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Published in: European Journal of Nuclear Medicine and Molecular Imaging 4/2022

Open Access 01-03-2022 | Positron Emission Tomography | Original Article

Mapping covariance in brain FDG uptake to structural connectivity

Authors: Igor Yakushev, Isabelle Ripp, Min Wang, Alex Savio, Michael Schutte, Aldana Lizarraga, Borjana Bogdanovic, Janine Diehl-Schmid, Dennis M. Hedderich, Timo Grimmer, Kuangyu Shi

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 4/2022

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Abstract

Purpose

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGcov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDGcov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDGcov and structural connectivity networks.

Methods

We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDGcov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking.

Results

For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDGcov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections.

Conclusion

Structural connectivity via white matter fiber tracts is a relevant substrate of FDGcov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDGcov connections.
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Literature
1.
go back to reference Yakushev I, Drzezga A, Habeck C. Metabolic connectivity: methods and applications. Curr Opin Neurol. 2017;30:677–85.CrossRef Yakushev I, Drzezga A, Habeck C. Metabolic connectivity: methods and applications. Curr Opin Neurol. 2017;30:677–85.CrossRef
2.
go back to reference Morbelli S, Perneczky R, Drzezga A, Frisoni GB, Caroli A, van Berckel BNM, et al. Metabolic networks underlying cognitive reserve in prodromal Alzheimer disease: a European Alzheimer disease consortium project. J Nucl Med. 2013;54:894–902.CrossRef Morbelli S, Perneczky R, Drzezga A, Frisoni GB, Caroli A, van Berckel BNM, et al. Metabolic networks underlying cognitive reserve in prodromal Alzheimer disease: a European Alzheimer disease consortium project. J Nucl Med. 2013;54:894–902.CrossRef
5.
go back to reference Zou N, Chetelat G, Baydogan MG, Li J, Fischer FU, Titov D, et al. Metabolic connectivity as index of verbal working memory. J Cereb Blood Flow Metab. 2015;35:1122–6.CrossRef Zou N, Chetelat G, Baydogan MG, Li J, Fischer FU, Titov D, et al. Metabolic connectivity as index of verbal working memory. J Cereb Blood Flow Metab. 2015;35:1122–6.CrossRef
6.
go back to reference Verger A, Klesse E, Chawki MB, Witjas T, Azulay J-P, Eusebio A, et al. Brain PET substrate of impulse control disorders in Parkinson’s disease: a metabolic connectivity study. Hum Brain Mapp. 2018;39:3178–86.CrossRef Verger A, Klesse E, Chawki MB, Witjas T, Azulay J-P, Eusebio A, et al. Brain PET substrate of impulse control disorders in Parkinson’s disease: a metabolic connectivity study. Hum Brain Mapp. 2018;39:3178–86.CrossRef
7.
8.
go back to reference 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;37:252–62.CrossRef 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;37:252–62.CrossRef
9.
go back to reference Caminiti SP, Tettamanti M, Sala A, Presotto L, Iannaccone S, Cappa SF, et al. Metabolic connectomics targeting brain pathology in dementia with Lewy bodies. J Cereb Blood Flow Metab. 2017;37:1311–25.CrossRef Caminiti SP, Tettamanti M, Sala A, Presotto L, Iannaccone S, Cappa SF, et al. Metabolic connectomics targeting brain pathology in dementia with Lewy bodies. J Cereb Blood Flow Metab. 2017;37:1311–25.CrossRef
10.
go back to reference Huber M, Beyer L, Prix C, Schönecker S, Palleis C, Rauchmann B-S, et al. Metabolic correlates of dopaminergic loss in dementia with Lewy bodies. Mov Disord. 2020;35:595–605.CrossRef Huber M, Beyer L, Prix C, Schönecker S, Palleis C, Rauchmann B-S, et al. Metabolic correlates of dopaminergic loss in dementia with Lewy bodies. Mov Disord. 2020;35:595–605.CrossRef
13.
go back to reference Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al 2009 Predicting human resting-state functional connectivity from structural connectivity. PNAS [Internet]. National Academy of Sciences [cited 2020 Aug 13];106:2035–40. Available from: https://www.pnas.org/content/106/6/2035 Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al 2009 Predicting human resting-state functional connectivity from structural connectivity. PNAS [Internet]. National Academy of Sciences [cited 2020 Aug 13];106:2035–40. Available from: https://​www.​pnas.​org/​content/​106/​6/​2035
14.
go back to reference Jamadar SD, Ward PGD, Liang EX, Orchard ER, Chen Z, Egan GF. 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;31:2855–67.CrossRef Jamadar SD, Ward PGD, Liang EX, Orchard ER, Chen Z, Egan GF. 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;31:2855–67.CrossRef
15.
go back to reference Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund L-O, et al 2004 Mild cognitive impairment – beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine [Internet]. [cited 2021 Sep 23];256:240–6. Available from: https://onlinelibrary.wiley.com/doi/abs/https://doi.org/10.1111/j.1365-2796.2004.01380.x Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund L-O, et al 2004 Mild cognitive impairment – beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine [Internet]. [cited 2021 Sep 23];256:240–6. Available from: https://​onlinelibrary.​wiley.​com/​doi/​abs/​https://​doi.​org/​10.​1111/​j.​1365-2796.​2004.​01380.​x
16.
go back to reference World Health Organization 1993 The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research. CIM-10/ICD-10: Classification internationale des maladies Dixième révision Chapitre V(F): troubles mentaux et troubles du comportement: critères diagnostiques pour la recherche [Internet]. Geneva: World Health Organization Available from: https://apps.who.int/iris/handle/10665/37108 World Health Organization 1993 The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research. CIM-10/ICD-10: Classification internationale des maladies Dixième révision Chapitre V(F): troubles mentaux et troubles du comportement: critères diagnostiques pour la recherche [Internet]. Geneva: World Health Organization Available from: https://​apps.​who.​int/​iris/​handle/​10665/​37108
17.
19.
go back to reference 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;19:224–47.CrossRef 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;19:224–47.CrossRef
20.
21.
go back to reference Borghammer P, Aanerud J, Gjedde A. Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization. Neuroimage. 2009;46:981–8.CrossRef Borghammer P, Aanerud J, Gjedde A. Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization. Neuroimage. 2009;46:981–8.CrossRef
22.
go back to reference Yakushev I, Landvogt C, Buchholz H-G, Fellgiebel A, Hammers A, Scheurich A, et al. Choice of reference area in studies of Alzheimer’s disease using positron emission tomography with fluorodeoxyglucose-F18. Psychiatry Res. 2008;164:143–53.CrossRef Yakushev I, Landvogt C, Buchholz H-G, Fellgiebel A, Hammers A, Scheurich A, et al. Choice of reference area in studies of Alzheimer’s disease using positron emission tomography with fluorodeoxyglucose-F18. Psychiatry Res. 2008;164:143–53.CrossRef
23.
go back to reference Yakushev I, Hammers A, Fellgiebel A, Schmidtmann I, Scheurich A, Buchholz H-G, et al. SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. Neuroimage. 2009;44:43–50.CrossRef Yakushev I, Hammers A, Fellgiebel A, Schmidtmann I, Scheurich A, Buchholz H-G, et al. SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. Neuroimage. 2009;44:43–50.CrossRef
24.
go back to reference Spetsieris PG, Eidelberg D. Scaled subprofile modeling of resting state imaging data in Parkinson’s disease: methodological issues. Neuroimage. 2011;54:2899–914.CrossRef Spetsieris PG, Eidelberg D. Scaled subprofile modeling of resting state imaging data in Parkinson’s disease: methodological issues. Neuroimage. 2011;54:2899–914.CrossRef
25.
go back to reference Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–78.CrossRef Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–78.CrossRef
26.
go back to reference Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform. 2011;5:13.CrossRef Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform. 2011;5:13.CrossRef
28.
go back to reference Tibshirani R 1996 Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) [Internet]. [cited 2020 Oct 22];58:267–88. Available from: https://rss.onlinelibrary.wiley.com/doi/abs/https://doi.org/10.1111/j.2517-6161.1996.tb02080.x Tibshirani R 1996 Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) [Internet]. [cited 2020 Oct 22];58:267–88. Available from: https://​rss.​onlinelibrary.​wiley.​com/​doi/​abs/​https://​doi.​org/​10.​1111/​j.​2517-6161.​1996.​tb02080.​x
29.
go back to reference 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;50:935–49.CrossRef 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;50:935–49.CrossRef
30.
go back to reference Cook PA, Bai Y, Nedjati-Gilani S, Seunarine KK, Hall MG, Parker GJ, et al 2006 Camino: open-source diffusion-MRI reconstruction and processing. in 14th scientific meeting of the International Society for Magnetic Resonance in Medicine Seattle, WA, USA p 2759. 2006;1. Cook PA, Bai Y, Nedjati-Gilani S, Seunarine KK, Hall MG, Parker GJ, et al 2006 Camino: open-source diffusion-MRI reconstruction and processing. in 14th scientific meeting of the International Society for Magnetic Resonance in Medicine Seattle, WA, USA p 2759. 2006;1.
32.
go back to reference Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45:265–9.CrossRef Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45:265–9.CrossRef
33.
go back to reference Thiebaut de Schotten M, Ffytche DH, Bizzi A, Dell’Acqua F, Allin M, Walshe M, et al. Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography. Neuroimage. 2011 54:49–59. Thiebaut de Schotten M, Ffytche DH, Bizzi A, Dell’Acqua F, Allin M, Walshe M, et al. Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography. Neuroimage. 2011 54:49–59.
35.
go back to reference Gong G, He Y, Chen ZJ, Evans AC. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. Neuroimage. 2012;59:1239–48.CrossRef Gong G, He Y, Chen ZJ, Evans AC. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. Neuroimage. 2012;59:1239–48.CrossRef
36.
go back to reference Koenis MMG, Brouwer RM, van den Heuvel MP, Mandl RCW, van Soelen ILC, Kahn RS, et al. Development of the brain’s structural network efficiency in early adolescence: a longitudinal DTI twin study. Hum Brain Mapp. 2015;36:4938–53.CrossRef Koenis MMG, Brouwer RM, van den Heuvel MP, Mandl RCW, van Soelen ILC, Kahn RS, et al. Development of the brain’s structural network efficiency in early adolescence: a longitudinal DTI twin study. Hum Brain Mapp. 2015;36:4938–53.CrossRef
37.
go back to reference Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science. 2002;296:910–3.CrossRef Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science. 2002;296:910–3.CrossRef
41.
go back to reference Straathof M, Sinke MR, Dijkhuizen RM, Otte WM. A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains. J Cereb Blood Flow Metab. 2019;39:189–209.CrossRef Straathof M, Sinke MR, Dijkhuizen RM, Otte WM. A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains. J Cereb Blood Flow Metab. 2019;39:189–209.CrossRef
42.
go back to reference Zimmermann J, Ritter P, Shen K, Rothmeier S, Schirner M, McIntosh AR. Structural architecture supports functional organization in the human aging brain at a regionwise and network level. Hum Brain Mapp. 2016;37:2645–61.CrossRef Zimmermann J, Ritter P, Shen K, Rothmeier S, Schirner M, McIntosh AR. Structural architecture supports functional organization in the human aging brain at a regionwise and network level. Hum Brain Mapp. 2016;37:2645–61.CrossRef
43.
go back to reference Uddin LQ, Supekar KS, Ryali S, Menon V. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J Neurosci [Internet]. Society for Neuroscience; 2011 [cited 2020 Aug 14];31:18578–89. Available from: https://www.jneurosci.org/content/31/50/18578 Uddin LQ, Supekar KS, Ryali S, Menon V. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J Neurosci [Internet]. Society for Neuroscience; 2011 [cited 2020 Aug 14];31:18578–89. Available from: https://​www.​jneurosci.​org/​content/​31/​50/​18578
44.
go back to reference Roland JL, Snyder AZ, Hacker CD, Mitra A, Shimony JS, Limbrick DD, et al. On the role of the corpus callosum in interhemispheric functional connectivity in humans. PNAS [Internet]. National Academy of Sciences; 2017 [cited 2020 Aug 14];114:13278–83. Available from: https://www.pnas.org/content/114/50/13278 Roland JL, Snyder AZ, Hacker CD, Mitra A, Shimony JS, Limbrick DD, et al. On the role of the corpus callosum in interhemispheric functional connectivity in humans. PNAS [Internet]. National Academy of Sciences; 2017 [cited 2020 Aug 14];114:13278–83. Available from: https://​www.​pnas.​org/​content/​114/​50/​13278
45.
go back to reference Vázquez-Rodríguez B, Suárez LE, Markello RD, Shafiei G, Paquola C, Hagmann P, et al. Gradients of structure–function tethering across neocortex. PNAS [Internet]. National Academy of Sciences; 2019 [cited 2020 Aug 14];116:21219–27. Available from: https://www.pnas.org/content/116/42/21219 Vázquez-Rodríguez B, Suárez LE, Markello RD, Shafiei G, Paquola C, Hagmann P, et al. Gradients of structure–function tethering across neocortex. PNAS [Internet]. National Academy of Sciences; 2019 [cited 2020 Aug 14];116:21219–27. Available from: https://​www.​pnas.​org/​content/​116/​42/​21219
46.
go back to reference Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, et al. Weight consistency specifies regularities of macaque cortical networks. Cereb Cortex. 2011;21:1254–72.CrossRef Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, et al. Weight consistency specifies regularities of macaque cortical networks. Cereb Cortex. 2011;21:1254–72.CrossRef
48.
go back to reference Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLOS Biology [Internet]. 2008 [cited 2019 Aug 30];6:e159. Available from: https://journals.plos.org/plosbiology/article?id=https://doi.org/10.1371/journal.pbio.0060159 Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLOS Biology [Internet]. 2008 [cited 2019 Aug 30];6:e159. Available from: https://​journals.​plos.​org/​plosbiology/​article?​id=​https://​doi.​org/​10.​1371/​journal.​pbio.​0060159
49.
go back to reference Hahn G, Skeide MA, Mantini D, Ganzetti M, Destexhe A, Friederici AD, et al. A new computational approach to estimate whole-brain effective connectivity from functional and structural MRI, applied to language development. Scientific Reports [Internet]. Nature Publishing Group; 2019 [cited 2020 Sep 22];9:8479. Available from: https://www.nature.com/articles/s41598-019-44909-6 Hahn G, Skeide MA, Mantini D, Ganzetti M, Destexhe A, Friederici AD, et al. A new computational approach to estimate whole-brain effective connectivity from functional and structural MRI, applied to language development. Scientific Reports [Internet]. Nature Publishing Group; 2019 [cited 2020 Sep 22];9:8479. Available from: https://​www.​nature.​com/​articles/​s41598-019-44909-6
50.
go back to reference Di X, Biswal BB, Alzheimer’s Disease Neuroimaging Initiative. Metabolic brain covariant networks as revealed by FDG-PET with reference to resting-state fMRI networks. Brain Connect. 2012;2:275–83. Di X, Biswal BB, Alzheimer’s Disease Neuroimaging Initiative. Metabolic brain covariant networks as revealed by FDG-PET with reference to resting-state fMRI networks. Brain Connect. 2012;2:275–83.
52.
go back to reference Horwitz B, Duara R, Rapoport SI. Intercorrelations of glucose metabolic rates between brain regions: application to healthy males in a state of reduced sensory input. J Cereb Blood Flow Metab. 1984;4:484–99.CrossRef Horwitz B, Duara R, Rapoport SI. Intercorrelations of glucose metabolic rates between brain regions: application to healthy males in a state of reduced sensory input. J Cereb Blood Flow Metab. 1984;4:484–99.CrossRef
54.
go back to reference Gallos LK, Makse HA, Sigman M. A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks. PNAS [Internet]. National Academy of Sciences; 2012 [cited 2020 May 19];109:2825–30. Available from: https://www.pnas.org/content/109/8/2825 Gallos LK, Makse HA, Sigman M. A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks. PNAS [Internet]. National Academy of Sciences; 2012 [cited 2020 May 19];109:2825–30. Available from: https://​www.​pnas.​org/​content/​109/​8/​2825
55.
go back to reference Sinke MRT, Otte WM, Christiaens D, Schmitt O, Leemans A, van der Toorn A, et al. Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics. Brain Struct Funct [Internet]. 2018 [cited 2020 Sep 22];223:2269–85. Available from:https://doi.org/10.1007/s00429-018-1628-y Sinke MRT, Otte WM, Christiaens D, Schmitt O, Leemans A, van der Toorn A, et al. Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics. Brain Struct Funct [Internet]. 2018 [cited 2020 Sep 22];223:2269–85. Available from:https://​doi.​org/​10.​1007/​s00429-018-1628-y
57.
go back to reference Khalsa S, Mayhew SD, Chechlacz M, Bagary M, Bagshaw AP. The structural and functional connectivity of the posterior cingulate cortex: comparison between deterministic and probabilistic tractography for the investigation of structure-function relationships. Neuroimage. 2014;102(Pt 1):118–27.CrossRef Khalsa S, Mayhew SD, Chechlacz M, Bagary M, Bagshaw AP. The structural and functional connectivity of the posterior cingulate cortex: comparison between deterministic and probabilistic tractography for the investigation of structure-function relationships. Neuroimage. 2014;102(Pt 1):118–27.CrossRef
Metadata
Title
Mapping covariance in brain FDG uptake to structural connectivity
Authors
Igor Yakushev
Isabelle Ripp
Min Wang
Alex Savio
Michael Schutte
Aldana Lizarraga
Borjana Bogdanovic
Janine Diehl-Schmid
Dennis M. Hedderich
Timo Grimmer
Kuangyu Shi
Publication date
01-03-2022
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 4/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-021-05590-y

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