Molecular Imaging and Biology

, Volume 20, Issue 4, pp 659–666 | Cite as

Coupled Imaging with [18F]FBB and [18F]FDG in AD Subjects Show a Selective Association Between Amyloid Burden and Cortical Dysfunction in the Brain

  • Agostino ChiaravallotiEmail author
  • Anna Elisa Castellano
  • Maria Ricci
  • Gaetano Barbagallo
  • Pasqualina Sannino
  • Francesco Ursini
  • Georgios Karalis
  • Orazio Schillaci
Research Article



The present study was aimed to investigate the relationships between dysfunction of cortical glucose metabolism as detectable by means of 2-deoxy-2-[18F]fluoro -D-glucose ([18F]FDG) positron emission tomography/x-ray computed tomography (PET/CT) and amyloid burden as detectable by means of 4-{(E)-2-[4-(2-{2-[2-[18F]fluoroethoxy]ethoxy}ethoxy)phenyl]vinyl}-N-methylaniline (florbetaben; [18F]FBB) in a group of patients affected by Alzheimer’s disease (AD).


We examined 38 patients newly diagnosed with AD according to the NINCDS-ADRDA criteria. All the subjects underwent a PET/CT scan using both [18F]FDG and [18F]FBB with an average interval of 1 month. We used statistical parametric mapping (SPM8) implemented in Matlab R2012b and WFU pickatlas for the definition of a region of interest (ROI) mask including the whole cortex. These data were then normalized on the counts of the cerebellum and then used for a regression analysis on [18F]FDG scans in SPM. Furthermore, 58 control subjects were used as control group for [18F]FDG PET/CT scans.


SPM analysis in AD patients showed a significant negative correlation between [18F] FBB and [18F] FDG uptake in temporal and parietal lobes bilaterally. Of note, these areas in AD patients displayed a marked glucose hypometabolism compared to control group.


Combined imaging with [18F]FBB and [18FFDG shows that amyloid burden in the brain is related to cortical dysfunction of temporal and parietal lobes in AD.

Key words

Alzheimer PET [18F]FDG [18F]FBB Brain imaging 



The authors wish to thank Tiziana Martino (IRCCS Neuromed) for data collection.

Financial Support

The Authors declare that they do not receive financial support for this work.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained accordingly.


  1. 1.
    Alzheimer’s Assoc. (2016) 2016 Alzheimer’s disease facts and figures. Alzheimers Dement 12:459–509CrossRefGoogle Scholar
  2. 2.
    Swerdlow RH (2007) Pathogenesis of Alzheimer’s disease. Clin Interv Aging 2(3):347–359PubMedPubMedCentralGoogle Scholar
  3. 3.
    Hardy JA, Higgins GA (1992) Alzheimer’s disease: the amyloid cascade hypothesis. Science 256(5054):184–185. CrossRefPubMedGoogle Scholar
  4. 4.
    Chiaravalloti A, Danieli R, Lacanfora A et al (2017) Usefulness of 18F florbetaben in diagnosis of Alzheimer’s disease and other types of dementia. Curr Alzheimer Res 14:154–160CrossRefPubMedGoogle Scholar
  5. 5.
    Sabri O, Seibyl J, Rowe C, Barthel H (2015) Beta-amyloid imaging with florbetaben. Clin Transl Imaging 3(1):13–26. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Klunk WE, Engler H, Nordberg A et al (2004) Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-B. Ann Neurol 55:306–319CrossRefPubMedGoogle Scholar
  7. 7.
    O’Brien JT, Firbank MJ, Davison C, Barnett N, Bamford C, Donaldson C, Olsen K, Herholz K, Williams D, Lloyd J (2014) 18F-FDG PET and perfusion SPECT in the diagnosis of Alzheimer and Lewy body dementias. J Nucl Med 55(12):1959–1965. CrossRefPubMedGoogle Scholar
  8. 8.
    Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21(10):1133–1145. CrossRefPubMedGoogle Scholar
  9. 9.
    Newberg AB, Arnold SE, Wintering N, Rovner BW, Alavi A (2012) Initial clinical comparison of 18F-florbetapir and 18F-FDG PET in patients with Alzheimer disease and controls. J Nucl Med 53(6):902–907. CrossRefPubMedGoogle Scholar
  10. 10.
    Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R, Thomas RG, Walter S, Trojanowski JQ, Shaw LM, Beckett LA, Jack CR Jr, Jagust W, Toga AW, Saykin AJ, Morris JC, Green RC, Weiner MW, Alzheimer’s Disease Neuroimaging Initiative (2010) Clinical Core of the Alzheimer’s Disease Neuroimaging Initiative: progress and plans. Alzheimers Dement 6(3):239–246. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Shin J, Tsui W, Li Y et al (2011) Resting-state glucose metabolism level is associated with the regional pattern of amyloid pathology in Alzheimer’s disease. Int J Alzheimers Dis 2011:759780PubMedPubMedCentralGoogle Scholar
  12. 12.
    Adriaanse SM, van Dijk KR, Ossenkoppele R, Reuter M, Tolboom N, Zwan MD, Yaqub M, Boellaard R, Windhorst AD, van der Flier WM, Scheltens P, Lammertsma AA, Barkhof F, van Berckel BN (2014) The effect of amyloid pathology and glucose metabolism on cortical volume loss over time in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 41(6):1190–1198. PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Kikuchi M, Hirosawa T, Yokokura M et al (2011) Effects of brain amyloid deposition and reduced glucose metabolism on the default mode of brain function in normal aging. J Neurosci 31:11193–11199CrossRefPubMedGoogle Scholar
  14. 14.
    Mosconi L, Andrews RD, Matthews DC (2013) Comparing brain amyloid deposition, glucose metabolism, and atrophy in mild cognitive impairment with and without a family history of dementia. J Alzheimers Dis 35(3):509–524. CrossRefPubMedGoogle Scholar
  15. 15.
    Edison P, Archer HA, Hinz R, Hammers A, Pavese N, Tai YF, Hotton G, Cutler D, Fox N, Kennedy A, Rossor M, Brooks DJ (2007) Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology 68(7):501–508. CrossRefPubMedGoogle Scholar
  16. 16.
    Chiaravalloti A, Koch G, Toniolo S et al (2016) Comparison between early-onset and late-onset Alzheimer’s disease patients with amnestic presentation: CSF and 18F-FDG PET study. Dement Geriatr Cogn Dis Extra 6:108–119CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Liguori C, Chiaravalloti A, Sancesario G et al (2016) Cerebrospinal fluid lactate levels and brain [18F]FDG PET hypometabolism within the default mode network in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 43:2040–2049CrossRefPubMedGoogle Scholar
  18. 18.
    Varma A, Snowden J, Lloyd J et al (1999) Evaluation of the NINCDS-ADRDA criteria in the differentiation of Alzheimer’s disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry 66:184–188CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Pierantozzi M, Panella M, Palmieri MG et al (2004) Different TMS patterns of intracortical inhibition in early onset Alzheimer dementia and frontotemporal dementia. Clin Neurophysiol 115:2410–2418CrossRefPubMedGoogle Scholar
  20. 20.
    Alessandrini M, Pagani M, Napolitano B et al (2013) Early and phasic cortical metabolic changes in vestibular neuritis onset. PLoS One 8:e57596CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Puri KS, Suresh KR, Gogtay NJ, Thatte UM (2009) Declaration of Helsinki, 2008: implications for stakeholders in research. J Postgrad Med 55(2):131–134. CrossRefPubMedGoogle Scholar
  22. 22.
    Chiaravalloti A, Barbagallo G, Ricci M et al (2017) Brain metabolic correlates of CSF tau protein in a large cohort of Alzheimer’s disease patients: a CSF and FDG PET study. Brain Res 1678:116–122CrossRefPubMedGoogle Scholar
  23. 23.
    Chiaravalloti A, Ursini F, Fiorentini A et al (2017) Functional correlates of TSH, fT3 and fT4 in Alzheimer disease: a F-18 FDG PET/CT study. Sci Rep 7:6220CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Bennett CM, Wolford GL, Miller MB (2009) The principled control of false positives in neuroimaging. Soc Cogn Affect Neurosci 4(4):417–422. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Lancaster JL, Rainey LH, Summerlin JL, Freitas CS, Fox PT, Evans AC, Toga AW, Mazziotta JC (1997) Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp 5(4):238–242.<238::AID-HBM6>3.0.CO;2-4 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Soonawala D, Amin T, Ebmeier KP et al (2002) Statistical parametric mapping of (99m)Tc-HMPAO-SPECT images for the diagnosis of Alzheimer’s disease: normalizing to cerebellar tracer uptake. NeuroImage 17:1193–1202CrossRefPubMedGoogle Scholar
  27. 27.
    Schmahmann JD, Doyon J, McDonald D et al (1999) Three-dimensional MRI atlas of the human cerebellum in proportional stereotaxic space. NeuroImage 10:233–260CrossRefPubMedGoogle Scholar
  28. 28.
    Cohen AD, Klunk WE (2014) Early detection of Alzheimer’s disease using PiB and FDG PET. Neurobiol Dis 72 Pt A:117–122CrossRefPubMedGoogle Scholar
  29. 29.
    Petrie EC, Cross DJ, Galasko D, Schellenberg GD, Raskind MA, Peskind ER, Minoshima S (2009) Preclinical evidence of Alzheimer changes: convergent cerebrospinal fluid biomarker and fluorodeoxyglucose positron emission tomography findings. Arch Neurol 66(5):632–637. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Vukovich R, Perneczky R, Drzezga A, Förstl H, Kurz A, Riemenschneider M (2009) Brain metabolic correlates of cerebrospinal fluid beta-amyloid 42 and tau in Alzheimer’s disease. Dement Geriatr Cogn Disord 27(5):474–480. CrossRefPubMedGoogle Scholar
  31. 31.
    Grimmer T, Riemenschneider M, Forstl H et al (2009) Beta amyloid in Alzheimer’s disease: increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid. Biol Psychiatry 65:927–934CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Takahashi R, Ishii K, Yokoyama K, For The Alzheimer SDNIEY (2017) Validation of a new imaging technique using the glucose metabolism to amyloid deposition ratio in the diagnosis of Alzheimer’s disease. Curr Alzheimer Res 14(2):161–168. CrossRefPubMedGoogle Scholar
  33. 33.
    Altmann A, Ng B, Landau SM, Jagust WJ, Greicius MD (2015) Regional brain hypometabolism is unrelated to regional amyloid plaque burden. Brain 138(12):3734–3746. CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Furst AJ, Lal RA (2011) Amyloid-beta and glucose metabolism in Alzheimer’s disease. J Alzheimers Dis 26(Suppl 3):105–116CrossRefPubMedGoogle Scholar
  35. 35.
    Cohen AD, Price JC, Weissfeld LA, James J, Rosario BL, Bi W, Nebes RD, Saxton JA, Snitz BE, Aizenstein HA, Wolk DA, DeKosky ST, Mathis CA, Klunk WE (2009) Basal cerebral metabolism may modulate the cognitive effects of Abeta in mild cognitive impairment: an example of brain reserve. J Neurosci 29(47):14770–14778. CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Klupp E, Grimmer T, Tahmasian M et al (2015) Prefrontal hypometabolism in Alzheimer disease is related to longitudinal amyloid accumulation in remote brain regions. J Nucl Med 56:399–404CrossRefPubMedGoogle Scholar
  37. 37.
    Perani D (2014) FDG-PET and amyloid-PET imaging: the diverging paths. Curr Opin Neurol 27(4):405–413. CrossRefPubMedGoogle Scholar
  38. 38.
    Murray J, Tsui WH, Li Y et al (2014) FDG and amyloid PET in cognitively normal individuals at risk for late-onset Alzheimer’s disease. Adv Mol Imaging 4(02):15–26. CrossRefGoogle Scholar

Copyright information

© World Molecular Imaging Society 2018

Authors and Affiliations

  • Agostino Chiaravalloti
    • 1
    • 2
    Email author
  • Anna Elisa Castellano
    • 2
  • Maria Ricci
    • 3
  • Gaetano Barbagallo
    • 4
  • Pasqualina Sannino
    • 2
  • Francesco Ursini
    • 5
  • Georgios Karalis
    • 1
  • Orazio Schillaci
    • 1
    • 2
  1. 1.Department of Biomedicine and PreventionUniversity Tor VergataRomeItaly
  2. 2.IRCCS NeuromedPozzilliItaly
  3. 3.Department of Radiological, Oncological and Pathological SciencesSapienza University of RomeRomeItaly
  4. 4.Institute of NeurologyUniversity Magna Graecia of CatanzaroCatanzaroItaly
  5. 5.Department of Health SciencesUniversity Magna Graecia of CatanzaroCatanzaroItaly

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