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

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

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.

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References

  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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  4. Clark CM et al. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA. 2011;305(3):275–83.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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. 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  11. Rodrigue KM et al. beta-Amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology. 2012;78(6):387–95.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  19. Pope WB et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology. 2009;252(1):182–9.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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. 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.

    Article  CAS  PubMed  Google Scholar 

  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.

    PubMed Central  PubMed  Google Scholar 

  24. Firbank MJ et al. Diffusion tensor imaging in Alzheimer’s disease and dementia with Lewy bodies. Psychiatry Res. 2011;194(2):176–83.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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. 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. 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Jagust W et al. What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? Neurology. 2007;69(9):871–7.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  34. Wolz R et al. Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One. 2011;6(10):e25446.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  36. Stadlbauer A et al. Magnetic resonance fiber density mapping of age-related white matter changes. Eur J Radiol. 2012;81(12):4005–12.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

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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.

Author information

Authors and Affiliations

  1. Inserm, imagerie cérébrale et handicaps neurologiques UMR 825, Centre Hospitalier Universitaire de Toulouse, Place du Dr. Baylac, CHU Purpan, Pavillon Baudot, 31059, Toulouse cedex 9, France

    Federico Nemmi, Laure Saint-Aubert, Djilali Adel, Anne-Sophie Salabert, Jérémie Pariente, Pierre Payoux & Patrice Péran

  2. Université de Toulouse, UPS, imagerie cérébrale et handicaps neurologiques UMR 825, Centre Hospitalier Universitaire de Toulouse, Toulouse, France

    Federico Nemmi, Laure Saint-Aubert, Djilali Adel, Anne-Sophie Salabert, Jérémie Pariente, Pierre Payoux & Patrice Péran

  3. Service de Médecine Nucléaire, Pôle Imagerie, Centre Hospitalier Universitaire de Toulouse, Toulouse, France

    Djilali Adel, Anne-Sophie Salabert & Pierre Payoux

  4. Service de neurologie, pôle neurosciences, Centre Hospitalier Universitaire de Toulouse, Toulouse, France

    Jérémie Pariente & Emmanuel J. Barbeau

  5. Université de Toulouse, UPS, centre de recherche cerveau et cognition, CNRS, CerCo, Toulouse, France

    Emmanuel J. Barbeau

Authors
  1. Federico Nemmi
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  2. Laure Saint-Aubert
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  3. Djilali Adel
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  4. Anne-Sophie Salabert
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  5. Jérémie Pariente
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  6. Emmanuel J. Barbeau
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  7. Pierre Payoux
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  8. Patrice Péran
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Corresponding author

Correspondence to Federico Nemmi.

Additional information

Federico Nemmi and Laure Saint-Aubert contributed equally to this work.

Electronic supplementary material

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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)

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)

Supplementary Table 1

(DOCX 15 kb)

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Nemmi, F., Saint-Aubert, L., Adel, D. et al. Insight on AV-45 binding in white and grey matter from histogram analysis: a study on early Alzheimer’s disease patients and healthy subjects. Eur J Nucl Med Mol Imaging 41, 1408–1418 (2014). https://doi.org/10.1007/s00259-014-2728-4

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  • DOI: https://doi.org/10.1007/s00259-014-2728-4

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