Top

European Journal of Nuclear Medicine and Molecular Imaging

Published in:

26-08-2022 | Alzheimer's Disease | Original Article

Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach

Authors: Min Wang, Michael Schutte, Timo Grimmer, Aldana Lizarraga, Thomas Schultz, Dennis M. Hedderich, Janine Diehl-Schmid, Axel Rominger, Sybille Ziegler, Nassir Navab, Zhuangzhi Yan, Jiehui Jiang, Igor Yakushev, Kuangyu Shi

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 1/2022

Abstract

Purpose

Sparse inverse covariance estimation (SICE) is increasingly utilized to estimate inter-subject covariance of FDG uptake (FDG_cov) as proxy of metabolic brain connectivity. However, this statistical method suffers from the lack of robustness in the connectivity estimation. Patterns of FDG_cov were observed to be spatially similar with patterns of structural connectivity as obtained from DTI imaging. Based on this similarity, we propose to regularize the sparse estimation of FDG_cov using the structural connectivity.

Methods

We retrospectively analyzed the FDG-PET and DTI data of 26 healthy controls, 41 patients with Alzheimer’s disease (AD), and 30 patients with frontotemporal lobar degeneration (FTLD). Structural connectivity matrix derived from DTI data was introduced as a regularization parameter to assign individual penalties to each potential metabolic connectivity. Leave-one-out cross validation experiments were performed to assess the differential diagnosis ability of structure weighted SICE approach. A few approaches of structure weighted were compared with the standard SICE.

Results

Compared to the standard SICE, structural weighting has shown more stable performance in the supervised classification, especially in the differentiation AD vs. FTLD (accuracy of 89–90%, while unweighted SICE only 85%). There was a significant positive relationship between the minimum number of metabolic connection and the robustness of the classification accuracy (r = 0.57, P < 0.001). Shuffling experiments showed significant differences between classification score derived with true structural weighting and those obtained by randomized structure (P < 0.05).

Conclusion

The structure-weighted sparse estimation can enhance the robustness of metabolic connectivity, which may consequently improve the differentiation of pathological phenotypes.

Available only for authorised users

Yakushev I, Chételat G, Fischer FU, Landeau B, Bastin C, Scheurich A, et al. Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults. Neuroimage. 2013;79:184–90. https://doi.org/10.1016/j.neuroimage.2013.04.069.CrossRefPubMed

Wang M, Jiang J, Yan Z, Alberts I, Ge J, Zhang H, 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.CrossRefPubMedPubMedCentral

Huang S, Li J, Sun L, Ye J, Fleisher A, Wu T, et al. Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. Neuroimage. 2010;50(3):935–49. https://doi.org/10.1016/j.neuroimage.2009.12.120.CrossRefPubMed

Yakushev I, Drzezga A, Habeck C. Metabolic connectivity: methods and applications. Current opinion in neurology. 2017;30(6):677–85. https://doi.org/10.1097/wco.0000000000000494.CrossRefPubMed

Morbelli S, Perneczky R, Drzezga A, Frisoni GB, Caroli A, van Berckel BN, et al. Metabolic networks underlying cognitive reserve in prodromal Alzheimer disease: a European Alzheimer disease consortium project. J Nucl Med. 2013;54(6):894–902. https://doi.org/10.2967/jnumed.112.113928.CrossRefPubMed

Perani D, Farsad M, Ballarini T, Lubian F, Malpetti M, Fracchetti A, et al. The impact of bilingualism on brain reserve and metabolic connectivity in Alzheimer's dementia. Proc Natl Acad Sci U S A. 2017;114(7):1690–5. https://doi.org/10.1073/pnas.1610909114.CrossRefPubMedPubMedCentral

Titov D, Diehl-Schmid J, Shi K, Perneczky R, Zou N, Grimmer T, et al. Metabolic connectivity for differential diagnosis of dementing disorders. J Cereb Blood Flow Metab. 2017;37(1):252–62. https://doi.org/10.1177/0271678X15622465.CrossRefPubMed

Jeong Y, Cho SS, Park JM, Kang SJ, Lee JS, Kang E, et al. 18F-FDG PET findings in frontotemporal dementia: an SPM analysis of 29 patients. J Nucl Med. 2005;46(2):233–9.PubMed

Toussaint PJ, Perlbarg V, Bellec P, Desarnaud S, Lacomblez L, Doyon J, et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses. Neuroimage. 2012;63(2):936–46. https://doi.org/10.1016/j.neuroimage.2012.03.091.CrossRefPubMed

10.

Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9(3):432–41. https://doi.org/10.1093/biostatistics/kxm045.CrossRefPubMed

11.

Tucholka A, Grau-Rivera O, Falcon C, Rami L, Sánchez-Valle R, Lladó A, et al. Structural connectivity alterations along the Alzheimer's disease continuum: reproducibility across two independent samples and correlation with cerebrospinal fluid amyloid-β and Tau. J Alzheimer's Dis : JAD. 2018;61(4):1575–87. https://doi.org/10.3233/jad-170553.CrossRefPubMed

12.

Alm KH, Bakker A. Relationships between diffusion tensor imaging and cerebrospinal fluid metrics in early stages of the Alzheimer's disease continuum. J Alzheimer's Dis : JAD. 2019;70(4):965–81. https://doi.org/10.3233/jad-181210.CrossRefPubMed

13.

Yakushev I, Ripp I, Wang M, Savio A, Schutte M, Lizarraga A, et al. Mapping covariance in brain FDG uptake to structural connectivity. Eur J Nucl Med Mol Imaging. 2021. https://doi.org/10.1007/s00259-021-05590-y.

14.

Broser PJ, Groeschel S, Hauser TK, Lidzba K, Wilke M. Functional MRI-guided probabilistic tractography of cortico-cortical and cortico-subcortical language networks in children. Neuroimage. 2012;63(3):1561–70. https://doi.org/10.1016/j.neuroimage.2012.07.060.CrossRefPubMed

15.

Sreedharan RM, Menon AC, James JS, Kesavadas C, Thomas SV. Arcuate fasciculus laterality by diffusion tensor imaging correlates with language laterality by functional MRI in preadolescent children. Neuroradiology. 2015;57(3):291–7. https://doi.org/10.1007/s00234-014-1469-1.CrossRefPubMed

16.

Zhu D, Li K, Guo L, Jiang X, Zhang T, Zhang D, et al. DICCCOL: dense individualized and common connectivity-based cortical landmarks. Cereb Cortex. 2013;23(4):786–800. https://doi.org/10.1093/cercor/bhs072.CrossRefPubMed

17.

Bowman FD, Zhang L, Derado G, Chen S. Determining functional connectivity using fMRI data with diffusion-based anatomical weighting. Neuroimage. 2012;62(3):1769–79. https://doi.org/10.1016/j.neuroimage.2012.05.032.CrossRefPubMed

18.

Ng B, Varoquaux G, Poline JB, Thirion B. A novel sparse graphical approach for multimodal brain connectivity inference. Med Image Comput Comput Assist Interv. 2012;15(Pt 1):707–14. https://doi.org/10.1007/978-3-642-33415-3_87.CrossRefPubMed

19.

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(7):939–44. https://doi.org/10.1212/wnl.34.7.939.CrossRefPubMed

20.

Neary D, Snowden JS, Gustafson L, Passant U, Stuss D, Black S, et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology. 1998;51(6):1546–54. https://doi.org/10.1212/wnl.51.6.1546.CrossRefPubMed

21.

Gonzalez-Escamilla G, Lange C, Teipel S, Buchert R, Grothe MJ. PETPVE12: an SPM toolbox for partial volume effects correction in brain PET - application to amyloid imaging with AV45-PET. Neuroimage. 2017;147:669–77. https://doi.org/10.1016/j.neuroimage.2016.12.077.CrossRefPubMed

22.

Hammers A, Allom R, Koepp MJ, Free SL, Myers R, Lemieux L, 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.CrossRefPubMedPubMedCentral

23.

Wilkins B, Lee N, Gajawelli N, Law M, Leporé N. Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values. Neuroimage. 2015;109:341–56. https://doi.org/10.1016/j.neuroimage.2014.12.060.CrossRefPubMed

24.

Thirion B, Varoquaux G, Dohmatob E, Poline JB. Which fMRI clustering gives good brain parcellations? Front Neurosci. 2014;8:167. https://doi.org/10.3389/fnins.2014.00167.CrossRefPubMedPubMedCentral

Title: Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach
Authors: Min Wang
Michael Schutte
Timo Grimmer
Aldana Lizarraga
Thomas Schultz
Dennis M. Hedderich
Janine Diehl-Schmid
Axel Rominger
Sybille Ziegler
Nassir Navab
Zhuangzhi Yan
Jiehui Jiang
Igor Yakushev
Kuangyu Shi
Publication date: 26-08-2022
Publisher: Springer Berlin Heidelberg
Keywords: Alzheimer's Disease
Positron Emission Tomography
Alzheimer's Disease
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 1/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-022-05949-9

ACC 2024 Congress

Springer Medicine

Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach

Abstract

Purpose

Methods

Results

Conclusion

ACC 2024 Congress

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2022

Lymph node classification in E-PSMA reporting guidelines for PSMA-PET

Biodistribution and dosimetry in human healthy volunteers of the PET radioligands [11C]CHDI-00485180-R and [11C]CHDI-00485626, designed for quantification of cerebral aggregated mutant huntingtin

Correction to: [18F]FDG brain PET findings in CLIPPERS at diagnosis and therapeutic follow-up

Regional myocardial perfusion imaging in predicting vessel-related outcome: interplay between the perfusion results and angiographic findings

Beyond equality, women require extra care in cardiovascular imaging

Quantitative PET imaging of the CD4 pool in nonhuman primates