Molecular Imaging and Biology

, Volume 21, Issue 1, pp 140–148 | Cite as

Spatial Patterns of Hypometabolism and Amyloid Deposition in Variants of Alzheimer’s Disease Corresponding to Brain Networks: a Prospective Cohort Study

  • Ying Wang
  • Zhihong Shi
  • Nan Zhang
  • Li Cai
  • Yansheng Li
  • Hailei Yang
  • Shaobo Yao
  • Xiling Xing
  • Yong Ji
  • Shuo GaoEmail author
Research Article



To identify the most vulnerable network among typical and three variants of Alzheimer’s disease (AD) and to link amyloid-β (Aβ) deposition and downstream network dysfunction.


In this study, 38 typical AD, 11 frontal variants, 8 logopenic variants, 6 posterior variants, and 20 normal controls were enrolled. 2-(4′-[11C] Methylaminophenyl)-6-hydroxybenzothiazole ([11C]PIB) and 2-deoxy-2-[18]fluoro-d-glucose ([18F]FDG) positron emission tomography (PET) imaging were performed. Voxel-wise statistical analysis was used for [18F]FDG analysis, whereas two-sample t test was performed between each AD group and control group. Moreover, the goodness of fit (GOF) of t-maps with brain functional network templates was assessed, and the most vulnerable network in each phenotypic of AD was chosen as volume of interests (VOIs). [11C]PIB binding potential (BPND) of VOIs were generated by using PMOD software. In addition, statistical analysis of BPND among four types of AD in each specific network was calculated by SPSS software.


The hypometabolism patterns indicated that in typical and frontal variants of AD, the most vulnerable network was the left executive control network (GOF score = 4.3, 5.0). For the logopenic variant, the highest GOF score (1.9) belonged to the auditory network. For the posterior variant, the higher visual network was the most vulnerable (GOF score = 6.0). The [11C]PIB BPND showed that there were no significant differences (p > 0.05) among AD groups within the specific networks.


The phenotypic diversity of AD correlates with specific functional network failure; however, Aβ plaques do not associate with specific network vulnerability.

Key words

Alzheimer’s disease Amyloid-β [18F]FDG [11C]PIB Positron-emission tomography 



The study was supported by the National Natural Science Foundation of China (Grant NO. 81571057 and 81501035), Tianjin Science and Technology Project (Grant NO. 16ZXMJSY00010).

Compliance with Ethical Standards

This study was approved by the Ethics Committee of Tianjin Medical University General Hospital (IRB2014–071-01).

Conflict of Interest Statement

The authors declare that they have no conflict of interest.


  1. 1.
    Kelley BJ, Petersen RC (2007) Alzheimer’s disease and mild cognitive impairment. Neurol Clin 25:577–609CrossRefGoogle Scholar
  2. 2.
    Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82:239–259CrossRefGoogle Scholar
  3. 3.
    Thal DR, Rub U, Orantes M, Braak H (2002) Phases of a beta-deposition in the human brain and its relevance for the development of AD. Neurology 58:1791–1800CrossRefGoogle Scholar
  4. 4.
    Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol 42:85–94CrossRefGoogle Scholar
  5. 5.
    Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, DeKosky ST, Gauthier S, Selkoe D, Bateman R, Cappa S, Crutch S, Engelborghs S, Frisoni GB, Fox NC, Galasko D, Habert MO, Jicha GA, Nordberg A, Pasquier F, Rabinovici G, Robert P, Rowe C, Salloway S, Sarazin M, Epelbaum S, de Souza LC, Vellas B, Visser PJ, Schneider L, Stern Y, Scheltens P, Cummings JL (2014) Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 13:614–629CrossRefGoogle Scholar
  6. 6.
    Lehmann M, Ghosh PM, Madison C, Laforce R Jr, Corbetta-Rastelli C, Weiner MW, Greicius MD, Seeley WW, Gorno-Tempini ML, Rosen HJ, Miller BL, Jagust WJ, Rabinovici GD (2013) Diverging patterns of amyloid deposition and hypometabolism in clinical variants of probable Alzheimer’s disease. Brain 136:844–858CrossRefGoogle Scholar
  7. 7.
    Jones DT, Knopman DS, Gunter JL, Graff-Radford J, Vemuri P, Boeve BF, Petersen RC, Weiner MW, Jack CR Jr, Alzheimer’s Disease Neuroimaging Initiative (2016) Cascading network failure across the Alzheimer’s disease spectrum. Brain 139:547–562CrossRefGoogle Scholar
  8. 8.
    Pievani M, de Haan W, Wu T, Seeley WW, Frisoni GB (2011) Functional network disruption in the degenerative dementias. Lancet Neurol 10:829–843CrossRefGoogle Scholar
  9. 9.
    Zhou J, Greicius MD, Gennatas ED, Growdon ME, Jang JY, Rabinovici GD, Kramer JH, Weiner M, Miller BL, Seeley WW (2010) Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain 133:1352–1367CrossRefGoogle Scholar
  10. 10.
    Raj A, Kuceyeski A, Weiner MA (2012) A network diffusion model of disease progression in dementia. Neuron 73:1204–1215CrossRefGoogle Scholar
  11. 11.
    Buckner RL, Sepulcre J, Talukdar T et al (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability and relation to Alzheimer’s disease. Neurobiol Dis 29:1860–1873Google Scholar
  12. 12.
    Warren JD, Fletcher PD, Golden HL (2012) The paradox of syndromic diversity in Alzheimer disease. Nat Rev Neurol 8:451–464CrossRefGoogle Scholar
  13. 13.
    Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD (2012) Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex 22:158–165CrossRefGoogle Scholar
  14. 14.
    Wu Y, Carson R (2002) Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging. J Cereb Blood Flow Metab 22:1440–1452CrossRefGoogle Scholar
  15. 15.
    Yaqub M, Tolboom N, Boellaard R, van Berckel BNM, van Tilburg EW, Luurtsema G, Scheltens P, Lammertsma AA (2008) Simplified parametric methods for [11C]PIB studies. NeuroImage 42:76–86CrossRefGoogle Scholar
  16. 16.
    Yamaguchi H, Hirai S, Morimatsu M, Shoji M, Nakazato Y (1989) Diffuse type of senile plaques in the cerebellum of Alzheimer-type dementia. Acta Neuropathol 77:314–319CrossRefGoogle Scholar
  17. 17.
    Woodward MC, Rowe CC, Jones G, Villemagne VL, Varos TA (2015) Differentiating the frontal presentation of Alzheimer’s disease with FDG-PET. J Alzheimers Dis 44:233–242CrossRefGoogle Scholar
  18. 18.
    Matías-Guiu JA, Cabrera-Martín MN, Pérez-Castejón MJ, Moreno-Ramos T, Rodríguez-Rey C, García-Ramos R, Ortega-Candil A, Fernandez-Matarrubia M, Oreja-Guevara C, Matías-Guiu J, Carreras JL (2015) Visual and statistical analysis of 18F-FDG PET in primary progressive aphasia. Eur J Nucl Med Mol Imaging 42:916–927CrossRefGoogle Scholar
  19. 19.
    Leyton CE, Hodges JR, Piguet O, Ballard KJ (2017) Common and divergent neural correlates of anomia in amnestic and logopenic presentations of Alzheimer’s disease. Cortex 86:45–54CrossRefGoogle Scholar
  20. 20.
    Arnemann KL, Stöber F, Narayan S et al (2017) Metabolic brain networks in aging and preclinical Alzheimer’s disease. Neuroimage Clin 17:987–999CrossRefGoogle Scholar
  21. 21.
    Rabinovici GD, Rosen HJ, Alkalay A, Kornak J, Furst AJ, Agarwal N, Mormino EC, O'Neil JP, Janabi M, Karydas A, Growdon ME, Jang JY, Huang EJ, DeArmond SJ, Trojanowski JQ, Grinberg LT, Gorno-Tempini ML, Seeley WW, Miller BL, Jagust WJ (2011) Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD. Neurology 77:2034–2042CrossRefGoogle Scholar
  22. 22.
    Beach TG, Monsell SE, Phillips LE, Kukull W (2012) Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010. J Neuropathol Exp Neurol 71:266–273CrossRefGoogle Scholar
  23. 23.
    Gorno-Tempini ML, Brambati SM, Ginex V, Ogar J, Dronkers NF, Marcone A, Perani D, Garibotto V, Cappa SF, Miller BL (2008) The logopenic/phonological variant of primary progressive aphasia. Neurology 71:1227–1234CrossRefGoogle Scholar
  24. 24.
    Gorno-Tempini ML, Dronkers NF, Rankin KP, Ogar JM, Phengrasamy L, Rosen HJ, Johnson JK, Weiner MW, Miller BL (2004) Cognition and anatomy in three variants of primary progressive aphasia. Ann Neurol 55:335–346CrossRefGoogle Scholar
  25. 25.
    Foxe D, Leyton CE, Hodges JR, Burrell JR, Irish M, Piguet O (2016) The neural correlates of auditory and visuospatial span in logopenic progressive aphasia and Alzheimer’s disease. Cortex 83:39–50CrossRefGoogle Scholar
  26. 26.
    Mesulam M, Wicklund A, Johnson N, Rogalski E, Léger GC, Rademaker A, Weintraub S, Bigio EH (2008) Alzheimer and frontotemporal pathology in subsets of primary progressive aphasia. Ann Neurol 63:709–719CrossRefGoogle Scholar
  27. 27.
    Rosenbloom MH, Alkalay A, Agarwal N, Baker SL, O'Neil JP, Janabi M, Yen IV, Growdon M, Jang J, Madison C, Mormino EC, Rosen HJ, Gorno-Tempini ML, Weiner MW, Miller BL, Jagust WJ, Rabinovici GD (2011) Distinct clinical and metabolic deficits in PCA and AD are not related to amyloid distribution. Neurology 76:1789–1796CrossRefGoogle Scholar
  28. 28.
    Jack CR Jr, Knopman DS, Jagust WJ et al (2013) Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 12:207–216CrossRefGoogle Scholar
  29. 29.
    Ossenkoppele R, Schonhaut DR, Baker SL, O'Neil JP, Janabi M, Ghosh PM, Santos M, Miller ZA, Bettcher BM, Gorno-Tempini ML, Miller BL, Jagust WJ, Rabinovici GD (2015) Tau, amyloid, and hypometabolism in a patient with posterior cortical atrophy. Ann Neurol 77:338–342CrossRefGoogle Scholar
  30. 30.
    Rapoport SI, Horwitz B, Grady CL et al (1991) Abnormal brain glucose metabolism in Alzheimer’s disease, as measured by position emission tomography. Adv Exp Med Biol 29:231–248CrossRefGoogle Scholar
  31. 31.
    Josephs KA (2017) Current understanding of neurodegenerative diseases associated with the protein tau. Mayo Clin Proc 92:1291–1303CrossRefGoogle Scholar
  32. 32.
    Dronse J, Fliessbach K, Bischof GN, von Reutern B, Faber J, Hammes J, Kuhnert G, Neumaier B, Onur OA, Kukolja J, van Eimeren T, Jessen F, Fink GR, Klockgether T, Drzezga A (2017) In vivo patterns of tau pathology, amyloid-β burden, and neuronal dysfunction in clinical variants of Alzheimer’s disease. J Alzheimers Dis 55:465–471CrossRefGoogle Scholar
  33. 33.
    Gutierrez A, Vitorica J (2018) Toward a new concept of Alzheimer’s disease models: a perspective from neuroinflammation. J Alzheimers Dis:1–10Google Scholar
  34. 34.
    Yamane T, Ishii K, Sakata M et al (2017) Inter-rater variability of visual interpretation and comparison with quantitative evaluation of 11C-PiB PET amyloid images of the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) multicenter study. Eur J Nucl Med Mol Imaging 44:850–857CrossRefGoogle Scholar

Copyright information

© World Molecular Imaging Society 2018

Authors and Affiliations

  • Ying Wang
    • 1
  • Zhihong Shi
    • 2
  • Nan Zhang
    • 3
  • Li Cai
    • 1
  • Yansheng Li
    • 1
  • Hailei Yang
    • 1
  • Shaobo Yao
    • 1
  • Xiling Xing
    • 1
  • Yong Ji
    • 2
  • Shuo Gao
    • 1
    Email author
  1. 1.Department of PET-CT DiagnosticTianjin Medical University General HospitalTianjinChina
  2. 2.Tianjin Key Laboratory of Cerebrovascular and Neurodegenerative DiseasesTianjin Huanhu HospitalTianjinChina
  3. 3.Department of NeurologyTianjin Medical University General Hospital, Tianjin Neurological InstituteTianjinChina

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