General Neuroscience; Software; EEG; tDCS; Head model; Sensitivity analysis
Abstract :
[en] Accurate electromagnetic modeling of the head of a subject is of main interest in the fields of source reconstruction and brain stimulation. Those processes rely heavily on the quality of the model and, even though the geometry of the tissues can be extracted from magnetic resonance images (MRI) or computed tomography (CT), their physical properties such as the electrical conductivity are difficult to measure with non intrusive techniques. In this paper, we propose a tool to assess the uncertainty in the model parameters, the tissue conductivity, as well as compute a parametric forward models for electroencephalography (EEG) and transcranial direct current stimulation (tDCS) current distribution.
Research center :
GIGA CRC (Cyclotron Research Center) In vivo Imaging-Aging & Memory - ULiège
Akalin Acar, Z., Acar, C. E., & Makeig, S. (2016). Simultaneous head tissue conductivity and EEG source location estimation. NeuroImage, 124, 168–180 http://link.springer.com/10.1007/s10548-012-0274-6
Akalin Acar, Z., & Makeig, S. (2013). Effects of forward model errors on EEG source localization. Brain Topography, 26, 378–396 https://linkinghub.elsevier.com/retrieve/pii/S1053811915007442
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26, 839–851 https://linkinghub.elsevier.com/retrieve/pii/S1053811905001102
Bashar, M. R., Li, Y., & Wen, P. (2010). Uncertainty and sensitivity analysis for anisotropic inhomogeneous head tissue conductivity in human head modelling. Australasian Physical & Engineering Sciences in Medicine, 33, 145–152 http://link.springer.com/10.1007/s13246-010-0015-7
Burger, H. C., & Milaan, J. B. V. (1943). Measurements of the specific resistance of the human body to direct current. Acta Medica Scandinavica, 114, 584–607. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0954-6820.1943.tb11253.x
Chen, H., Loeppky, J. L., Sacks, J., & Welch, W. J. (2016). Analysis methods for computer experiments: How to assess and what counts? Statistical Science, 31, 40–60 http://projecteuclid.org/euclid.ss/1455115913
Cho, J. H., Vorwerk, J., Wolters, C. H., & Knösche, T. R. (2015). Influence of the head model on EEG and MEG source connectivity analyses. NeuroImage, 110, 60–77 https://linkinghub.elsevier.com/retrieve/pii/S1053811915000683
Dannhauer, M., Lanfer, B., Wolters, C. H., & Knösche, T. R. (2011). Modeling of the human skull in EEG source analysis. Human Brain Mapping, 32, 1383–1399 http://doi.wiley.com/10.1002/hbm.21114
Feinberg, J., & Langtangen, H. P. (2015). Chaospy: An open source tool for designing methods of uncertainty quantification. Journal of Computational Science, 11, 46–57 https://linkinghub.elsevier.com/retrieve/pii/S1877750315300119
Fischl, B., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004). Sequence-independent segmentation of magnetic resonance images. NeuroImage, 23(Suppl 1), S69-84. 10.1016/j.neuroimage.2004.07.016
Fuchs, M., Wagner, M., & Kastner, J. (2007). Development of volume conductor and source models to localize epileptic foci. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 24, 101–119. 10.1097/WNP.0b013e318038fb3e
Gabriel, C. (1996). Compilation of the dielectric properties of body tissues at RF and microwave frequencies. Technical Report Defense Technical Information Center Fort Belvoir, VA. http://www.dtic.mil/docs/citations/ADA303903
Gabriel, C., Gabriel, S., & Corthout, E. (1996a). The dielectric properties of biological tissues: I. Literature survey. Physics in Medicine and Biology, 41, 2231–2249 https://iopscience.iop.org/article/10.1088/0031-9155/41/11/001
Gabriel, S., Lau, R. W., & Gabriel, C. (1996b). The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Physics in Medicine and Biology, 41, 2251–2269 https://iopscience.iop.org/article/10.1088/0031-9155/41/11/002
Gabriel, S., Lau, R. W., & Gabriel, C. (1996c). The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. Physics in Medicine and Biology, 41, 2271–2293 https://iopscience.iop.org/article/10.1088/0031-9155/41/11/003
Geddes, L. A., & Baker, L. E. (1967). The specific resistance of biological material-a compendium of data for the biomedical engineer and physiologist. Medical & Biological Engineering, 5, 271–293. 10.1007/BF02474537
Geuzaine, C. (2007). GetDP: a general finite-element solver for the de Rham complex. PAMM Volume 7 Issue 1. Special Issue: Sixth International Congress on Industrial Applied Mathematics (ICIAM07) and GAMM Annual Meeting, Zürich 2007, 7, https://doi.org/10.1002/pamm.200700750
Geuzaine, C., & Remacle, J. F. (2009). Gmsh: a three-dimensional finite element mesh generator with built-in pre- ad post-processing facilities. International Journal for Numerical Methods in Engineering, 79, 1309–1331. 10.1002/nme.2579.
Güllmar, D., Haueisen, J., & Reichenbach, J. R. (2010). Influence of anisotropic electrical conductivity in white matter tissue on the EEG/MEG forward and inverse solution. A high-resolution whole head simulation study. NeuroImage, 51, 145–163 https://linkinghub.elsevier.com/retrieve/pii/S1053811910001825
Goncalves, S., de Munck, J., Verbunt, J., Bijma, F., Heethaar, R., & Lopes da Silva, F. (2003). In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head. IEEE Transactions on Biomedical Engineering, 50, 754–767. Conference Name: IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2003.812164
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., et al. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7, 1–13. 10.3389/fnins.2013.00267
Grignard, M. (2021a). shamo. https://doi.org/10.5281/zenodo.4420811
Grignard, M. (2021b). shamo documentation. https://cyclotronresearchcentre.github.io/shamo/index.html
Hallez, H., Vanrumste, B., Grech, R., Muscat, J., De Clercq, W., Vergult, A., et al. (2007). Review on solving the forward problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation, 4, 46 https://jneuroengrehab.biomedcentral.com/articles/10.1186/1743-0003-4-46
Halton, J. H. (1960). On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numerische Mathematik, 2, 84–90 http://link.springer.com/10.1007/BF01386213
Haueisen, J., Ramon, C., Czapski, P., & Eiselt, M. (1995). On the influence of volume currents and extended sources on neuromagnetic fields: A simulation study. Annals of Biomedical Engineering, 23, 728–739 http://link.springer.com/10.1007/BF02584472
Haueisen, J., Ramon, C., Eiselt, M., Brauer, H., & Nowak, H. (1997). Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head. IEEE Transactions on Biomedical Engineering, 44, 727–735 http://ieeexplore.ieee.org/document/605429/
Haueisen, J., Tuch, D., Ramon, C., Schimpf, P., Wedeen, V., George, J., & Belliveau, J. (2002). The influence of brain tissue anisotropy on human EEG and MEG. NeuroImage, 15, 159–166 https://linkinghub.elsevier.com/retrieve/pii/S1053811901909620
Herman, J., & Usher, W. (2017). SALib: An open-source Python library for sensitivity snalysis. Journal of Open Source Software, 2, 97 https://joss.theoj.org/papers/10.21105/joss.00097
Huang, Y., Datta, A., Bikson, M., & Parra, L. C. (2019). Realistic volumetric-approach to simulate transcranial electric stimulation-ROAST-a fully automated open-source pipeline. Journal of Neural Engineering, 16, 056006. Publisher: IOP Publishing. https://doi.org/10.1088/1741-2552/ab208d
Iacono, M. I., Neufeld, E., Akinnagbe, E., Bower, K., Wolf, J., Vogiatzis Oikonomidis, I., et al. (2015). MIDA: A multimodal imaging-based detailed anatomical model of the human head and neck. PLOS ONE, 10, e0124126 https://dx.plos.org/10.1371/journal.pone.0124126
Jochmann, T., Güllmar, D., Haueisen, J., & Reichenbach, J. R. (2011). Influence of tissue conductivity changes on the EEG signal in the human brain A simulation study. Zeitschrift für Medizinische Physik, 21, 102–112 https://linkinghub.elsevier.com/retrieve/pii/S0939388910000966
Katoch, N., Choi, B. K., Sajib, S. Z. K., Lee, E., Kim, H. J., Kwon, O. I., & Woo, E. J. (2019). Conductivity tensor imaging of in vivo human brain and experimental validation using giant vesicle suspension. IEEE Transactions on Medical Imaging, 38, 1569–1577. Conference Name: IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2018.2884440
Lanfer, B., Scherg, M., Dannhauer, M., Knösche, T., Burger, M., & Wolters, C. (2012). Influences of skull segmentation inaccuracies on EEG source analysis. NeuroImage, 62, 418–431 https://linkinghub.elsevier.com/retrieve/pii/S1053811912004946
Latikka, J., Hyttinen, J., Kuurne, T., Eskola, H., & Malmivuo, J. (2001). The conductivity of brain tissues: comparison of results in vivo and in vitro measurements. In 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 910–912). Istanbul, Turkey: IEEE volume 1. http://ieeexplore.ieee.org/document/1019092/
Malmivuo, J., & Plonsey, R. (1995). Bioelectromagnetism: Principles and applications of bioelectric and biomagnetic fields. Oxford University Press. https://oxford.universitypressscholarship.com/view/10.1093/acprof:oso/9780195058239.001.0001/acprof-9780195058239
McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in reported human head tissue electrical conductivity values. Brain Topography, 32, 825–858 http://link.springer.com/10.1007/s10548-019-00710-2
Michel, C. M., & Brunet, D. (2019). EEG source imaging: A practical review of the analysis steps. Frontiers in Neurology, 10, 325 https://www.frontiersin.org/article/10.3389/fneur.2019.00325
Montes-Restrepo, V., van Mierlo, P., Strobbe, G., Staelens, S., Vandenberghe, S., & Hallez, H. (2014). Influence of skull modeling approaches on EEG source localization. Brain Topography, 27, 95–111 http://link.springer.com/10.1007/s10548-013-0313-y
Nielsen, J. D., Madsen, K. H., Puonti, O., Siebner, H. R., Bauer, C., Madsen, C. G., et al. (2018). Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art. NeuroImage, 174, 587–598. 10.1016/j.neuroimage.2018.03.001
Nuwer, M. R. (2018). 10–10 electrode system for EEG recording. Clinical Neurophysiology, 129, 1103 http://www.sciencedirect.com/science/article/pii/S1388245718300907
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Plonsey, R., & Heppner, D. B. (1967). Considerations of quasi-stationarity in electrophysiological systems. The Bulletin of Mathematical Biophysics, 29, 657–664. 10.1007/BF02476917
Puonti, O., Van Leemput, K., Saturnino, G. B., Siebner, H. R., Madsen, K. H., & Thielscher, A. (2020). Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling. NeuroImage, 219, 117044. https://www.sciencedirect.com/science/article/pii/S1053811920305309
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. Adaptive computation and machine learning. Cambridge, Mass: MIT Press. OCLC: ocm61285753.
Sadleir, R. J., & Argibay, A. (2007). Modeling skull electrical properties. Annals of Biomedical Engineering, 35, 1699–1712 http://link.springer.com/10.1007/s10439-007-9343-5
Sajib, S. Z. K., Jeong, W. C., Kyung, E. J., Kim, H. B., Oh, T. I., Kim, H. J., Kwon, O. I., & Woo, E. J. (2016). Experimental evaluation of electrical conductivity imaging of anisotropic brain tissues using a combination of diffusion tensor imaging and magnetic resonance electrical impedance tomography. AIP Advances, 6, 065109. Publisher: American Institute of Physics. https://aip.scitation.org/doi/full/10.1063/1.4953893
Saltelli, A. (Ed.) (2008). Global sensitivity analysis: the primer. Chichester, England; Hoboken, NJ: John Wiley. OCLC: ocn180852094.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., & Tarantola, S. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181, 259–270 https://linkinghub.elsevier.com/retrieve/pii/S0010465509003087
Saturnino, G. B., Thielscher, A., Madsen, K. H., Knösche, T. R., & Weise, K. (2019). A principled approach to conductivity uncertainty analysis in electric field calculations. NeuroImage, 188, 821–834 http://www.sciencedirect.com/science/article/pii/S1053811918322031
Schimpf, P. H. (2007). Application of quasi-static magnetic reciprocity to finite element models of the MEG lead-field. IEEE Transactions on Biomedical Engineering, 54, 2082–2088. Conference Name: IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME
Schirru, A., Pampuri, S., De Nicolao, G., & McLoone, S. (2011). Efficient marginal likelihood computation for Gaussian process regression. arXiv:1110.6546 [stat],. ArXiv: 1110.6546
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl 1), S208-219. 10.1016/j.neuroimage.2004.07.051
Sobol, I. (1967). On the distribution of points in a cube and the approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics, 7, 86–112 https://linkinghub.elsevier.com/retrieve/pii/0041555367901449
Sobol, I. (1976). Uniformly distributed sequences with an additional uniform property. USSR Computational Mathematics and Mathematical Physics, 16, 236–242 https://linkinghub.elsevier.com/retrieve/pii/0041555376901543
Sobol, I. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55, 271–280 https://linkinghub.elsevier.com/retrieve/pii/S0378475400002706
Taberna, G. A., Samogin, J., & Mantini, D. (2021). Automated head tissue modelling based on structural magnetic resonance images for electroencephalographic source reconstruction. Neuroinformatics. http://link.springer.com/10.1007/s12021-020-09504-5
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, e879716. Publisher: Hindawi. https://www.hindawi.com/journals/cin/2011/879716/
The CGAL Project (2020). CGAL user and reference manual. (5th ed.). CGAL Editorial Board. https://doc.cgal.org/5.0.3/Manual/packages.html
Thielscher, A., Antunes, A., & Saturnino, G. B. (2015). Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS? 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 222–225). Milan: IEEE http://ieeexplore.ieee.org/document/7318340/
Tuch, D. S., Wedeen, V. J., Dale, A. M., George, J. S., & Belliveau, J. W. (2001). Conductivity tensor mapping of the human brain using diffusion tensor MRI. Proceedings of the National Academy of Sciences, 98, 11697–11701 http://www.pnas.org/cgi/doi/10.1073/pnas.171473898
Vallaghe, S., & Clerc, M. (2009). A global sensitivity analysis of three- and four-layer EEG conductivity models. IEEE Transactions on Biomedical Engineering, 56, 988–995. Conference Name: IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2008.2009315
Vorwerk, J., Aydin, U., Wolters, C., & Butson, C. (2019). Influence of head tissue conductivity uncertainties on EEG dipole reconstruction. Frontiers in Neuroscience, 13. https://doi.org/10.1186/s12938-018-0463-y
Vorwerk, J., Oostenveld, R., Piastra, M. C., Magyari, L., & Wolters, C. H. (2018). The FieldTrip-SimBio pipeline for EEG forward solutions. BioMedical Engineering Online, 17, 37. 10.1186/s12938-018-0463-y
Weinstein, D., Zhukov, L., & Johnson, C. (2000). Lead field basis for FEM source localization. Annals of Biomedical Engineering, 28, 1059–1065. 10.1114/1.1310220.
Windhoff, M., Opitz, A., & Thielscher, A. (2013). Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models. Human Brain Mapping, 34, 923–935. https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.21479
Wolters, C., Anwander, A., Tricoche, X., Weinstein, D., Koch, M., & MacLeod, R. (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, 813–826 https://linkinghub.elsevier.com/retrieve/pii/S1053811905007871
Wu, Z., Liu, Y., Hong, M., & Yu, X. (2018). A review of anisotropic conductivity models of brain white matter based on diffusion tensor imaging. Medical & Biological Engineering & Computing, 56, 1325–1332 http://link.springer.com/10.1007/s11517-018-1845-9
Ziegler, E., Chellappa, S. L., Gaggioni, G., Ly, J. Q., Vandewalle, G., André, E., et al. (2014). A finite-element reciprocity solution for EEG forward modeling with realistic individual head models. NeuroImage, 103, 542–551 https://linkinghub.elsevier.com/retrieve/pii/S1053811914007307
