Top

Journal of Computational Neuroscience

Published in:

11-07-2019

Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study

Authors: C . G. Bénar, C. Grova, V. K. Jirsa, J. M. Lina

Published in: Journal of Computational Neuroscience | Issue 1/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Electrophysiological signals (electroencephalography, EEG, and magnetoencephalography, MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with different sizes (ranging from 0.4 to 104.2 cm²). Small patches were attributed signals of high frequencies, whereas large patches were associated with signals of low frequencies, on a logarithmic scale. The tested parameters included i) the space/frequency structure (range of patch sizes and frequencies) and ii) the amplitude factor c parametrizing the spatial scale ratios. We found that the space/frequency structure may cause differences between EEG and MEG scale-free spectra that are compatible with real data findings reported in previous studies. We also found that below a certain spatial scale, there were no more differences between EEG and MEG, suggesting a limit for the resolution of both methods.Our work provides an explanation of experimental findings. This does not rule out other mechanisms for differences between EEG and MEG, but suggests an important role of spatio-temporal structure of neural dynamics. This can help the analysis and interpretation of power-law measures in EEG and MEG, and we believe our results can also impact computational modeling of brain dynamics, where different local connectivity structures could be used at different frequencies.

previous article How to correctly quantify neuronal phase-response curves from noisy recordings

next article Modeling grid fields instead of modeling grid cells

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

In a purely resistive medium, the propagation of electromagnetic fields only depends on the electrical resistance of the different components (here, brain, skull, CSF, scalp…). Importantly, in this case, there is no dependency of the observed fields on frequency. In other words, there is no filtering effect of the tissues, in contrast with non-resistive tissues where signals may be attenuated at high frequencies.

Ahlfors, S. P., Han, J., Lin, F. H., Witzel, T., Belliveau, J. W., Hamalainen, M. S., et al. (2010). Cancellation of EEG and MEG signals generated by extended and distributed sources. Human Brain Mapping, 31(1), 140–149. https://doi.org/10.1002/hbm.20851.CrossRefPubMedPubMedCentral

Atasoy, S., Donnelly, I., & Pearson, J. (2016). Human brain networks function in connectome-specific harmonic waves. Nature Communications, 7, 10340. https://doi.org/10.1038/ncomms10340.CrossRefPubMedPubMedCentral

Bangera, N. B., Schomer, D. L., Dehghani, N., Ulbert, I., Cash, S., Papavasiliou, S., Eisenberg, S. R., Dale, A. M., & Halgren, E. (2010). Experimental validation of the influence of white matter anisotropy on the intracranial EEG forward solution. Journal of Computational Neuroscience, 29(3), 371–387. https://doi.org/10.1007/s10827-009-0205-z.CrossRefPubMedPubMedCentral

Bullock, T. H., & McClune, M. C. (1989). Lateral coherence of the electrocorticogram: A new measure of brain synchrony. Electroencephalography and Clinical Neurophysiology, 73(6), 479–498.CrossRefPubMed

Bullock, T. H., McClune, M. C., Achimowicz, J. Z., Iragui-Madoz, V. J., Duckrow, R. B., & Spencer, S. S. (1995). EEG coherence has structure in the millimeter domain: Subdural and hippocampal recordings from epileptic patients. Electroencephalography and Clinical Neurophysiology, 95(3), 161–177.CrossRefPubMed

Buzsaki, G. (2006). Rhythms of the brain.

Buzsaki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes. Nature Reviews. Neuroscience, 13(6), 407–420. https://doi.org/10.1038/nrn3241.CrossRefPubMedPubMedCentral

Ciuciu, P., Abry, P., & He, B. J. (2014). Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks. Neuroimage, 95, 248–263. https://doi.org/10.1016/j.neuroimage.2014.03.047.CrossRefPubMedPubMedCentral

Cointepas, Y., Geffroy, D., Souedet, N., Denghien, I., & Rivière, D. (2010). The BrainVISA project: A shared software development infrastructure for biomedical imaging research.

Cosandier-Rimélé, D., Badier, J. M., Chauvel, P., & Wendling, F. (2007). Modeling and interpretation of scalp-EEG and depth-EEG signals during interictal activity. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, 4277–4280. https://doi.org/10.1109/iembs.2007.4353281.CrossRef

Dehghani, N., Bedard, C., Cash, S. S., Halgren, E., & Destexhe, A. (2010). Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media. Journal of Computational Neuroscience, 29(3), 405–421. https://doi.org/10.1007/s10827-010-0263-2.CrossRefPubMedPubMedCentral

Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009.CrossRef

Destexhe, A., Contreras, D., & Steriade, M. (1999). Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. The Journal of Neuroscience, 19(11), 4595–4608.CrossRefPubMedPubMedCentral

Freeman, W. J. (2005). A field-theoretic approach to understanding scale-free neocortical dynamics. Biological Cybernetics, 92(6), 350–359. https://doi.org/10.1007/s00422-005-0563-1.CrossRefPubMed

Gavaret, M., Badier, J. M., Bartolomei, F., Benar, C. G., & Chauvel, P. (2014). MEG and EEG sensitivity in a case of medial occipital epilepsy. Brain Topography, 27(1), 192–196. https://doi.org/10.1007/s10548-013-0317-7.CrossRefPubMed

Gramfort, A., Papadopoulo, T., Olivi, E., & Clerc, M. (2010). OpenMEEG: Opensource software for quasistatic bioelectromagnetics. Biomedical Engineering Online, 9, 45. https://doi.org/10.1186/1475-925X-9-45. CrossRefPubMedPubMedCentral

Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993). Magnetoencephalography—Theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65, 414–497.CrossRef

He, B. J. (2014). Scale-free brain activity: Past, present, and future. Trends in Cognitive Sciences, 18(9), 480–487. https://doi.org/10.1016/j.tics.2014.04.003.CrossRefPubMedPubMedCentral

Jirsa, V. K. (2009). Neural field dynamics with local and global connectivity and time delay. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 367(1891), 1131–1143. https://doi.org/10.1098/rsta.2008.0260.CrossRefPubMed

Khodagholy, D., Doublet, T., Gurfinkel, M., Quilichini, P., Ismailova, E., Leleux, P., Herve, T., Sanaur, S., Bernard, C., & Malliaras, G. G. (2011). Highly conformable conducting polymer electrodes for in vivo recordings. Advanced Materials, 23(36), H268–H272. https://doi.org/10.1002/adma.201102378.CrossRefPubMed

Miller, K. J., Sorensen, L. B., Ojemann, J. G., & den Nijs, M. (2009). Power-law scaling in the brain surface electric potential. PLoS Computational Biology, 5(12), e1000609. https://doi.org/10.1371/journal.pcbi.1000609.CrossRefPubMedPubMedCentral

Muthukumaraswamy, S. D., & Singh, K. D. (2013). Visual gamma oscillations: The effects of stimulus type, visual field coverage and stimulus motion on MEG and EEG recordings. Neuroimage, 69, 223–230. https://doi.org/10.1016/j.neuroimage.2012.12.038.CrossRefPubMed

Nelson, M. J., & Pouget, P. (2012). Physical model of coherent potentials measured with different electrode recording site sizes. Journal of Neurophysiology, 107(5), 1291–1300. https://doi.org/10.1152/jn.00177.2011.CrossRefPubMed

Nunez, P., & Srinivasan, R. (2005). Electric fields of the brain: The Neurophysics of EEG. Oxford: Oxford University Press.

Nunez, P. L. (2000). Toward a quantitative description of large-scale neocortical dynamic function and EEG. Behavioral and Brain Sciences, 23(3), 371–37+. https://doi.org/10.1017/S0140525x00003253.CrossRefPubMed

Pesaran, B., Vinck, M., Einevoll, G. T., Sirota, A., Fries, P., Siegel, M., Truccolo, W., Schroeder, C. E., & Srinivasan, R. (2018). Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation. Nature Neuroscience, 21(7), 903–919. https://doi.org/10.1038/s41593-018-0171-8.CrossRefPubMed

Pfurtscheller, G., & Cooper, R. (1975). Frequency dependence of the transmission of the EEG from cortex to scalp. Electroencephalography and Clinical Neurophysiology, 38(1), 93–96.CrossRefPubMed

Roehri, N., Lina, J. M., Mosher, J. C., Bartolomei, F., & Benar, C. G. (2016). Time-frequency strategies for increasing high frequency oscillation detectability in intracerebral EEG. IEEE Transactions on Biomedical Engineering, 63(99), 1–2606. https://doi.org/10.1109/TBME.2016.2556425.CrossRef

Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015). Mathematical framework for large-scale brain network modeling in the virtual brain. Neuroimage, 111, 385–430. https://doi.org/10.1016/j.neuroimage.2015.01.002.CrossRefPubMed

Sanz Leon, P., Knock, S. A., Woodman, M. M., Domide, L., Mersmann, J., McIntosh, A. R., & Jirsa, V. (2013). The virtual brain: A simulator of primate brain network dynamics. Frontiers in Neuroinformatics, 7, 10. https://doi.org/10.3389/fninf.2013.00010.CrossRefPubMedPubMedCentral

Srinivasan, R., Winter, W. R., Ding, J., & Nunez, P. L. (2007). EEG and MEG coherence: Measures of functional connectivity at distinct spatial scales of neocortical dynamics. Journal of Neuroscience Methods, 166(1), 41–52. https://doi.org/10.1016/j.jneumeth.2007.06.026.CrossRefPubMedPubMedCentral

Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011). Brainstorm: A user-friendly application for MEG/EEG analysis. Computational Intelligence and Neuroscience, 2011, 879716. https://doi.org/10.1155/2011/879716.CrossRefPubMedPubMedCentral

von Ellenrieder, N., Dan, J., Frauscher, B., & Gotman, J. (2016). Sparse asynchronous cortical generators can produce measurable scalp EEG signals. Neuroimage, 138, 123–133. https://doi.org/10.1016/j.neuroimage.2016.05.067.CrossRef

von Stein, A., & Sarnthein, J. (2000). Different frequencies for different scales of cortical integration: From local gamma to long range alpha/theta synchronization. International Journal of Psychophysiology, 38(3), 301–313.CrossRef

Wirsich, J., Perry, A., Ridley, B., Proix, T., Golos, M., Benar, C., et al. (2016). Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy. Neuroimage Clin, 11, 707–718. https://doi.org/10.1016/j.nicl.2016.05.010.CrossRefPubMedPubMedCentral

Title: Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study
Authors: C . G. Bénar
C. Grova
V. K. Jirsa
J. M. Lina
Publication date: 11-07-2019
Publisher: Springer US
Published in: Journal of Computational Neuroscience / Issue 1/2019
Print ISSN: 0929-5313
Electronic ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-019-00721-9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 1/2019

Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries

Modeling grid fields instead of modeling grid cells

Biophysically interpretable inference of single neuron dynamics

Electrodiffusion models of synaptic potentials in dendritic spines

How to correctly quantify neuronal phase-response curves from noisy recordings

Premium Partner