Forward and Inverse Effects of the Complete Electrode Model in Neonatal EEG

Overview

Journal J Neurophysiol

Publisher American Physiological Society

Specialties Neurology
Physiology

Date 2016 Nov 18

PMID 27852731

Citations 5

Authors

S Pursiainen

S Lew

C H Wolters

Affiliations

Soon will be listed here.

Abstract

This paper investigates finite element method-based modeling in the context of neonatal electroencephalography (EEG). In particular, the focus lies on electrode boundary conditions. We compare the complete electrode model (CEM) with the point electrode model (PEM), which is the current standard in EEG. In the CEM, the voltage experienced by an electrode is modeled more realistically as the integral average of the potential distribution over its contact surface, whereas the PEM relies on a point value. Consequently, the CEM takes into account the subelectrode shunting currents, which are absent in the PEM. In this study, we aim to find out how the electrode voltage predicted by these two models differ, if standard size electrodes are attached to a head of a neonate. Additionally, we study voltages and voltage variation on electrode surfaces with two source locations: ) next to the C6 electrode and ) directly under the Fz electrode and the frontal fontanel. A realistic model of a neonatal head, including a skull with fontanels and sutures, is used. Based on the results, the forward simulation differences between CEM and PEM are in general small, but significant outliers can occur in the vicinity of the electrodes. The CEM can be considered as an integral part of the outer head model. The outcome of this study helps understanding volume conduction of neonatal EEG, since it enlightens the role of advanced skull and electrode modeling in forward and inverse computations. The effect of the complete electrode model on electroencephalography forward and inverse computations is explored. A realistic neonatal head model, including a skull structure with fontanels and sutures, is used. The electrode and skull modeling differences are analyzed and compared with each other. The results suggest that the complete electrode model can be considered as an integral part of the outer head model. To achieve optimal source localization results, accurate electrode modeling might be necessary.

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References

Ollikainen J, Vauhkonen M, Karjalainen P, Kaipio J . Effects of electrode properties on EEG measurements and a related inverse problem. Med Eng Phys. 2001; 22(8):535-45. DOI: 10.1016/s1350-4533(00)00070-9. View

Breitling C, Zaehle T, Dannhauer M, Bonath B, Tegelbeckers J, Flechtner H . Improving Interference Control in ADHD Patients with Transcranial Direct Current Stimulation (tDCS). Front Cell Neurosci. 2016; 10:72. PMC: 4834583. DOI: 10.3389/fncel.2016.00072. View

Gargiulo P, Belfiore P, Fridgeirsson E, Vanhatalo S, Ramon C . The effect of fontanel on scalp EEG potentials in the neonate. Clin Neurophysiol. 2015; 126(9):1703-10. DOI: 10.1016/j.clinph.2014.12.002. View

Dannhauer M, Lanfer B, Wolters C, Knosche T . Modeling of the human skull in EEG source analysis. Hum Brain Mapp. 2010; 32(9):1383-99. PMC: 6869856. DOI: 10.1002/hbm.21114. View

Acar Z, Makeig S . Neuroelectromagnetic forward head modeling toolbox. J Neurosci Methods. 2010; 190(2):258-70. PMC: 4126205. DOI: 10.1016/j.jneumeth.2010.04.031. View

Uutela K, Hamalainen M, Somersalo E . Visualization of magnetoencephalographic data using minimum current estimates. Neuroimage. 1999; 10(2):173-80. DOI: 10.1006/nimg.1999.0454. View

Stenroos M, Sarvas J . Bioelectromagnetic forward problem: isolated source approach revis(it)ed. Phys Med Biol. 2012; 57(11):3517-35. DOI: 10.1088/0031-9155/57/11/3517. View

Guler S, Dannhauer M, Erem B, MacLeod R, Tucker D, Turovets S . Optimization of focality and direction in dense electrode array transcranial direct current stimulation (tDCS). J Neural Eng. 2016; 13(3):036020. PMC: 5198846. DOI: 10.1088/1741-2560/13/3/036020. View

Dale A, Fischl B, Sereno M . Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999; 9(2):179-94. DOI: 10.1006/nimg.1998.0395. View

10.

Awada K, Jackson D, Williams J, Wilton D, Baumann S, Papanicolaou A . Computational aspects of finite element modeling in EEG source localization. IEEE Trans Biomed Eng. 1997; 44(8):736-52. DOI: 10.1109/10.605431. View

11.

Liehr M, Haueisen J . Influence of anisotropic compartments on magnetic field and electric potential distributions generated by artificial current dipoles inside a torso phantom. Phys Med Biol. 2008; 53(1):245-54. DOI: 10.1088/0031-9155/53/1/017. View

12.

Yan Y, Nunez P, Hart R . Finite-element model of the human head: scalp potentials due to dipole sources. Med Biol Eng Comput. 1991; 29(5):475-81. DOI: 10.1007/BF02442317. View

13.

Cook M, Koles Z . A high-resolution anisotropic finite-volume head model for EEG source analysis. Conf Proc IEEE Eng Med Biol Soc. 2007; 2006:4536-9. DOI: 10.1109/IEMBS.2006.260314. View

14.

Lucka F, Pursiainen S, Burger M, Wolters C . Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents. Neuroimage. 2012; 61(4):1364-82. DOI: 10.1016/j.neuroimage.2012.04.017. View

15.

Dannhauer M, Brooks D, Tucker D, MacLeod R . A pipeline for the simulation of transcranial direct current stimulation for realistic human head models using SCIRun/BioMesh3D. Annu Int Conf IEEE Eng Med Biol Soc. 2013; 2012:5486-9. PMC: 3651514. DOI: 10.1109/EMBC.2012.6347236. View

16.

Gramfort A, Papadopoulo T, Olivi E, Clerc M . Forward field computation with OpenMEEG. Comput Intell Neurosci. 2011; 2011:923703. PMC: 3061324. DOI: 10.1155/2011/923703. View

17.

Wetterling F, Liehr M, Schimpf P, Liu H, Haueisen J . The localization of focal heart activity via body surface potential measurements: tests in a heterogeneous torso phantom. Phys Med Biol. 2009; 54(18):5395-409. DOI: 10.1088/0031-9155/54/18/003. View

18.

Montes-Restrepo V, van Mierlo P, Strobbe G, Staelens S, Vandenberghe S, Hallez H . Influence of skull modeling approaches on EEG source localization. Brain Topogr. 2013; 27(1):95-111. DOI: 10.1007/s10548-013-0313-y. View

19.

Guler S, Dannhauer M, Erem B, MacLeod R, Tucker D, Turovets S . Optimizing Stimulus Patterns for Dense Array tDCS With Fewer Sources Than Electrodes Using A branch and Bound Algorithm. Proc IEEE Int Symp Biomed Imaging. 2017; 2016:229-232. PMC: 5417550. DOI: 10.1109/ISBI.2016.7493251. View

20.

Baumann S, Wozny D, Kelly S, Meno F . The electrical conductivity of human cerebrospinal fluid at body temperature. IEEE Trans Biomed Eng. 1997; 44(3):220-3. DOI: 10.1109/10.554770. View