Skip to main content
SpringerLink
Log in

Quantitative SPM Analysis Involving an Adaptive Template May Be Easily Applied to [18F]FDG PET Images of the Rat Brain

  • Research Article
  • Published:
Molecular Imaging and Biology Aims and scope Submit manuscript

Abstract

Purpose

The Statistical Parametric Mapping (SPM) software is frequently used for the quantitative analysis of patients’ brain images obtained from 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]FDG PET). However, its adaptation to small animals is difficult, particularly for the initial step of spatial normalization which requires a specific brain anatomical template. This study was aimed at determining whether SPM analysis can be applied to rat, and more specifically to the lithium-pilocarpine model of epilepsy, by using an adaptive template. This template developed for PET clinical imaging is constructed from a block matching algorithm.

Procedures

SPM analysis of brain [18F]FDG PET images from Sprague-Dawley rats was used with the block matching (BM) adaptive template for the detection of brain abnormalities (1) artificially inserted within the initially normal brain images of 10 rats (50 % decrease in signal intensity within 40 spheres of 0.5 to 1.0 mm in diameter) and (2) occurring at 4 h (n = 16), 48 h (n = 15), and 8 days (n = 13) after lithium-pilocarpine treatment.

Results

Concordant positive clusters were documented for all inserted abnormalities, whereas no aberrant clusters were documented in remote brain areas. Positive clusters were also detected on sites known to be involved in the epileptogenesis process of the lithium-pilocarpine model (piriform and entorhinal cortex, hippocampus), with the expected time-specific changes involving an early hypermetabolism followed by a severe hypometabolism and a subsequent partial recovery.

Conclusion

A quantitative SPM analysis of brain [18F]FDG PET images may be applied to the monitoring of rat brain function when using an adaptive BM template.

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.

Similar content being viewed by others

References

  1. Goffin K, Dedeurwaerdere S, Van Laere K et al (2008) Neuronuclear assessment of patients with epilepsy. Semin Nucl Med 38:227–239

    Article  PubMed  Google Scholar 

  2. Willmann O, Wennberg R, May T, Woermann FG et al (2007) The contribution of 18F-FDG PET in preoperative epilepsy surgery evaluation for patients with temporal lobe epilepsy: a meta-analysis. Seizure 16:509–520

    Article  CAS  PubMed  Google Scholar 

  3. Verger A, Yagdigul Y, Van Der Gucht A et al (2016) Temporal epilepsy lesions may be detected by the voxel-based quantitative analysis of brain FDG-PET images using an original block-matching normalization software. Ann Nucl Med 30:272–278

    Article  CAS  PubMed  Google Scholar 

  4. Verger A, Hossu G, Kearney-Schwartz A et al (2016) Grey-matter metabolism in relation with white-matter lesions in older hypertensive patients with subjective memory complaints: a pilot voxel-based analysis study. Cerebrovasc Dis 42:106–109

    Article  CAS  PubMed  Google Scholar 

  5. Van Der Gucht A, Verger A, Guedj E et al (2015) Age-related changes in FDG brain uptake are more accurately assessed when applying an adaptive template to the SPM method of voxel-based quantitative analysis. Ann Nucl Med 29:921–928

    Article  PubMed  Google Scholar 

  6. Casteels C, Vermaelen P, Nuyts J et al (2006) Construction and evaluation of multitracer small-animal PET probabilistic atlases for voxel-based functional mapping of the rat brain. J Nucl Med 47:1858–1866

    PubMed  Google Scholar 

  7. Nie B, Liu H, Chen K et al (2014) A statistical parametric mapping toolbox used for voxel-wise analysis of FDG-PET images of rat brain. PLoS One 9:e108295

    Article  PubMed  PubMed Central  Google Scholar 

  8. Buiter HJ, van Velden FH, Leysen JE et al (2012) Reproducible analysis of rat brain PET studies using an additional [18F]NaF scan and an MR-based ROI template. Int J Mol Imaging 2012:580717

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lee EM, Park GY, Im KC et al (2015) Changes in glucose metabolism and metabolites during the epileptogenic process in the lithium-pilocarpine model of epilepsy. Epilepsia 53:860–869

    Article  Google Scholar 

  10. Goffin K, Van Paesschen W, Dupont P et al (2009) Longitudinal microPET imaging of brain glucose metabolism in rat lithium-pilocarpine model of epilepsy. Exp Neurol 217:205–209

    Article  CAS  PubMed  Google Scholar 

  11. Jupp B, Williams J, Binns D et al (2012) Hypometabolism precedes limbic atrophy and spontaneous recurrent seizures in a rat model of TLE. Epilepsia 53:1233–1244

    Article  PubMed  Google Scholar 

  12. Guo Y, Gao F, Wang S et al (2009) In vivo mapping of temporospatial changes in glucose utilization in rat brain during epileptogenesis: an 18F-fluorodeoxyglucose-small animal positron emission tomography study. Neuroscience 162:972–979

    Article  CAS  PubMed  Google Scholar 

  13. Person C, Louis-Dorr V, Poussier S et al (2012) Voxel-based quantitative analysis of brain images from 18F-FDG PET with a block-matching algorithm for spatial normalization. Clin Nucl Med 37:268–273

    Article  PubMed  Google Scholar 

  14. Dubé C, Boyet S, Marescaux C et al (2001) Relationship between neuronal loss and interictal glucose metabolism during the chronic phase of the lithium-pilocarpine model of epilepsy in the immature and adult rat. Exp Neurol 167:227–241

    Article  PubMed  Google Scholar 

  15. Curia G, Longo D, Biagini G et al (2008) The pilocarpine model of temporal lobe epilepsy. J Neurosci Methods 172:143–157

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Good CD, Johnsrude IS, Ashburner J et al (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14:21–36

    Article  CAS  PubMed  Google Scholar 

  17. Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38:95–113

    Article  PubMed  Google Scholar 

  18. Bieth M, Lombaert H, Reader AJ et al (2013) Atlas construction for dynamic (4D) PET using diffeomorphic transformations. Med Image Comput Comput Assist Interv 16:35–42

    PubMed  Google Scholar 

  19. Martino ME, de Villoria JG, Lacalle-Aurioles M et al (2013) Comparison of different methods of spatial normalization of FDG-PET brain images in the voxel-wise analysis of MCI patients and controls. Ann Nucl Med 27:600–609

    Article  PubMed  Google Scholar 

  20. Della Rosa PA, Cerami C, Gallivanone F et al (2014) A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics 12:575–593

    Article  PubMed  Google Scholar 

  21. Da Silva Fernandes MJ, Dubé C, Boyet S et al (1999) Correlation between hypermetabolism and neuronal damage during status epilepticus induced by lithium and pilocarpine in immature and adult rats. J Cereb Blood Flow Metab 19:195–209

    Article  CAS  Google Scholar 

  22. André V, Dubé C, François J et al (2007) Pathogenesis and pharmacology of epilepsy in the lithium-pilocarpine model. Epilepsia 48:41–47

    Article  PubMed  Google Scholar 

  23. Welch A, Mingarelli M, Riedel G, Platt B (2013) Mapping changes in mouse brain metabolism with PET/CT. J Nucl Med 54:1946–1953

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to thank M. Bernard Chalon for his help in software development, M. Pierre Pothier (Les Services PM-SYS, Sherbrooke, Canada) for critical review of the manuscript, and the team Inria Sophia Antipolis – Méditerranée “Nef” for the providing of the BM software and of the computation cluster.

Author information

Authors and Affiliations

  1. NANCYCLOTEP-Experimental Imaging Platform, F-5400, Nancy, France

    Sylvain Poussier, Fatiha Maskali, Antoine Verger, Henri Boutley, Gilles Karcher & Pierre-Yves Marie

  2. INSERM, IADI U947 and Lorraine University, F-5400, Nancy, France

    Sylvain Poussier & Antoine Verger

  3. Department of Nuclear Medicine, University Hospital, F-5400, Nancy, France

    Fatiha Maskali, Antoine Verger, Gilles Karcher & Pierre-Yves Marie

  4. Department of Neurology, University Hospital, F-5400, Nancy, France

    Gaelle Vexiau & Emmanuel Raffo

  5. INSERM U1116 and Lorraine University, F-5400, Nancy, France

    Gilles Karcher & Pierre-Yves Marie

Authors
  1. Sylvain Poussier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatiha Maskali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gaelle Vexiau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antoine Verger
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Henri Boutley
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gilles Karcher
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Emmanuel Raffo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pierre-Yves Marie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sylvain Poussier.

Ethics declarations

Funding

This study was funded by the Lorraine University through a dedicated Nancyclotep call for proposal and by the French National Health Ministry (“Yearly research funding for residents”).

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. This study was approved by the Lorraine Ethics Committee on Animal Experimentation (CELMEA LORRAIN N°66, Study Number 2015090117136919 (APAFIS # 1129)).

Additional information

Sylvain Poussier and Fatiha Maskali contributed equally to this article

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poussier, S., Maskali, F., Vexiau, G. et al. Quantitative SPM Analysis Involving an Adaptive Template May Be Easily Applied to [18F]FDG PET Images of the Rat Brain. Mol Imaging Biol 19, 731–735 (2017). https://doi.org/10.1007/s11307-016-1043-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11307-016-1043-9

Key words

Search

Navigation