVr analysis. However, white matter histograms were not decisive to discriminate groups, and indices based on grey matter only showed better discriminative power than SUVr. The cluster analysis divided our sample into two clusters, showing different uptakes in grey, but also in white matter.

Conclusion

These results demonstrate that AV-45 binding in white matter conveys subtle information not detectable using the SUVr approach. Although it is not more efficient than standard SUVr in discriminating AD patients from healthy subjects, this information could reveal white matter modifications.

Keywords

AV-45 Amyloid Intensity histogram Discrimination analysis Alzheimer’s disease 

Notes

Acknowledgments

This study was supported by a grant from the University Hospital of Toulouse, local grant 2007, and a grant from the Agence Nationale de la Recherche. The authors thank the promoter of this study and Toulouse University Hospital.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

259_2014_2728_MOESM1_ESM.pdf (359 kb)
Supplementary Figure 1 Intensity histograms of grey and white matter of an AD patient (a) and of a healthy control (b). Histograms were derived from grey and white matter probability images thresholded at 0.25, 0.50, 0.75 and 0.95, corresponding respectively to probabilities of 25 %, 50 %, 75 % and 95 % of a voxel of being in grey/white matter. No differences related to the chosen threshold are present. (PDF 358 kb)
259_2014_2728_MOESM2_ESM.pdf (393 kb)
Supplementary Figure 2 White matter mean histograms of AD (red filled squares) and HC (blue filled squares) and white matter histograms of the three AD patients and of the seven HC subjects misclassified in the discriminant analysis performed using white matter indices. The mean histograms were calculated without the misclassified subjects in each group. Vertical dashed-dotted lines in panels a and b mark 25th and 75th percentiles of histograms. (PDF 392 kb)
259_2014_2728_MOESM3_ESM.docx (16 kb)
Supplementary Table 1 (DOCX 15 kb)

References

  1. 1.
    Choi SR et al. Preclinical properties of 18F-AV-45: a PET agent for Abeta plaques in the brain. J Nucl Med. 2009;50(11):1887–94.PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Carpenter Jr AP et al. The use of the exploratory IND in the evaluation and development of 18F-PET radiopharmaceuticals for amyloid imaging in the brain: a review of one company’s experience. Q J Nucl Med Mol Imaging. 2009;53(4):387–93.PubMedGoogle Scholar
  3. 3.
    Clark CM et al. Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-beta plaques: a prospective cohort study. Lancet Neurol. 2012;11(8):669–78.PubMedCrossRefGoogle Scholar
  4. 4.
    Clark CM et al. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA. 2011;305(3):275–83.PubMedCrossRefGoogle Scholar
  5. 5.
    Lin KJ et al. Whole-body biodistribution and brain PET imaging with [18F]AV-45, a novel amyloid imaging agent–a pilot study. Nucl Med Biol. 2010;37(4):497–508.PubMedCrossRefGoogle Scholar
  6. 6.
    Wong DF 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.PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Saint-Aubert L et al. Cortical florbetapir-PET amyloid load in prodromal Alzheimer’s disease patients. Eur J Nucl Med Mol Imaging. 2013;3(1):43.Google Scholar
  8. 8.
    Camus V et al. Using PET with 18F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment. Eur J Nucl Med Mol Imaging. 2012;39(4):621–31.PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Fleisher AS 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(11):1404–11.PubMedCrossRefGoogle Scholar
  10. 10.
    La Joie R et al. Region-specific hierarchy between atrophy, hypometabolism, and beta-amyloid (Abeta) load in Alzheimer’s disease dementia. J Neurosci. 2012;32(46):16265–73.PubMedCrossRefGoogle Scholar
  11. 11.
    Rodrigue KM et al. beta-Amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology. 2012;78(6):387–95.PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Villemagne VL et al. Amyloid imaging with (18)F-florbetaben in Alzheimer disease and other dementias. J Nucl Med. 2011;52(8):1210–7.PubMedCrossRefGoogle Scholar
  13. 13.
    Villemagne VL et al. Comparison of 11C-PiB and 18F-florbetaben for Abeta imaging in ageing and Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2012;39(6):983–9.PubMedCrossRefGoogle Scholar
  14. 14.
    Fodero-Tavoletti MT et al. Characterization of PiB binding to white matter in Alzheimer disease and other dementias. J Nucl Med. 2009;50(2):198–204.PubMedCrossRefGoogle Scholar
  15. 15.
    Dubois B et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 2007;6(8):734–46.PubMedCrossRefGoogle Scholar
  16. 16.
    Wallon D et al. The French series of autosomal dominant early onset Alzheimer’s disease cases: mutation spectrum and cerebrospinal fluid biomarkers. J Alzheimers Dis. 2012;30(4):847–56.PubMedGoogle Scholar
  17. 17.
    Fleiss JL, Nee JCM, Landis JR. Large sample variance of kappa in the case of different sets of raters. Psychol Bull. 1979;86(5):974–7.CrossRefGoogle Scholar
  18. 18.
    Kyriazi S et al. Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging–value of histogram analysis of apparent diffusion coefficients. Radiology. 2011;261(1):182–92.PubMedCrossRefGoogle Scholar
  19. 19.
    Pope WB et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology. 2009;252(1):182–9.PubMedCrossRefGoogle Scholar
  20. 20.
    Pope WB et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol. 2011;32(5):882–9.PubMedCrossRefGoogle Scholar
  21. 21.
    Conover WJ, Iman RL. Rank transformations as a bridge between parametric and nonparametric statistics—rejoinder. Am Stat. 1981;35(3):132–3.Google Scholar
  22. 22.
    Barthel H et al. Cerebral amyloid-beta PET with florbetaben (18F) in patients with Alzheimer’s disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol. 2011;10(5):424–35.PubMedCrossRefGoogle Scholar
  23. 23.
    Canu E et al. Mapping the structural brain changes in Alzheimer’s disease: the independent contribution of two imaging modalities. J Alzheimers Dis. 2011;26 Suppl 3:263–74.PubMedCentralPubMedGoogle Scholar
  24. 24.
    Firbank MJ et al. Diffusion tensor imaging in Alzheimer’s disease and dementia with Lewy bodies. Psychiatry Res. 2011;194(2):176–83.PubMedCrossRefGoogle Scholar
  25. 25.
    Vandenberghe R et al. Binary classification of 18F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI. Neuroimage. 2013;64:517–25.PubMedCrossRefGoogle Scholar
  26. 26.
    Kim JH et al. Regional white matter hyperintensities in normal aging, single domain amnestic mild cognitive impairment, and mild Alzheimer’s disease. J Clin Neurosci. 2011;18(8):1101–6.PubMedCrossRefGoogle Scholar
  27. 27.
    Makedonov I, Black SE, MacIntosh BJ. Cerebral small vessel disease in aging and Alzheimer’s disease: a comparative study using MRI and SPECT. Eur J Neurol. 2013;20(2):243–50.PubMedCrossRefGoogle Scholar
  28. 28.
    Chao LL et al. Associations between white matter hyperintensities and beta amyloid on integrity of projection, association, and limbic fiber tracts measured with diffusion tensor MRI. PLoS One. 2013;8(6):e65175.PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Hong YT, et al. Amyloid imaging with carbon 11-labeled Pittsburgh compound B for traumatic brain injury. JAMA Neurol. 2014;71(1):23–31.Google Scholar
  30. 30.
    Gurol ME, et al. Cerebral amyloid angiopathy burden associated with leukoaraiosis: A positron emission tomography/magnetic resonance imaging study. Ann Neurol. 2013;73(4):529–536.Google Scholar
  31. 31.
    Casanova R, Hsu F-C, Espeland MA, Alzheimer’s Disease Neuroimaging Initiative. Classification of structural MRI images in Alzheimer’s disease from the perspective of ill-posed problems. PLoS One. 2012;7(10):e44877.PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Jagust W et al. What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? Neurology. 2007;69(9):871–7.PubMedCrossRefGoogle Scholar
  33. 33.
    Westman E, Muehlboeck JS, Simmons A. Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage. 2012;62(1):229–38.PubMedCrossRefGoogle Scholar
  34. 34.
    Wolz R et al. Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One. 2011;6(10):e25446.PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Jeon T et al. Regional changes of cortical mean diffusivities with aging after correction of partial volume effects. Neuroimage. 2012;62(3):1705–16.PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Stadlbauer A et al. Magnetic resonance fiber density mapping of age-related white matter changes. Eur J Radiol. 2012;81(12):4005–12.PubMedCrossRefGoogle Scholar
  37. 37.
    Watanabe H et al. Progression and prognosis in multiple system atrophy: an analysis of 230 Japanese patients. Brain. 2002;125(Pt 5):1070–83.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Federico Nemmi
    • 1
    • 2
  • Laure Saint-Aubert
    • 1
    • 2
  • Djilali Adel
    • 1
    • 2
    • 3
  • Anne-Sophie Salabert
    • 1
    • 2
    • 3
  • Jérémie Pariente
    • 1
    • 2
    • 4
  • Emmanuel J. Barbeau
    • 4
    • 5
  • Pierre Payoux
    • 1
    • 2
    • 3
  • Patrice Péran
    • 1
    • 2
  1. 1.Inserm, imagerie cérébrale et handicaps neurologiques UMR 825, Centre Hospitalier Universitaire de ToulouseToulouse cedex 9France
  2. 2.Université de Toulouse, UPS, imagerie cérébrale et handicaps neurologiques UMR 825, Centre Hospitalier Universitaire de ToulouseToulouseFrance
  3. 3.Service de Médecine Nucléaire, Pôle ImagerieCentre Hospitalier Universitaire de ToulouseToulouseFrance
  4. 4.Service de neurologie, pôle neurosciencesCentre Hospitalier Universitaire de ToulouseToulouseFrance
  5. 5.Université de Toulouse, UPS, centre de recherche cerveau et cognition, CNRS, CerCoToulouseFrance

Personalised recommendations