Abstract
Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called “electroencephalography (EEG) source connectivity” has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the “EEG source connectivity” pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.
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Data used in this work can be found at https://drive.google.com/drive/folders/1yDwdoLwSOg9UZrdDf6AzpT78Ve5HJRxT?usp=sharing.
Code Availability
Data and Codes supporting the results of this study are available at https://github.com/sahar-allouch/comp-epi.git. We used Matlab (The Mathworks, USA, version 2018b), Brainstorm toolbox (Tadel et al. 2011), Fieldtrip toolbox ((Oostenveld et al. 2011); http://fieldtriptoolbox.org), OpenMEEG (Gramfort et al. 2010) implemented in Brainstorm, and BrainNet Viewer (Xia et al. 2013).
References
Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676. https://doi.org/10.1093/cercor/bhs352
Anzolin A, Presti P, Van De Steen F, Astolfi L, Haufe S, Marinazzo D (2019) Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources. Brain Topogr. https://doi.org/10.1007/s10548-019-00705-z
Awan FG, Saleem O, Kiran A (2019) Recent trends and advances in solving the inverse problem for EEG source localization. Inverse Probl Sci Eng 27(11):1521–1536. https://doi.org/10.1080/17415977.2018.1490279
Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. IEEE Signal Process Mag 18:14–30
Bartolomei F, Guye M, Wendling F (2013) Abnormal binding and disruption in large scale networks involved in human partial seizures. EPJ Nonlinear Biomed Phys 1(4):1–16. https://doi.org/10.1140/epjnbp11
Bassett DS, Sporns O (2017) Network neuroscience. Nat Neurosci 20(3):353–364. https://doi.org/10.1038/nn.4502
Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw. https://doi.org/10.18637/jss.v067.i01
Bensaid S, Modolo J, Merlet I, Wendling F, Benquet P (2019) COALIA: a computational model of human EEG for consciousness research. Front Syst Neurosci 13:1–18. https://doi.org/10.3389/fnsys.2019.00059
Bettus G, Wendling F, Guye M, Valton L, Régis J, Chauvel P, Bartolomei F (2008) Enhanced EEG functional connectivity in mesial temporal lobe epilepsy. Epilepsy Res 81(1):58–68. https://doi.org/10.1016/j.eplepsyres.2008.04.020
Bradley A, Yao J, Dewald J, Richter CP (2016) Evaluation of electroencephalography source localization algorithms with multiple cortical sources. PLoS ONE 11(1):1–14. https://doi.org/10.1371/journal.pone.0147266
Canuet L, Ishii R, Pascual-Marqui RD, Iwase M, Kurimoto R, Aoki Y, Ikeda S, Takahashi H, Nakahachi T, Takeda M (2011) Resting-state EEG source localization and functional connectivity in schizophrenia-like psychosis of epilepsy. PLoS ONE. https://doi.org/10.1371/journal.pone.0027863
Cho JH, Vorwerk J, Wolters CH, Knösche TR (2015) Influence of the head model on EEG and MEG source connectivity analyses. Neuroimage 110:60–77. https://doi.org/10.1016/j.neuroimage.2015.01.043
Colclough GL, Woolrich MW, Tewarie PK, Brookes MJ, Quinn AJ, Smith SM (2016) How reliable are MEG resting-state connectivity metrics? Neuroimage 138:284–293. https://doi.org/10.1016/j.neuroimage.2016.05.070
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Fornito A, Zalesky A, Bullmore ET (2010) Network scaling effects in graph analytic studies of human resting-state fMRI data. Front Syst Neurosci 4:1–16. https://doi.org/10.3389/fnsys.2010.00022
Fox J, Weisberg S (2019) An R companion to applied regression (third). Sage, Thousand Oaks. https://doi.org/10.1177/0049124105277200
Fraschini M, Demuru M, Crobe A, Marrosu F, Stam CJ, Hillebrand A (2016) The effect of epoch length on estimated EEG functional connectivity and brain network organisation. J Neural Eng 13(3):1–10. https://doi.org/10.1088/1741-2560/13/3/036015
Fuchs M, Wagner M, Köhler T, Wischmann HA (1999) Linear and nonlinear current density reconstructions. J Clin Neurophysiol 16(3):267–295. https://doi.org/10.1097/00004691-199905000-00006
Gramfort A, Papadopoulo T, Olivi E, Clerc M (2010) OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed Eng Online. https://doi.org/10.1186/1475-925X-8-1
Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M, Xanthopoulos P, Sakkalis V, Vanrumste B (2008) Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil. https://doi.org/10.1186/1743-0003-5-25
Grova C, Daunizeau J, Lina JM, Bénar CG, Benali H, Gotman J (2006) Evaluation of EEG localization methods using realistic simulations of interictal spikes. Neuroimage 29:734–753. https://doi.org/10.1016/j.neuroimage.2005.08.053
Gueorguieva R, Krystal JH (2004) Move over ANOVA? Progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Arch Gen Psychiatry 61:310–317
Halder T, Talwar S, Jaiswal AK, Banerjee A (2019) Quantitative evaluation in estimating sources underlying brain oscillations using current source density methods and beamformer approaches. Eneuro 6(4):1–14. https://doi.org/10.1523/ENEURO.0170-19.2019
Hämäläinen MS, Ilmoniemi RJ (1994) Inetrpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Compu 32:35–42
Hassan M, Dufor O, Merlet I, Berrou C, Wendling F (2014) EEG source connectivity analysis: from dense array recordings to brain networks. PLoS ONE. https://doi.org/10.1371/journal.pone.0105041
Hassan M, Benquet P, Biraben A, Berrou C, Dufor O, Wendling F (2015) Dynamic reorganization of functional brain networks during picture naming. Cortex 73:276–288. https://doi.org/10.1016/j.cortex.2015.08.019
Hassan M, Merlet I, Mheich A, Kabbara A, Biraben A, Nica A, Wendling F (2017) Identification of interictal epileptic networks from dense-EEG. Brain Topogr 30(1):60–76. https://doi.org/10.1007/s10548-016-0517-z
Haufe S, Ewald A (2016) A simulation framework for benchmarking EEG-based brain connectivity estimation methodologies. Brain Topogr 32(4):625–642. https://doi.org/10.1007/s10548-016-0498-y
Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biom J 50(3):346–363. https://doi.org/10.1002/bimj.200810425
Kabbara A, Falou WEL, Khalil M, Wendling F, Hassan M (2017) The dynamic functional core network of the human brain at rest. Sci Rep. https://doi.org/10.1038/s41598-017-03420-6
Klem GH, Lüders HO, Jasper HH, Elger C (1999) The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl 52:3–6
Lachaux J-P, Rodriguez E, Le Van Quyen M, Lutz A, Martinerie J, Varela FJ (2000) Studying single-trials of phase synchronous activity in the brain. Int J Bifurc Chaos 10(10):2429–2439. https://doi.org/10.1142/s0218127400001560
Lantz G, Grave de Peralta R, Spinelli L, Seeck M, Michel CM (2003) Epileptic source localization with high density EEG: how many electrodes are needed? Clin Neurophysiol 114(1):63–69. https://doi.org/10.1016/S1388-2457(02)00337-1
Lin FH, Witzel T, Ahlfors SP, Stufflebeam SM, Belliveau JW, Hämäläinen MS (2006) Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. Neuroimage 31:160–171. https://doi.org/10.1016/j.neuroimage.2005.11.054
Mheich A, Hassan M, Khalil M, Gripon V, Dufor O, Wendling F (2018) SimiNet: a novel method for quantifying brain network similarity. IEEE Trans Pattern Anal Mach Intell 40(9):2238–2249. https://doi.org/10.1109/TPAMI.2017.2750160
Mheich A, Wendling F, Hassan M (2020) Brain network similarity: methods and applications. Network Neurosci 4(3):507–527. https://doi.org/10.1162/netn_a_00133
O’Neill GC, Tewarie PK, Colclough GL, Gascoyne LE, Hunt BAE, Morris PG, Woolrich MW, Brookes MJ (2017) Measurement of dynamic task related functional networks using MEG. Neuroimage 146:667–678. https://doi.org/10.1016/j.neuroimage.2016.08.061
Oostenveld R, Fries P, Maris E, Schoffelen JM (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. https://doi.org/10.1155/2011/156869
Pascual-Marqui RD (2007) Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. arxiv: 0710.3341
R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Schelter B, Winterhalder M, Hellwig B, Guschlbauer B, Lücking CH, Timmer J (2006) Direct or indirect? Graphical models for neural oscillators. J Physiol Paris 99(1):37–46. https://doi.org/10.1016/j.jphysparis.2005.06.006
Sohrabpour A, Lu Y, Kankirawatana P, Blount J, Kim H, He B (2015) Effect of EEG electrode number on epileptic source localization in pediatric patients. Clin Neurophysiol 126:472–480. https://doi.org/10.1016/j.clinph.2014.05.038
Song J, Davey C, Poulsen C, Luu P, Turovets S, Anderson E, Li K, Tucker D (2015) EEG source localization: sensor density and head surface coverage. J Neurosci Methods 256:9–21. https://doi.org/10.1016/j.jneumeth.2015.08.015
Srinivasan R, Tucker DM, Murias M (1998) Estimating the spatial Nyquist of the human EEG. Behav Res Methods Instrum Comput 30(1):8–19. https://doi.org/10.3758/BF03209412
Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28:1178–1193. https://doi.org/10.1002/hbm.20346
Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci. https://doi.org/10.1155/2011/879716
Tait L, Özkan A, Szul MJ, Zhang J (2021) A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: performance, precision, and parcellation. Hum Brain Mapp. https://doi.org/10.1002/hbm.25578
van den Heuvel MP, de Lange SC, Zalesky A, Seguin C, Yeo BTT, Schmidt R (2017) Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: issues and recommendations. Neuroimage 152(February):437–449. https://doi.org/10.1016/j.neuroimage.2017.02.005
Vinck M, Oostenveld R, Van Wingerden M, Battaglia F, Pennartz CMA (2011) An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55(4):1548–1565. https://doi.org/10.1016/j.neuroimage.2011.01.055
Wang J, Wang L, Zang Y, Yang H, Tang H, Gong Q, Chen Z, Zhu C, He Y (2009) Parcellation-dependent small-world brain functional networks: a resting-state fmri study. Hum Brain Mapp 30:1511–1523. https://doi.org/10.1002/hbm.20623
Wang HE, Bénar CG, Quilichini PP, Friston KJ, Jirsa VK, Bernard C (2014) A systematic framework for functional connectivity measures. Front Neurosci. https://doi.org/10.3389/fnins.2014.00405
Wendling F, Ansari-Asl K, Bartolomei F, Senhadji L (2009) From EEG signals to brain connectivity: a model-based evaluation of interdependence measures. J Neurosci Methods 183:9–18. https://doi.org/10.1016/j.jneumeth.2009.04.021
Wolters CH, Anwander A, Tricoche X, Weinstein D, Koch MA, MacLeod RS (2006) Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: a simulation and visualization study using high-resolution finite element modeling. Neuroimage 30(3):813–826. https://doi.org/10.1016/j.neuroimage.2005.10.014
Xia M, Wang J, He Y (2013) BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE. https://doi.org/10.1371/journal.pone.0068910
Acknowledgements
This work was financed by the Rennes University, the Institute of Clinical Neuroscience of Rennes (project named EEGCog). Authors would also like to thank the Lebanese Association for Scientific Research (LASER) and Campus France, Programme Hubert Curien CEDRE (PROJECT No. 42257YA), for supporting this study. The authors would like to acknowledge the Lebanese National Council for Scientific Research (CNRS-L), the Agence Universitaire de la Francophonie (AUF) and the Lebanese university for granting Ms. Allouch a doctoral scholarship.
Funding
This work was funded by Rennes University, the Institute of Clinical Neurosciences of Rennes (project named EEGCog). It was also supported by the Programme Hubert Curien CEDRE (PROJECT No. 42257YA), the National Council for Scientific Research (CNRS-L) and the Agence Universitaire de la Francophonie (AUF) and the Lebanese University.
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Allouch, S., Yochum, M., Kabbara, A. et al. Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks. Brain Topogr 35, 54–65 (2022). https://doi.org/10.1007/s10548-021-00859-9
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DOI: https://doi.org/10.1007/s10548-021-00859-9