Skip to main content

Advertisement

SpringerLink
Log in

Quantitative longitudinal interrelationships between brain metabolism and amyloid deposition during a 2-year follow-up in patients with early Alzheimer’s disease

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

Abstract

Purpose

Similar regional anatomical distributions were reported for fibrillary amyloid deposition [measured by 11C-Pittsburgh compound B (PIB) positron emission tomography (PET)] and brain hypometabolism [measured by 18F-fluorodeoxyglucose (FDG) PET] in numerous Alzheimer’s disease (AD) studies. However, there is a lack of longitudinal studies evaluating the interrelationships of these two different pathological markers in the same AD population. Our most recent AD study suggested that the longitudinal pattern of hypometabolism anatomically follows the pattern of amyloid deposition with temporal delay, which indicates that neuronal dysfunction may spread within the anatomical pattern of amyloid pathology. Based on this finding we now hypothesize that in early AD patients quantitative longitudinal decline in hypometabolism may be related to the amount of baseline amyloid deposition during a follow-up period of 2 years.

Methods

Fifteen patients with mild probable AD underwent baseline (T1) and follow-up (T2) examination after 24 ± 2.1 months with [18F]FDG PET, [11C]PIB PET, structural T1-weighted MRI and neuropsychological testing [Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery]. Longitudinal cognitive measures and quantitative PET measures of amyloid deposition and metabolism [standardized uptake value ratios (SUVRs)] were obtained using volume of interest (VOI)-based approaches in the frontal-lateral-retrosplenial (FLR) network and in predefined bihemispheric brain regions after partial volume effect (PVE) correction of PET data. Statistical group comparisons (SUVRs and cognitive measures) between patients and 15 well-matched elderly controls who had undergone identical imaging procedures once as well as Pearson’s correlation analyses within patients were performed.

Results

Group comparison revealed significant cognitive decline and increased mean PIB/decreased FDG SUVRs in the FLR network as well as in several AD-typical regions in patients relative to controls. Concurrent with cognitive decline patients showed longitudinal increase in mean PIB/decrease in mean FDG SUVRs over time in the FLR network and in several AD-typical brain regions. Correlation analyses of FLR network SUVRs in patients revealed significant positive correlations between PIB T1 and delta FDG (FDG T1-T2) SUVRs, between PIB T1 and PIB T2 SUVRs, between FDG T1 and PIB T2 SUVRs as well as between FDG T1 and FDG T2 SUVRs, while significant negative correlations were found between FDG T1 and delta PIB (PIB T1-T2) SUVRs as well as between FDG T2 and delta FDG (FDG T1-T2) SUVRs. These findings were confirmed in locoregional correlation analyses, revealing significant associations in the same directions for two left hemispheric regions and nine right hemispheric regions, showing the strongest association for bilateral precuneus.

Conclusion

Baseline amyloid deposition in patients with mild probable AD was associated with longitudinal metabolic decline. Additionally, mildly decreased/relatively preserved baseline metabolism was associated with a longitudinal increase in amyloid deposition. The latter bidirectional associations were present in the whole AD-typical FLR network and in several highly interconnected hub regions (i.e. in the precuneus). Our longitudinal findings point to a bidirectional quantitative interrelationship of the two investigated AD pathologies, comprising an initial relative maintenance of neuronal activity in already amyloid-positive hub regions (neuronal compensation), followed by accelerated amyloid deposition, accompanied by functional neuronal decline (neuronal breakdown) along with cognitive decline.

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
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Braak H, Braak E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol 1991;82:239–59.

    Article  PubMed  CAS  Google Scholar 

  2. Braak H, Braak E. Diagnostic criteria for neuropathologic assessment of Alzheimer’s disease. Neurobiol Aging 1997;18:S85–8.

    Article  PubMed  CAS  Google Scholar 

  3. Förster S, Buschert VC, Buchholz HG, Teipel SJ, Friese U, Zach C, et al. Effects of a 6-month cognitive intervention program on brain metabolism in amnestic mild cognitive impairment and mild Alzheimer’s disease. J Alzheimers Dis 2011;25:695–706. doi:10.3233/JAD-2011-100996.

    PubMed  Google Scholar 

  4. Förster S, Grimmer T, Miederer I, Henriksen G, Yousefi BH, Graner P, et al. Regional expansion of hypometabolism in Alzheimer’s disease follows amyloid deposition with temporal delay. Biol Psychiatry 2012;71:792–7. doi:10.1016/j.biopsych.2011.04.023.

    Article  PubMed  Google Scholar 

  5. Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer’s disease treatment studies. Am J Psychiatry 2002;159:738–45.

    Article  PubMed  Google Scholar 

  6. Förster S, Teipel S, Zach C, Rominger A, Cumming P, Fougere CL, et al. FDG-PET mapping the brain substrates of visuo-constructive processing in Alzheimer’s disease. J Psychiatr Res 2010;44:462–9. doi:10.1016/j.jpsychires.2009.09.012.

    Article  PubMed  Google Scholar 

  7. Förster S, Vaitl A, Teipel SJ, Yakushev I, Mustafa M, la Fougère C, et al. Functional representation of olfactory impairment in early Alzheimer’s disease. J Alzheimers Dis 2010;22:581–91. doi:10.3233/JAD-2010-091549.

    PubMed  Google Scholar 

  8. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol 2004;55:306–19. doi:10.1002/ana.20009.

    Article  PubMed  CAS  Google Scholar 

  9. Drzezga A, Grimmer T, Henriksen G, Mühlau M, Perneczky R, Miederer I, et al. Effect of APOE genotype on amyloid plaque load and gray matter volume in Alzheimer disease. Neurology 2009;72:1487–94. doi:10.1212/WNL.0b013e3181a2e8d0.

    Article  PubMed  CAS  Google Scholar 

  10. Grimmer T, Riemenschneider M, Förstl H, Henriksen G, Klunk WE, Mathis CA, et al. Beta amyloid in Alzheimer’s disease: increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid. Biol Psychiatry 2009;65:927–34. doi:10.1016/j.biopsych.2009.01.027.

    Article  PubMed  CAS  Google Scholar 

  11. Jack Jr CR, Lowe VJ, Weigand SD, Wiste HJ, Senjem ML, Knopman DS, et al. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain 2009;132:1355–65. doi:10.1093/brain/awp062.

    Article  PubMed  Google Scholar 

  12. Scheinin NM, Aalto S, Koikkalainen J, Lötjönen J, Karrasch M, Kemppainen N, et al. Follow-up of [11C]PIB uptake and brain volume in patients with Alzheimer disease and controls. Neurology 2009;73:1186–92. doi:10.1212/WNL.0b013e3181bacf1b.

    Article  PubMed  CAS  Google Scholar 

  13. Kadir A, Almkvist O, Forsberg A, Wall A, Engler H, Långström B, et al. Dynamic changes in PET amyloid and FDG imaging at different stages of Alzheimer’s disease. Neurobiol Aging 2012;33:198.e1–14. doi:10.1016/j.neurobiolaging.2010.06.015.

    Article  Google Scholar 

  14. Engler H, Forsberg A, Almkvist O, Blomquist G, Larsson E, Savitcheva I, et al. Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease. Brain 2006;129:2856–66. doi:10.1093/brain/awl178.

    Article  PubMed  Google Scholar 

  15. Villemagne VL, Pike KE, Chételat G, Ellis KA, Mulligan RS, Bourgeat P, et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Ann Neurol 2011;69:181–92. doi:10.1002/ana.22248.

    Article  PubMed  CAS  Google Scholar 

  16. Cohen AD, Price JC, Weissfeld LA, James J, Rosario BL, Bi W, et al. Basal cerebral metabolism may modulate the cognitive effects of Abeta in mild cognitive impairment: an example of brain reserve. J Neurosci 2009;29:14770–8. doi:10.1523/JNEUROSCI.3669-09.2009.

    Article  PubMed  CAS  Google Scholar 

  17. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984;34:939–44.

    Article  PubMed  CAS  Google Scholar 

  18. Drzezga A, Grimmer T, Henriksen G, Stangier I, Perneczky R, Diehl-Schmid J, et al. Imaging of amyloid plaques and cerebral glucose metabolism in semantic dementia and Alzheimer’s disease. Neuroimage 2008;39:619–33. doi:10.1016/j.neuroimage.2007.09.020.

    Article  PubMed  Google Scholar 

  19. Ziolko SK, Weissfeld LA, Klunk WE, Mathis CA, Hoge JA, Lopresti BJ, et al. Evaluation of voxel-based methods for the statistical analysis of PIB PET amyloid imaging studies in Alzheimer’s disease. Neuroimage 2006;33:94–102. doi:10.1016/j.neuroimage.2006.05.063.

    Article  PubMed  Google Scholar 

  20. Hedden T, Van Dijk KR, Becker JA, Mehta A, Sperling RA, Johnson KA, et al. Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci 2009;29:12686–94. doi:10.1523/JNEUROSCI.3189-09.2009.

    Article  PubMed  CAS  Google Scholar 

  21. Drzezga A, Becker JA, Van Dijk KR, Sreenivasan A, Talukdar T, Sullivan C, et al. Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain 2011;134:1635–46. doi:10.1093/brain/awr066.

    Article  PubMed  Google Scholar 

  22. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968–80. doi:10.1016/j.neuroimage.2006.01.021.

    Article  PubMed  Google Scholar 

  23. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005;25:7709–17. doi:10.1523/JNEUROSCI.2177-05.2005.

    Article  PubMed  CAS  Google Scholar 

  24. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 2008;1124:1–38. doi:10.1196/annals.1440.011.

    Article  PubMed  Google Scholar 

  25. Kamenetz F, Tomita T, Hsieh H, Seabrook G, Borchelt D, Iwatsubo T, et al. APP processing and synaptic function. Neuron 2003;37:925–37.

    Article  PubMed  CAS  Google Scholar 

  26. Cirrito JR, Yamada KA, Finn MB, Sloviter RS, Bales KR, May PC, et al. Synaptic activity regulates interstitial fluid amyloid-beta levels in vivo. Neuron 2005;48:913–22. doi:10.1016/j.neuron.2005.10.028.

    Article  PubMed  CAS  Google Scholar 

  27. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 2004;101:4637–42. doi:10.1073/pnas.0308627101.

    Article  PubMed  CAS  Google Scholar 

  28. Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging 2011;32:1207–18. doi:10.1016/j.neurobiolaging.2009.07.002.

    Article  PubMed  Google Scholar 

  29. Edison P, Archer HA, Hinz R, Hammers A, Pavese N, Tai YF, et al. Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology 2007;68:501–8. doi:10.1212/01.wnl.0000244749.20056.d4.

    Article  PubMed  CAS  Google Scholar 

  30. Ng S, Villemagne VL, Berlangieri S, Lee ST, Cherk M, Gong SJ, et al. Visual assessment versus quantitative assessment of 11C-PIB PET and 18F-FDG PET for detection of Alzheimer’s disease. J Nucl Med 2007;48:547–52.

    Article  PubMed  CAS  Google Scholar 

  31. Grimmer T, Tholen S, Yousefi BH, Alexopoulos P, Förschler A, Förstl H, et al. Progression of cerebral amyloid load is associated with the apolipoprotein E ε4 genotype in Alzheimer’s disease. Biol Psychiatry 2010;68:879–84. doi:10.1016/j.biopsych.2010.05.013.

    Article  PubMed  CAS  Google Scholar 

  32. Tosun D, Schuff N, Mathis CA, Jagust W, Weiner MW, Alzheimer’s Disease Neuroimaging Initiative. Spatial patterns of brain amyloid-beta burden and atrophy rate associations in mild cognitive impairment. Brain 2011;134:1077–88. doi:10.1093/brain/awr044.

    Article  PubMed  Google Scholar 

  33. Villain N, Fouquet M, Baron JC, Mézenge F, Landeau B, de La Sayette V, et al. Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer’s disease. Brain 2010;133:3301–14. doi:10.1093/brain/awq203.

    Article  PubMed  Google Scholar 

  34. Chételat G, Desgranges B, Landeau B, Mézenge F, Poline JB, de la Sayette V, et al. Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer’s disease. Brain 2008;131:60–71. doi:10.1093/brain/awm288.

    Article  PubMed  Google Scholar 

  35. Gouw AA, Seewann A, Vrenken H, van der Flier WM, Rozemuller JM, Barkhof F, et al. Heterogeneity of white matter hyperintensities in Alzheimer’s disease: post-mortem quantitative MRI and neuropathology. Brain 2008;131:3286–98. doi:10.1093/brain/awn265.

    Article  PubMed  CAS  Google Scholar 

  36. Mosconi L, Pupi A, De Leon MJ. Brain glucose hypometabolism and oxidative stress in preclinical Alzheimer’s disease. Ann N Y Acad Sci 2008;1147:180–95. doi:10.1196/annals.1427.007.

    Article  PubMed  CAS  Google Scholar 

  37. Bai F, Zhang Z, Watson DR, Yu H, Shi Y, Yuan Y. Abnormal white matter independent of hippocampal atrophy in amnestic type mild cognitive impairment. Neurosci Lett 2009;462:147–51. doi:10.1016/j.neulet.2009.07.009.

    Article  PubMed  CAS  Google Scholar 

  38. Selkoe DJ. Alzheimer’s disease is a synaptic failure. Science 2002;298:789–91. doi:10.1126/science.1074069298/5594/789.

    Article  PubMed  CAS  Google Scholar 

  39. Wei W, Nguyen LN, Kessels HW, Hagiwara H, Sisodia S, Malinow R. Amyloid beta from axons and dendrites reduces local spine number and plasticity. Nat Neurosci 2010;13:190–6. doi:10.1038/nn.2476.

    Article  PubMed  CAS  Google Scholar 

  40. Ingelsson M, Fukumoto H, Newell KL, Growdon JH, Hedley-Whyte ET, Frosch MP, et al. Early Abeta accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 2004;62:925–31.

    Article  PubMed  CAS  Google Scholar 

Download references

Conflicts of interest

None.

Author information

Authors and Affiliations

  1. Department of Nuclear Medicine, Technische Universität München, Munich, Germany

    Stefan Förster, Behrooz H. Yousefi, Hans-Jürgen Wester, Elisabeth Klupp & Alexander Drzezga

  2. TUM-Neuroimaging Center (TUM-NIC), Technische Universität München, Munich, Germany

    Stefan Förster & Alexander Drzezga

  3. Department of Nuclear Medicine, Ludwig Maximilians Universität München, Munich, Germany

    Axel Rominger

  4. Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany

    Hans Förstl, Alexander Kurz & Timo Grimmer

  5. Klinik und Poliklinik für Nuklearmedizin, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Str. 22, 81675, Munich, Germany

    Stefan Förster

Authors
  1. Stefan Förster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Behrooz H. Yousefi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hans-Jürgen Wester
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elisabeth Klupp
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Axel Rominger
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hans Förstl
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alexander Kurz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Timo Grimmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alexander Drzezga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan Förster.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Förster, S., Yousefi, B.H., Wester, HJ. et al. Quantitative longitudinal interrelationships between brain metabolism and amyloid deposition during a 2-year follow-up in patients with early Alzheimer’s disease. Eur J Nucl Med Mol Imaging 39, 1927–1936 (2012). https://doi.org/10.1007/s00259-012-2230-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-012-2230-9

Keywords

Search

Navigation