Skip to main content
Springer Nature Link
Log in

Individual-level metabolic connectivity from dynamic [18F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard

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

Abstract

Purpose

This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [18F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer’s full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression.

Methods

We designed an index (Distance from Healthy Group, DfHG) based on the alteration of wi-MC in each patient (n = 44) compared to a healthy reference (from 57 healthy controls), to individually quantify metabolic connectivity abnormalities, resulting in an Impairment Map highlighting significantly compromised areas. We then assessed whether our measure of metabolic network alteration is associated with well-established markers of disease severity (tumor grade and volume, with and without edema). Subsequently, we investigated disruptions in wi-MC homotopic connectivity, assessing both affected and seemingly healthy tissue to deepen the pathology’s impact on neural communication. Finally, we compared network impairments with local metabolic alterations determined from SUVR, a validated diagnostic tool in clinical practice.

Results

Our framework revealed how gliomas cause extensive alterations in the topography of brain networks, even in structurally unaffected regions outside the lesion area, with a significant reduction in connectivity between contralateral homologous regions. High-grade gliomas have a stronger impact on brain networks, and edema plays a mediating role in global metabolic alterations. As compared to the conventional SUVR-based analysis, our approach offers a more holistic view of the disease burden in individual patients, providing interesting additional insights into glioma-related alterations.

Conclusion

Considering our results, individual PET connectivity estimates could hold significant clinical value, potentially allowing the identification of new prognostic factors and personalized treatment in gliomas or other focal pathologies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

References

  1. Sala A, Lizarraga A, Caminiti SP, et al. Brain connectomics: time for a molecular imaging perspective? Trends Cogn Sci. 2023;27(4):353–66. https://doi.org/10.1016/j.tics.2022.11.015.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Yakushev I, Drzezga A, Habeck C. Metabolic connectivity: methods and applications. Curr Opin Neurol. 2017;30(6):677–85. https://doi.org/10.1097/WCO.0000000000000494.

    Article  PubMed  Google Scholar 

  3. Veronese M, Moro L, Arcolin M, et al. Covariance statistics and network analysis of brain PET imaging studies. Sci Rep. 2019;9(1). https://doi.org/10.1038/s41598-019-39005-8.

  4. Jamadar SD, Ward PGD, Liang EX, et al. Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb Cortex. 2021;31(6):2855–67. https://doi.org/10.1093/cercor/bhaa393.

    Article  PubMed  Google Scholar 

  5. Sun T, Wang Z, Wu Y, et al. Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging. Eur J Nucl Med Mol Imaging. 2022;49(8):2994–3004. https://doi.org/10.1007/s00259-022-05832-7.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wang M, Jiang J, Yan Z, et al. Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia. Eur J Nucl Med Mol Imaging. 2020;47(12):2753–64. https://doi.org/10.1007/s00259-020-04814-x.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Huang SY, Hsu JL, Lin KJ, et al. A Novel Individual Metabolic Brain Network for 18F-FDG PET imaging. Front Neurosci. 2020;14(May):1–11. https://doi.org/10.3389/fnins.2020.00344.

    Article  Google Scholar 

  8. Volpi T, Vallini G, Silvestri E, et al. A new framework for metabolic connectivity mapping using bolus [ 18 F]FDG PET and kinetic modelling. J Cereb Blood Flow Metab. 2022;1–21. https://doi.org/10.1177/0271678X231184365.

  9. Liptrot M, Adams KH, Martiny L, et al. Cluster analysis in kinetic modelling of the brain: a noninvasive alternative to arterial sampling. NeuroImage. 2004;21(2):483–93. https://doi.org/10.1016/j.neuroimage.2003.09.058.

    Article  PubMed  Google Scholar 

  10. Bertoldo A, Rizzo G, Veronese M. Deriving physiological information from PET images: From SUV to compartmental modelling [Internet]. Vol. 2, Clinical and Translational Imaging. Springer-Verlag Italia s.r.l.; 2014. 239–51. https://doi.org/10.1007/s40336-014-0067-x.

  11. Mancusi R, Monje M. The neuroscience of cancer. Nature. 2023;618(7965):467–79. https://doi.org/10.1038/s41586-023-05968-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Daniel AGS, Hacker CD, Lee JJ, et al. Homotopic functional connectivity disruptions in glioma patients are associated with tumor malignancy and overall survival. Neuro-Oncology Adv. 2021;3(1):1–10. https://doi.org/10.1093/noajnl/vdab176.

    Article  Google Scholar 

  13. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–20. https://doi.org/10.1007/s00401-016-1545-1.

    Article  PubMed  Google Scholar 

  14. Goyal MS, Blazey T, Metcalf NV, et al. Brain aerobic glycolysis and resilience in Alzheimer disease. Proc Natl Acad Sci U S A. 2023;120(7):1–8. https://doi.org/10.1073/pnas.2212256120.

    Article  CAS  Google Scholar 

  15. Hammers A, Allom R, Koepp MJ, et al. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp. 2003;19(4):224–47. https://doi.org/10.1002/hbm.10123.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yan X, Kong R, Xue A, et al. Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity. NeuroImage. 2023;273(October 2022):120010. https://doi.org/10.1016/j.neuroimage.2023.120010.

    Article  CAS  PubMed  Google Scholar 

  17. Avants BB, Tustison NJ, Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage. 2011;54(3):2033–44. https://doi.org/10.1016/j.neuroimage.2010.09.025.

    Article  PubMed  Google Scholar 

  18. Silvestri E, Moretto M, Facchini S, et al. Widespread cortical functional disconnection in gliomas: an individual network mapping approach. Brain Commun. 2022;4(2):1–14. https://doi.org/10.1093/braincomms/fcac082.

    Article  Google Scholar 

  19. Andersen SM, Rapcsak SZ, Beeson PM. Cost function masking during normalization of brains with focal lesions: still a necessity? NeuroImage. 2010;53(1):78–84. https://doi.org/10.1016/j.neuroimage.2010.06.003.

    Article  PubMed  Google Scholar 

  20. Feng X, Deistung A, Dwyer MG, et al. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM). Magn Reson Imaging. 2017;39:110–22. https://doi.org/10.1016/j.mri.2017.02.002.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Deng S, Franklin CG, O’Boyle M, et al. Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults. NeuroImage. 2022;250(January):118923. https://doi.org/10.1016/j.neuroimage.2022.118923.

    Article  CAS  PubMed  Google Scholar 

  22. Nugent S, Croteau E, Potvin O, et al. Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and Alzheimer’s disease. Sci Rep. 2020;10(1):1–8. https://doi.org/10.1038/s41598-020-65957-3.

    Article  CAS  Google Scholar 

  23. Miller KD, Ostrom QT, Kruchko C, et al. Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin. 2021;71(5):381–406. https://doi.org/10.3322/caac.21693.

    Article  PubMed  Google Scholar 

  24. Daniel AGS, Park KY, Roland JL, et al. Functional connectivity within glioblastoma impacts overall survival. Neuro Oncol. 2021;23(3):412–21. https://doi.org/10.1093/neuonc/noaa189.

    Article  PubMed  Google Scholar 

  25. Horwitz B, Duara R, Rapoport SI. Intercorrelations of glucosemetabolic rates between brain regions: application to healthy males in a state of reduced sensory input. J Cereb Blood Flow Metab. 1984;4(4):484–99. https://doi.org/10.1038/jcbfm.1984.73.

    Article  CAS  PubMed  Google Scholar 

  26. Feeney DM, Baron JC, Diaschisis. Stroke. 1986;17(5):817–30. https://doi.org/10.1161/01.STR.17.5.817.

  27. Jung E, Alfonso J, Monyer H, et al. Neuronal signatures in cancer. Int J Cancer. 2020;147(12):3281–91. https://doi.org/10.1002/ijc.33138.

    Article  CAS  PubMed  Google Scholar 

  28. Lv K, Hu Y, Cao X, et al. Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study. Neuroradiology. 2024;66(5):775–84. https://doi.org/10.1007/s00234-024-03300-7.

    Article  PubMed  Google Scholar 

  29. Lin ZX. Glioma-related edema: New insight into molecular mechanisms and their clinical implications. Chin J Cancer. 2013;32(1):49–52. https://doi.org/10.5732/cjc.012.10242.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Karavasilis E, Christidi F, Velonakis G, et al. Ipsilateral and contralateral cerebro-cerebellar white matter connections: a diffusion tensor imaging study in healthy adults. J Neuroradiol. 2019;46(1):52–60. https://doi.org/10.1016/j.neurad.2018.07.004.

    Article  PubMed  Google Scholar 

  31. Buckner RL, Krienen FM, Castellanos A, et al. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(5):2322–45. https://doi.org/10.1152/jn.00339.2011.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Nenning KH, Furtner J, Kiesel B, et al. Distributed changes of the functional connectome in patients with glioblastoma. Sci Rep. 2020;10(1):1–11. https://doi.org/10.1038/s41598-020-74726-1.

    Article  CAS  Google Scholar 

  33. Baumann C, Tichy J, Schaefer JH, et al. Delay in diagnosing patients with right-sided glioblastoma induced by hemispheric-specific clinical presentation. J Neurooncol. 2020;146(1):63–9. https://doi.org/10.1007/s11060-019-03335-4.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this research was provided by the McDonnell Center for Systems Neuroscience and the NIH/ NIA R01AG053503 (AGV) and R01AG057536 (AGV, MSG). Some of the MRI sequences used were obtained from the Massachusetts General Hospital.

Author information

Author notes

  1. Erica Silvestri and Tommaso Volpi contributed equally to this work.

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Padova, Italy

    Giulia Vallini, Erica Silvestri & Alessandra Bertoldo

  2. Padova Neuroscience Center, University of Padova, Padova, Italy

    Tommaso Volpi, Diego Cecchin, Maurizio Corbetta & Alessandra Bertoldo

  3. Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA

    Tommaso Volpi

  4. Neuroimaging Laboratories, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA

    John J. Lee, Andrei G. Vlassenko & Manu S. Goyal

  5. Department of Medicine, Unit of Nuclear Medicine, University of Padova, Padova, Italy

    Diego Cecchin

  6. Department of Neuroscience, University of Padova, Padova, Italy

    Maurizio Corbetta

Authors
  1. Giulia Vallini
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Erica Silvestri
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tommaso Volpi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. John J. Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Andrei G. Vlassenko
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Manu S. Goyal
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Diego Cecchin
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Maurizio Corbetta
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Alessandra Bertoldo
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Andrei G. Vlassenko and Diego Cecchin collected the data. Giulia Vallini and Alessandra Bertoldo designed the research. Giulia Vallini, Erica Silvestri and Tommaso Volpi analyzed the data. Giulia Vallini, Erica Silvestri, Tommaso Volpi, John J. Lee, Andrei G. Vlassenko, Manu S. Goyal, Diego Cecchin, Maurizio Corbetta and Alessandra Bertoldo interpreted the results. Giulia Vallini wrote the manuscript. All authors revised the manuscript.

Corresponding author

Correspondence to Alessandra Bertoldo.

Ethics declarations

Ethical approval

All assessments and imaging procedures were approved by Human Research Protection Office and Radioactive Drug Research Committee at Washington University in St. Louis (healthy controls). Concerning glioma patients, the protocol has been approved by the local Ethics Committee of the University Hospital of Padova. All procedures performed in studies were conducted in accordance with the 1964 Declaration of Helsinki and its subsequent amendments.

Consent to participate

Informed written consent was obtained from all individual participants included in the study.

Consent to publish

Participants signed informed consent regarding publishing their data.

Competing interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vallini, G., Silvestri, E., Volpi, T. et al. Individual-level metabolic connectivity from dynamic [18F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard. Eur J Nucl Med Mol Imaging 52, 836–850 (2025). https://doi.org/10.1007/s00259-024-06956-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-024-06956-8

Keywords