Skip to main content

Advertisement

SpringerLink
Log in

Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps

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

Abstract

Purpose

An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level.

Methods

Selection of HC images was based on visual rating, after Cook’s distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB.

Results

Two-step Cook’s distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes.

Conclusions

The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings.

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. Kato T, Inui Y, Nakamura A, Ito K. Brain fluorodeoxyglucose (FDG) PET in dementia. Ageing Res Rev Elsevier BV. 2016;30:73–84.

    Article  Google Scholar 

  2. Iaccarino L, Sala A, Caminiti SP, Perani D. The emerging role of PET imaging in dementia. F1000Research. Faculty of 1000 Ltd.; 2017;6.

  3. Perani D, Caminiti SP, Carli G, Tondo G. PET neuroimaging in dementia conditions. PET SPECT Neurol Springer; 2020;211–82.

  4. Perani D, Schillaci O, Padovani A, Nobili F, Leonardo I, Anthony P, et al. A survey of FDG-and amyloid-PET imaging in dementia and GRADE analysis. Biomed Res Int Hindawi; 2014;2014.

  5. McKeith IG, Dickson DW, Lowe J, Emre M, O’Brien JT, Feldman H, et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium. Neurology. 2005;65:1863–72.

    Article  CAS  Google Scholar 

  6. Gorno-Tempini M, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, et al. Classification of primary progressive aphasia and its variants. Neurology. 2011;76:1006–14.

    Article  Google Scholar 

  7. Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B, et al. Criteria for the diagnosis of corticobasal degeneration. Neurology. 2013;80:496–503.

    Article  Google Scholar 

  8. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. Elsevier Ltd; 2011;7:280–92.

  9. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement Elsevier Ltd; 2011;7:270–9.

  10. Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain. 2011;134:2456–77.

    Article  Google Scholar 

  11. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement Elsevier Ltd; 2011;7:263–9.

  12. Smailagic N, Vacante M, Hyde C, Martin S, Ukoumunne O, Sachpekidis C. 18 F-FDG PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. John Wiley & Sons, Ltd; 2015

  13. Morbelli S, Garibotto V, Van De Giessen E, Arbizu J, Chételat G, Drezgza A, et al. A Cochrane review on brain [18 F] FDG PET in dementia: limitations and future perspectives. Springer; 2015.

  14. Waxman AD, Herholz K, Lewis DH, Herscovitch P, Minoshima S, Mountz JM, et al. Society of Nuclear Medicine procedure guideline for FDG PET brain imaging. Soc Nucl Med (Version 10). 2009

  15. Chen K, Ayutyanont N, Langbaum JBS, Fleisher AS, Reschke C, Lee W, et al. Characterizing Alzheimer’s disease using a hypometabolic convergence index. Neuroimage Elsevier; 2011;56:52–60.

  16. Landau S, Jagust W. UC Berkeley FDG MetaROI methods. Alzheimer’s Dis Neuroimaging Initiat 2011;

  17. López-González FJ, Silva-Rodríguez J, Paredes-Pacheco J, Niñerola-Baizán A, Efthimiou N, Martín-Martín C, et al. Intensity normalization methods in brain FDG-PET quantification. Neuroimage. Elsevier; 2020;222:117229.

  18. Nobili F, Festari C, Altomare D, Agosta F, Orini S, Van Laere K, et al. Automated assessment of FDG-PET for differential diagnosis in patients with neurodegenerative disorders. Eur J Nucl Med Mol Imaging Springer. 2018;45:1557–66.

    Article  CAS  Google Scholar 

  19. Mosconi L, Tsui WH, Pupi A, De Santi S, Drzezga A, Minoshima S, et al. 18F-FDG PET database of longitudinally confirmed healthy elderly individuals improves detection of mild cognitive impairment and Alzheimer’s disease. J Nucl Med Soc Nuclear Med. 2007;48:1129–34.

    Article  Google Scholar 

  20. Perani D, Della Rosa PA, Cerami C, Gallivanone F, Fallanca F, Giovanna E, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. NeuroImage Clin. Elsevier B.V.; 2014;6:445–54.

  21. Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al. A standardized [18 F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics Springer. 2014;12:575–93.

    Article  Google Scholar 

  22. Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics. Springer; 2014;12:575–93.

  23. Caminiti SP, Alongi P, Majno L, Volontè MA, Cerami C, Gianolli L, et al. Evaluation of an optimized [18F] fluoro-deoxy-glucose positron emission tomography voxel-wise method to early support differential diagnosis in atypical Parkinsonian disorders. Eur J Neurol Wiley Online Library; 2017;24:687-e26.

  24. Caminiti SP, Sala A, Iaccarino L, Beretta L, Pilotto A, Gianolli L, et al. Brain glucose metabolism in Lewy body dementia: implications for diagnostic criteria. Alzheimers Res Ther 2019;11.

  25. Iaccarino L, Chiotis K, Alongi P, Almkvist O, Wall A, Cerami C, et al. A cross-validation of FDG-and amyloid-PET biomarkers in mild cognitive impairment for the risk prediction to dementia due to Alzheimer’s disease in a clinical setting. J Alzheimer’s Dis IOS Press; 2017;59:603–14.

  26. Cerami C, Dodich A, Greco L, Iannaccone S, Magnani G, Marcone A, et al. The role of single-subject brain metabolic patterns in the early differential diagnosis of primary progressive aphasias and in prediction of progression to dementia. J Alzheimer’s Dis. IOS Press; 2017;55:183–97.

  27. Sala A, Caprioglio C, Santangelo R, Vanoli EG, Iannaccone S, Magnani G, et al. Brain metabolic signatures across the Alzheimer’s disease spectrum. Eur J Nucl Med Mol Imaging. Springer; 2020;47:256–269.

  28. Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Någren K, et al. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging. 2009;36:2103–10.

    Article  Google Scholar 

  29. Jian Y, Planeta B, Carson RE. Evaluation of bias and variance in low-count OSEM list mode reconstruction. Phys Med Biol. IOP Publishing; 2014;60:15.

  30. Buchert R, Wilke F, Chakrabarti B, Martin B, Brenner W, Mester J, et al. Adjusted scaling of FDG positron emission tomography images for statistical evaluation in patients with suspected Alzheimer’s disease. J Neuroimaging Wiley Online Library; 2005;15:348–55.

  31. Nichols TE. Multiple testing corrections, nonparametric methods, and random field theory. Neuroimage Elsevier. 2012;62:811–5.

    Article  Google Scholar 

  32. Presotto L, Ballarini T, Caminiti SP, Bettinardi V, Gianolli L, Perani D. Validation of 18F–FDG-PET single-subject optimized SPM procedure with different PET scanners. Neuroinform. 2017;15.

  33. Rahmim A, Qi J, Sossi V. Resolution modeling in PET imaging: theory, practice, benefits, and pitfalls. Med Phys Wiley Online Library; 2013;40.

  34. Kaalep A, Sera T, Rijnsdorp S, Yaqub M, Talsma A, Lodge MA, et al. Feasibility of state of the art PET/CT systems performance harmonisation. Eur J Nucl Med Mol Imaging. Springer; 2018;45:1344–61.

  35. Cerami C, Dodich A, Lettieri G, Iannaccone S, Magnani G, Marcone A, et al. Different FDG-PET metabolic patterns at single-subject level in the behavioral variant of fronto-temporal dementia. Elsevier Ltd; 2016;83:101–12.

  36. Caminiti SP, Sala A, Iaccarino L, Beretta L, Pilotto A, Gianolli L, et al. Brain glucose metabolism in Lewy body dementia: implications for diagnostic criteria. Alzheimers Res Ther BioMed Central. 2019;11:20.

    Article  Google Scholar 

  37. McKeith IG, Boeve BF, DIckson DW, Halliday G, Taylor JP, Weintraub D, et al. Diagnosis and management of dementia with Lewy bodies. Neurology. 2017;89:88–100.

    Article  Google Scholar 

  38. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. Elsevier; 2011;7:270–9.

  39. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement Elsevier; 2011;7:280–92.

  40. Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B, et al. Criteria for the diagnosis of corticobasal degeneration. Neurology AAN Enterprises; 2013;80:496–503.

  41. Teune LK, Bartels AL, de Jong BM, Willemsen ATM, Eshuis SA, de Vries JJ, et al. Typical cerebral metabolic patterns in neurodegenerative brain diseases. Mov Disord Wiley Online Library; 2010;25:2395–404.

  42. Caminiti SP, Alongi P, Majno L, Volontè MA, Cerami C, Gianolli L, et al. Evaluation of an optimized [18F]fluoro-deoxy-glucose positron emission tomography voxel-wise method to early support differential diagnosis in atypical Parkinsonian disorders. Eur J Neurol. 2017;24.

  43. Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. Eur J Nucl med Mol imaging. Soc Nuclear Med. 2008;49:390–8.

    Article  Google Scholar 

  44. Perani D, Iaccarino L, Bettinardi V. The need for “objective measurements” in FDG and amyloid PET neuroimaging. Clin Transl Imaging Springer; 2014;2:331–42.

  45. Presotto L, Ballarini T, Caminiti SP, Bettinardi V, Gianolli L, Perani D. Validation of 18 F–FDG-PET single-subject optimized SPM procedure with different PET scanners. Neuroinformatics. Springer; 2017;15:151–63.

  46. Gordon BA, Blazey TM, Su Y, Hari-Raj A, Dincer A, Flores S, et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol Elsevier; 2018;17:241–50.

  47. Mosconi L. Glucose metabolism in normal aging and Alzheimer’s disease: methodological and physiological considerations for PET studies. Clin Transl imaging Springer; 2013;1:217–33.

  48. Nichols T, Hayasaka S. Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res. Sage Publications Sage CA: Thousand Oaks, CA; 2003;12:419–46.

  49. Chen W-P, Samuraki M, Yanase D, Shima K, Takeda N, Ono K, et al. Effect of sample size for normal database on diagnostic performance of brain FDG PET for the detection of Alzheimer’s disease using automated image analysis. Nucl Med Commun LWW; 2008;29:270–6.

  50. Gallivanone F. The impact of different 18FDG PET healthy subject scans for comparison with single patient in SPM analysis. Q J Nucl Med Mol imaging. Minerva medica; 2014;61:115–32.

  51. Cerami C, Della Rosa PA, Magnani G, Santangelo R, Marcone A, Cappa SF, et al. Brain metabolic maps in mild cognitive impairment predict heterogeneity of progression to dementia. NeuroImage Clin Elsevier; 2015;7:187–94.

Download references

Funding

This work was supported by the Italian Ministry of Health (NET - 2011-02346784) and by the Italian Ministry of Health and the Italian Medicines Agency (Interceptor).

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defence award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Author information

Author notes

  1. Silvia Paola Caminiti, Arianna Sala and Luca Presotto contributed equally to this work.

Authors and Affiliations

  1. Vita-Salute San Raffaele University, Milan, Italy

    Silvia Paola Caminiti, Arianna Sala & Daniela Perani

  2. In vivo human molecular and structural neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Silvia Paola Caminiti, Arianna Sala & Daniela Perani

  3. Nuclear Medicine Unit, San Raffaele Hospital, Via Olgettina 60, 20132, Milan, Italy

    Luca Presotto & Daniela Perani

  4. Istituto Nazionale di Fisica Nucleare, Genoa, Italy

    Andrea Chincarini

  5. Ospedale di Prato (NOP) S. Stefano, Prato, Italy

    Stelvio Sestini

Authors
  1. Silvia Paola Caminiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arianna Sala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luca Presotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrea Chincarini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stelvio Sestini
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniela Perani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Orazio Schillaci
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Valentina Berti
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maria Lucia Calcagni
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Angelina Cistaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Silvia Morbelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Flavio Nobili
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sabina Pappatà
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Duccio Volterrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Clara Luigia Gobbo
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

for the Alzheimer’s Disease Neuroimaging Initiative (ADNI), for the Associazione Italiana Medicina Nucleare (AIMN) datasets, The AIMN Neurology Study-Group collaborators:

Corresponding author

Correspondence to Daniela Perani.

Ethics declarations

The authors declare that they have no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Additional information

Publisher’s note

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

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

This article is part of the Topical Collection on Neurology – Dementia.

Supplementary information

ESM 1

(DOCX 23 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Caminiti, S.P., Sala, A., Presotto, L. et al. Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps. Eur J Nucl Med Mol Imaging 48, 2486–2499 (2021). https://doi.org/10.1007/s00259-020-05175-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-020-05175-1

Keywords

Search

Navigation