DWI; fMRI; functional connectivity; molecular connectivity; multimodal imaging; Neurology; Neurology (clinical); Cardiology and Cardiovascular Medicine
Abstract :
[en] Functional magnetic resonance and diffusion weighted imaging have so far made a major contribution to delineation of the brain connectome at the macroscale. While functional connectivity (FC) was shown to be related to structural connectivity (SC) to a certain degree, their spatial overlap is unknown. Even less clear are relations of SC with estimates of connectivity from inter-subject covariance of regional F18-fluorodeoxyglucose uptake (FDGcov) and grey matter volume (GMVcov). Here, we asked to what extent SC underlies three proxy estimates of brain connectivity: FC, FDGcov and GMVcov. Simultaneous PET/MR acquisitions were performed in 56 healthy middle-aged individuals. Similarity between four networks was assessed using Spearman correlation and convergence ratio (CR), a measure of spatial overlap. Spearman correlation coefficient was 0.27 for SC-FC, 0.40 for SC-FDGcov, and 0.15 for SC-GMVcov. Mean CRs were 51% for SC-FC, 48% for SC-FDGcov, and 37% for SC-GMVcov. These results proved to be reproducible and robust against image processing steps. In sum, we found a relevant similarity of SC with FC and FDGcov, while GMVcov consistently showed the weakest similarity. These findings indicate that white matter tracts underlie FDGcov to a similar degree as FC, supporting FDGcov as estimate of functional brain connectivity.
Disciplines :
Neurosciences & behavior
Author, co-author :
Lizarraga, Aldana; Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
Ripp, Isabelle ; Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
Sala, Arianna ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group ; Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
Shi, Kuangyu; Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
Düring, Marco; Medical Image Analysis Center (MIAC AG) and Qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
Koch, Kathrin; Department of Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
Yakushev, Igor; Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
Language :
English
Title :
Similarity between structural and proxy estimates of brain connectivity.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the German Research Foundation (DFG), grants to I.Y. (grant number YA 373/3-1) and to K.K. (grant number KO 3744/8-1), and by the Federal Ministry of Education and Research Germany (BMBF), grant to I.Y. (grant number 031L0200B). Acknowledgements
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Bibliography
Smith R Raffelt D Tournier JD, et al. Quantitative streamlines tractography: methods and inter-subject normalisation. Open Science Framework 2020, https://osf.io/c67kn (accessed 9 March 2023).
Friston KJ. Functional and effective connectivity: a review. Brain Connect 2011; 1: 13–36.
Lowe MJ Mock BJ Sorenson JA. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. NeuroImage 1998; 7: 119–132.
Skudlarski P Jagannathan K Calhoun VD, et al. Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. NeuroImage 2008; 43: 554–561.
Honey CJ Sporns O Cammoun L, et al. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U S A 2009; 106: 2035–2040.
Reid AT Lewis J Bezgin G, et al. A cross-modal, cross-species comparison of connectivity measures in the primate brain. NeuroImage 2016; 125: 311–331.
Garcés P Pereda E Hernández‐Tamames JA, et al. Multimodal description of whole brain connectivity: a comparison of resting state MEG, fMRI, and DWI. Hum Brain Mapp 2016; 37: 20–34.
Zimmermann J Griffiths J Schirner M, et al. Subject specificity of the correlation between large-scale structural and functional connectivity. Netw Neurosci 2019; 3: 90–106.
Messé A. Parcellation influence on the connectivity‐based structure–function relationship in the human brain. Hum Brain Mapp 2020; 41: 1167–1180.
Koch MA Norris DG Hund-Georgiadis M. An Investigation of functional and anatomical connectivity using magnetic resonance imaging. NeuroImage 2002; 16: 241–250.
Seghier ML Price CJ. Interpreting and utilising intersubject variability in brain function. Trends Cogn Sci 2018; 22: 517–530.
Romero-Garcia R Whitaker KJ Váša F, NSPN Consortiumet al. Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. NeuroImage 2018; 171: 256–267.
Seidlitz J Váša F Shinn M, NSPN Consortiumet al. Morphometric similarity networks detect microscale cortical organization and predict Inter-Individual cognitive variation. Neuron 2018; 97: 231–247.e7. Jan
Montembeault M Rouleau I Provost JS, Alzheimer's Disease Neuroimaging Initiativeet al. Altered gray matter structural covariance networks in early stages of alzheimer’s disease. Cereb Cortex 2016; 26: 2650–2662.
Palaniyappan L Hodgson O Balain V, et al. Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study. Psychol Med 2019; 49: 412–420.
Gong G He Y Chen ZJ, et al. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. NeuroImage 2012; 59: 1239–1248.
Reid AT Hoffstaedter F Gong G, et al. A seed-based cross-modal comparison of brain connectivity measures. Brain Struct Funct 2017; 222: 1131–1151.
Di X Gohel S Thielcke A, et al. Do all roads lead to Rome? A comparison of brain networks derived from inter-subject volumetric and metabolic covariance and moment-to-moment hemodynamic correlations in old individuals. Brain Struct Funct 2017; 222: 3833–3845.
Sala A Lizarraga A Caminiti SP, et al. Brain connectomics: time for a molecular imaging perspective? Trends Cogn Sci 2023; 27: 353–366.
Moeller JR Strother SC Sidtis JJ, et al. Scaled subprofile model: a statistical approach to the analysis of functional patterns in positron emission tomographic data. J Cereb Blood Flow Metab 1987; 7: 649–658.
Sala A Caminiti SP Iaccarino L, et al. Vulnerability of multiple large‐scale brain networks in dementia with Lewy bodies. Hum Brain Mapp 2019; 40: 4537–4550.
Caminiti SP Tettamanti M Sala A, et al. Metabolic connectomics targeting brain pathology in dementia with Lewy bodies. J Cereb Blood Flow Metab 2017; 37: 1311–1325.
Veronese M Moro L Arcolin M, et al. Covariance statistics and network analysis of brain PET imaging studies. Sci Rep 2019; 9: 2496.
Verger A Horowitz T Chawki MB, et al. From metabolic connectivity to molecular connectivity: application to dopaminergic pathways. Eur J Nucl Med Mol Imaging 2020; 47: 413–424.
Hoenig MC Bischof GN Seemiller J, et al. Networks of tau distribution in Alzheimer’s disease. Brain 2018; 141: 568–581.
Ossenkoppele R Iaccarino L Schonhaut DR, et al. Tau covariance patterns in Alzheimer’s disease patients match intrinsic connectivity networks in the healthy brain. Neuroimage Clin 2019; 23: 101848.
Pereira JB Ossenkoppele R Palmqvist S, et al. Amyloid and tau accumulate across distinct spatial networks and are differentially associated with brain connectivity. eLife 2019; 8: e50830.
Yakushev I Drzezga A Habeck C. Metabolic connectivity: methods and applications. Curr Opin Neurol 2017; 30: 677–685.
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: 2855–2867.
Zou N Chetelat G Baydogan MG, et al. Metabolic connectivity as index of verbal working memory. J Cereb Blood Flow Metab 2015; 35: 1122–1126.
Huang Q Zhang J Zhang T, et al. Age-associated reorganization of metabolic brain connectivity in Chinese children. Eur J Nucl Med Mol Imaging 2020; 47: 235–246.
Perani D Farsad M Ballarini T, 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: 1690–1695.
Titov D Diehl-Schmid J Shi K, et al. Metabolic connectivity for differential diagnosis of dementing disorders. J Cereb Blood Flow Metab 2017; 37: 252–262.
Pagani M Giuliani A Öberg J, et al. Progressive disintegration of brain networking from normal aging to Alzheimer disease: analysis of independent components of 18F-FDG PET data. J Nucl Med 2017; 58: 1132–1139.
Verger A Roman S Chaudat RM, et al. Changes of metabolism and functional connectivity in late-onset deafness: evidence from cerebral 18F-FDG-PET. Hear Res 2017; 353: 8–16.
Shim HK Lee HJ Kim SE, et al. Alterations in the metabolic networks of temporal lobe epilepsy patients: a graph theoretical analysis using FDG-PET. Neuroimage Clin 2020; 27: 102349.
Yakushev I Ripp I Wang M, et al. Mapping covariance in brain FDG uptake to structural connectivity. Eur J Nucl Med Mol Imaging 2022; 49: 1288–1297.
Messaritaki E Dimitriadis SI Jones DK. Optimization of graph construction can significantly increase the power of structural brain network studies. NeuroImage 2019; 199: 495–511.
Ripp I Emch M Wu Q, et al. Adaptive working memory training does not produce transfer effects in cognition and neuroimaging. Transl Psychiatry 2022; 12: 512.
Rolls ET Joliot M Tzourio-Mazoyer N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. NeuroImage 2015; 122: 1–5.
Avants BB Tustison NJ Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 2011; 54: 2033–2044.
Andersson JLR Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 2016; 125: 1063–1078.
Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17: 143–155.
Behrens TEJ Berg HJ Jbabdi S, et al. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 2007; 34: 144–155.
van den Heuvel MP Sporns O. Rich-club organization of the human connectome. J Neurosci 2011; 31: 15775–15786.
Bonilha L Gleichgerrcht E Fridriksson J, et al. Reproducibility of the structural brain connectome derived from diffusion tensor imaging (Hayasaka S, ed.). PLoS ONE 2015; 10: e0135247.
Hagmann P Sporns O Madan N, et al. White matter maturation reshapes structural connectivity in the late developing human brain. Proc Natl Acad Sci U S A 2010; 107: 19067–19072.
Barbagallo G Caligiuri ME Arabia G, et al. Structural connectivity differences in motor network between tremor-dominant and nontremor Parkinson’s disease: structural network in PD phenotypes. Hum Brain Mapp 2017; 38: 4716–4729.
Nomi JS Schettini E Broce I, et al. Structural connections of functionally defined human insular subdivisions. Cereb Cortex 2018; 28: 3445–3456.
Buchanan CR Bastin ME Ritchie SJ, et al. The effect of network thresholding and weighting on structural brain networks in the UK biobank. NeuroImage 2020; 211: 116443.
Jenkinson M Bannister P Brady M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 2002; 17: 825–841.
Shirer WR Jiang H Price CM, et al. Optimization of rs-fMRI pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination. NeuroImage 2015; 117: 67–79.
Kalpouzos G Chételat G Baron JC, et al. Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiol Aging 2009; 30: 112–124.
Wu K Taki Y Sato K, et al. Age-related changes in topological organization of structural brain networks in healthy individuals. Hum Brain Mapp 2012; 33: 552–568.
Tong Y Hocke LM Frederick BB. Low frequency systemic hemodynamic “noise” in resting state BOLD fMRI: characteristics, causes, implications, mitigation strategies, and applications. Front Neurosci 2019; 13: 787.
Geerligs L Cam CAN Henson RN. Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation. NeuroImage 2016; 135: 16–31.
Huang SY Hsu JL Lin KJ, et al. Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer’s disease. Sci Rep 2018; 8: 13807.
Vanicek T Hahn A Traub-Weidinger T, et al. Insights into intrinsic brain networks based on graph theory and PET in right- compared to left-sided temporal lobe epilepsy. Sci Rep 2016; 6: 28513.
Hagmann P Cammoun L Gigandet X, et al. Mapping the structural core of human cerebral cortex (Friston KJ, ed.). PLoS Biol 2008; 6: e159.
Savio A Fünger S Tahmasian M, et al. Resting-state networks as simultaneously measured with functional MRI and PET. J Nucl Med 2017; 58: 1314–1317.
Sala A Lizarraga A Ripp I, et al. Static versus functional PET: making sense of metabolic connectivity. Cereb Cortex 2022; 32: 1125–1129.
Wang J Khosrowabadi R Ng KK, et al. Alterations in brain network topology and structural-functional connectome coupling relate to cognitive impairment. Front Aging Neurosci 2018; 10: 404.
Mechelli A Friston KJ Frackowiak RS, et al. Structural covariance in the human cortex. J Neurosci 2005; 25: 8303–8310.
Draganski B May A. Training-induced structural changes in the adult human brain. Behav Brain Res 2008; 192: 137–142.
Zhao K Zheng Q Che T, et al. Regional radiomics similarity networks (R2SNs) in the human brain: reproducibility, small-world properties and a biological basis. Network Neuroscience 2021; 5: 783–797.
Betzel RF Griffa A Hagmann P, et al. Distance-dependent consensus thresholds for generating group-representative structural brain networks. Netw Neurosci 2019; 3: 475–496.
Donahue CJ Sotiropoulos SN Jbabdi S, et al. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J Neurosci 2016; 36: 6758–6770.
Calamante F. The seven deadly sins of measuring brain structural connectivity using diffusion MRI streamlines fibre-tracking. Diagnostics 2019; 69: 115.
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