Skip to main content
Log in

Modeling Skull Electrical Properties

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

Accurate representations and measurements of skull electrical conductivity are essential in developing appropriate forward models for applications such as inverse EEG or Electrical Impedance Tomography of the head. Because of its layered structure, it is often assumed that skull is anisotropic, with an anisotropy ratio around 10. However, no detailed investigation of skull anisotropy has been performed. In this paper we investigate four-electrode measurements of conductivities and their relation to tissue anisotropy ratio (ratio of tangential to radial conductivity) in layered or anisotropic biological samples similar to bone. It is shown here that typical values for the thicknesses and radial conductivities of individual skull layers produce tissue with much smaller anisotropy ratios than 10. Moreover, we show that there are very significant differences between the field patterns formed in a three-layered isotropic structure plausible for bone, and those formed assuming that bone is homogeneous and anisotropic. We performed a measurement of conductivity using an electrode configuration sensitive to the distinction between three-layered and homogeneous anisotropic composition and found results consistent with the sample being three-layered. We recommend that the skull be more appropriately represented as three isotropic layers than as homogeneous and anisotropic.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

References

  1. Agilent Technologies Co. Ltd. The Impedance Measurement Handbook, Agilent Technologies, 2003.

  2. Akhtari M., Bryant H. C., Mamelak A. N., Heller L., Shih J. J., Mandelkern M., Matlachov A., Ranken D. M., Best E. D., Sutherling W. W. (2000) Conductivities of three-layer human skull. Brain Topogr. 13:29–42

    Article  PubMed  CAS  Google Scholar 

  3. Akhtari M., Bryant H. C., Mamelak A. N., Flynn E. R., Heller L., Shih J. J., Mandelkern M., Matlachov A., Ranken D. M., Dimauro M. A., Lee R. R., Sutherling W. W. (2002) Conductivities of three-layer live human skull. Brain Topogr. 14:151–167

    Article  PubMed  CAS  Google Scholar 

  4. Anwander, A., C. H. Wolters, M. Dumpelmann, and T. Knosche. Influence of realistic skull and white matter anisotropy on the inverse problem in EEG/MEG source localization. 13th International Conference on Biomagnetism, Jena Germany 2002 (BIOMAG 2002).

  5. van den Broek S. P., Reinders F., Donderwinkel M., Peters M. J. (1998) Volume conduction effects in EEG and MEG. Electroenceph. Clin. Neurophysiol. 106:522–534

    Article  PubMed  Google Scholar 

  6. Cohen D., Cuffin B. N. (1983) Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electroenceph. Clin. Neurophysiol. 56:38–51

    Article  PubMed  CAS  Google Scholar 

  7. Cuffin B. N. (1993) Effects of local variations in skull and scalp thickness on EEGs and MEGs. IEEE Trans. Biomed. Eng. 40:42–48

    Article  PubMed  CAS  Google Scholar 

  8. Einziger P. D., Livshitz L. M., Mizrahi J. (2002) Rigorous image-series expansions of quasi-static Green’s functions for regions with planar stratification. IEEE Trans. Antenn. Propag. 50:1813–1823

    Article  Google Scholar 

  9. Goncalves S., de Munck J. C., Heethaar R. M., Lopes Da Silva F. H., Van Dijk B. W. (2000) The application of electrical impedance tomography to reduce systematic errors in the EEG inverse problem—a simulation study. Physiol. Meas. 21:379–393

    Article  PubMed  CAS  Google Scholar 

  10. Goncalves S., De Munck J., Verbunt J. P. A., Bijma F., Heethaar R. M., Lopes Da Silva F. H. (2003a) In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head. IEEE Trans. Biomed. Eng. 50:754–767

    Article  PubMed  Google Scholar 

  11. Goncalves S., de Munck J., Verbunt J. P. A., Heethaar R. M., Lopes Da Silva F. H. (2003b) In vivo measurement of the brain and skull resistivities using an EIT-based method and the combined analysis of SEF/SEP data. IEEE Trans. Biomed. Eng. 50:1124–1128

    Article  PubMed  CAS  Google Scholar 

  12. Hallez H., Vanrumste B., van Hese P., D’Asseler Y., Lemahieu I., van de Walle R. (2005) A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization. Phys. Med. Biol. 50:3787–3806

    Article  PubMed  Google Scholar 

  13. 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 Trans. Biomed. Eng. 44:727–735

    Article  PubMed  CAS  Google Scholar 

  14. Hoekema R., Wieneke G. H., Leijten F. S. S., van Veelen C. W. M., van Rijen P. C., Huiskamp G. J. M., Ansems J., van Huffelen A. C. (2003) Measurement of the conductivity of skull, temporarily removed during epilepsy surgery. Brain Topogr. 16:29–38

    Article  PubMed  CAS  Google Scholar 

  15. Holdefer R. N., Sadleir R. J., Russell M. J. (2006) Predicted current densities in the brain during transcranial electrical stimulation. Clin. Neurophysiol. 117:1388–1397

    Article  PubMed  CAS  Google Scholar 

  16. Kun S., Peura R. (2000) Effects of sample geometry and electrode configuration on measured electrical resistivity of skeletal muscle. IEEE Trans. Biomed. Eng. 47:163–169

    Article  PubMed  CAS  Google Scholar 

  17. Law S. K. (1993) Thickness and resistivity variations over the upper surface of the human skull. Brain Topogr. 6:99–109

    Article  PubMed  CAS  Google Scholar 

  18. Livshitz L. M., Einziger P. D., Mizrahi J. (2000) Current distribution in skeletal muscle activated by functional electrical stimulation: image-series formulation and isometric recruitment curve. Ann. Biomed. Eng. 28:1218–1228

    Article  PubMed  CAS  Google Scholar 

  19. Malmivuo J. A., Suihko V. E. (2004) Effect of skull resistivity of the spatial resolutions of EEG and MEG. IEEE Trans. Biomed. Eng. 51:1276–1280

    Article  PubMed  Google Scholar 

  20. Marin G., Guerin C., Baillet S., Garnero L., Meunier G. (1998) Influence of skull anisotropy for the forward and inverse problem in EEG: simulation studies using FEM on realistic head models. Human Brain Mapp. 6:250–269

    Article  CAS  Google Scholar 

  21. Ollikainen J. O., Vauhkonen M., Karjalainen P. A., Kaipio J. (1999) Effects of local skull inhomogeneities on EEG source estimation. Med. Eng. Phys. 21:143–154

    Article  PubMed  CAS  Google Scholar 

  22. Oostendorp, T. F., J. Delbeke, and D. F. Stegeman. The conductivity of the human skull: results of in vivo and in vitro measurements. IEEE Trans. Biomed. Eng. 47:1487–1492, 2000.

    Article  PubMed  CAS  Google Scholar 

  23. Peters M. J., de Munck J. (1991) The influence of model parameters on the inverse solution based on MEGs and EEGs. Acta Otolaryngol. (Stock), Suppl. 491:61–69

    CAS  Google Scholar 

  24. Pohlmeier R., Buchner H., Knoll G., Rienacker A., Beckmann R., Pesch J. (1997) The influence of skull—conductivity misspecification on inverse source localization in realistically shaped finite element head models. Brain Topogr. 9:157–162

    Article  PubMed  CAS  Google Scholar 

  25. Ramon C., Haueisen J., Schimpf P. H. (2006a) Influence of head models on neuromagnetic fields and inverse source localizations. BioMed. Eng. OnLine 55:55

    Article  Google Scholar 

  26. Ramon C., Schimpf P. H., Haueisen J. (2006b) Influence of head models on EEG simulations and inverse source localizations. BioMed. Eng. OnLine 5:10

    Article  PubMed  Google Scholar 

  27. Roth B. J., Ko D., Von Albertini-Carletti I. R., Scaffidi D., Sato S. (1997) Dipole localization in patients with epilepsy using the realistically shaped head model. Electroenceph. Clin. Neurophysiol. 102:159–166

    Article  PubMed  CAS  Google Scholar 

  28. Rush S. (1962) Methods of measuring the resistivities of anisotropic conducting media in situ. J. Res. Nat. Bureau Stand.-C. Eng. Instrument. 66C:217–222

    Google Scholar 

  29. Rush S., Driscoll D. A. (1968) Current Distribution in the brain from surface electrodes. Anesth. Analg. 47:717–723

    Article  PubMed  CAS  Google Scholar 

  30. Ryynänen O., Hyttinen J., Malmivuo J. (2005) Effect of skull resistivity and measurement noise on the spatial resolution of EEG. Int. J. Bioelectromag. 7:317–320

    Google Scholar 

  31. Wikswo J. P., Gevins A., Williamson S. J. (1993) The future of the EEG and MEG. Electroenceph. Clin. Neurophysiol. 87:1–9

    Article  PubMed  Google Scholar 

  32. Wolters, C. H., A. Anwander, M. A. Koch, S. Reitzinger, M. Kuhn, and M. Svensen. Influence of head tissue conductivity anisotropy on human EEG an MEG using fast high resolution finite element modeling, based on a parallel algebraic multigrid solver. In: Forschung und Wissenschaftliches Rechnen "Contributions to the Heinz-Billing Award" edited by T. Plesser and V. Macho. Gottingen, Gesellschaft fur Wissenschaftliche Datenverarbeitung, 2001.

  33. Wolters C. H., Anwander A., Tricoche X., Weinstein D., Koch M. A., Macleod R. S. (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

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgment

This work was supported by NIH grant RO1EB-002389 to RJS.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. J. Sadleir.

Appendices

Appendix A

Assume a brick-shaped structure such as that shown in Fig. A, consisting of two layers with respective conductivities σ1 and σ2 (or an arbitrary number of layers each one having conductivity either σ1 or σ2, for example the three layer structure shown in Fig. 2a). The resistances observed in response to homogeneous electric fields applied in the radial and tangential directions, respectively, are

$$ R_r =\frac{t_1 }{\sigma_1 lw}+\frac{t_2 }{\sigma_2 lw}\hbox{ and} $$
(A.1)
$$ R_t =\frac{l}{w}\frac{1}{\sigma_2 t_2 +\sigma_1 t_1 }.$$
(A.2)
Figure A
figure 11

Brick-shaped structure with width w, length l and total thickness T, composed of two layers having conductivity σ1 and thickness t 1, and conductivity σ2 and thickness t 2, respectively. Homogeneous fields are applied through the faces with dimension l x w to obtain radial resistances and through faces having dimension w  x  T to obtain tangential resistances

We also have that

$$ k=\frac{\sigma_t }{\sigma_l }=\frac{R_l }{R_t }\frac{l^{2}}{T^{2}} $$
(A.3)

where T = t 1t 2, since \(\sigma_t =\frac{1}{\rho_t}=\frac{l}{TwR_t}\hbox{ and }\sigma_r =\frac{1}{\rho_r }=\frac{T}{lwR_r}\) Combining Eqs. (A.1), (A.2) and (A.3), we obtain

$$ k=\frac{\sigma_t }{\sigma_r }=\left( \frac{1}{\sigma_1 }\left( 1-\frac{1}{\alpha_t +1} \right)+\frac{1}{\left( \alpha_t +1 \right)\sigma_2} \right)\left( \frac{\sigma_2}{\alpha_t +1}+\sigma_1 \left( 1-\frac{1}{\alpha_t +1} \right) \right) $$
(A.4)

where we have substituted \(\alpha_t =\frac{t_1 }{t_2 }\) .

Taking the derivative of (A.4) with respect to α t , we find a maximum at α t = 1, or that the maximum value of k is at

$$ \begin{aligned} k_{\rm max } &=\left( \frac{1}{2\sigma_1 }+\frac{1}{2\sigma_2} \right)\left( \frac{\sigma_2}{2}+\frac{\sigma_1}{2}\right) \\ &=\frac{1}{4}\left( \frac{\sigma_2 }{\sigma_1 }+\frac{\sigma_1}{\sigma_2 }+2 \right)=\frac{1}{4\sigma_1 \sigma_2}\left( \sigma_1^2 +\sigma_2^2 +2\sigma_1 \sigma_2 \right)=\frac{\left( \sigma_1 +\sigma_2 \right)^{2}}{4\sigma_1 \sigma_2} \end{aligned} $$
(A.5)

Appendix B

Analytic expressions describing the dependence of diagonal and orthogonal resistivity or conductivity calculations on anisotropy, electrode separation and sample thickness can be derived using Livshitz et al.18 in conjunction with variable spatial scaling to account for anisotropy, similar to that described in Rush.28 The geometry used by Livshitz is shown in Fig. B.

Figure B
figure 12

Configuration for single semi-infinite slab observations, after Livshitz et al.18 Conductivities σ1 and σ3 are zero. σ2 may be anisotropic. Sources (x,y,z′) are positioned at A and D for diagonal observations, and voltage differences are calculated between B and C. For orthogonal observations, sources are positioned at A and C and voltage differences are calculated between B and D

The voltage within the a single semi-infinite slab (region 2) is described by

$$ V_2 \left( z,r \right)=\beta \mathop{\int}\limits_0^\infty {T_1 \left( \lambda \right)T_2 \left( \lambda \right)\left\{ e^{-\lambda \left| z-{z}^{\prime} \right|}+R_i \left( \lambda \right)e^{\lambda \left( z+{z}^{\prime} \right)} \right\}} J_o \left( {\lambda r} \right)d\lambda $$
(B.1)

where J 0 is the zeroth order Bessel function, and β = I/4π σ1 . The parameters z′ and r are the z coordinates of the source, and the xy distance between source and observation point respectively.

The functions T 1 and T 2 are transmission functions across the upper and lower boundaries, and are defined as

$$ T_i \left( \lambda \right)=\frac{1+K_{i-1}}{1+K_{i-1} R_i \left( \lambda \right)e^{2\lambda z_{i-1}}} $$
(B.2)

and

$$ R_i \left( \lambda \right)=\frac{K_i +R_{i+1} e^{2\lambda z_i}}{1+K_i R_{i+1} \left( \lambda \right)e^{2\lambda z_{i-1} }}e^{-2\lambda z_i} $$
(B.3)

The parameters K i are defined as

$$ K_i =\frac{\sigma_i -\sigma_{i+1} }{\sigma_i +\sigma_{i+1} }\hbox{ and }K_0 =0 $$
(B.4)

Therefore, choosing z 1 = 0 and z 2 = h obtains

$$ \begin{array}{ll} {K_1 =-1}& {K_2 =1} \\ {R_2 =e^{-2\lambda h}}& {R_1 =K_1 +\frac{\left( 1-K_1 \right)\left( 1+K_1 \right)e^{-2\lambda h}}{1+K_1 e^{-2\lambda h}}} \\ {T_1 =1}& {T_2 =\frac{1+K_1 }{1+K_1 e^{-2\lambda h}}} \\ \end{array} $$
(B.5)

because σ1 =  0 (the conductivity outside the slab), the relation (from Eq. B.4) is used to calculate β via

$$ \frac{1+K_1}{\sigma_1}=\frac{1-K_1}{\sigma_2} ;\,\mathop {\lim}\limits_{\sigma_1 \rightarrow 0} \frac{1+K_1 }{\sigma_1 }=\frac{2}{\sigma_2 } $$
(B.6)

Then, using the Weber–Lipschitz integral,

$$ \mathop{\int}\limits_0^\infty {e^{-\lambda \left| z \right|}J_0 \left( \lambda r \right)} d\lambda =\left( r^{2}+z^{2} \right)^{-\frac{1}{2}} $$
(B.7)

expanding the expression for R 1 as a Taylor Series in K 1 e −2λh and incorporating anisotropic scaling, we obtain the expression

$$ V_a =\frac{I}{2\pi \left( {\sigma_r \sigma_t } \right)^{1/2}}\left[ \begin{array}{l} \left( \alpha ^{2}r^{2}+\left| {z-{z}^{\prime}} \right|^{2} \right)^{-1/2}+\left( \alpha ^{2}r^{2}+\left( 2\left(-z-{z}^{\prime} \right)+h \right)^{2} \right)^{-1/2} \\ +\mathop{\sum}\limits_{m=1}^\infty {\left( \alpha ^{2}r^{2}+\left( 2mh+\left| z-{z}^{\prime} \right| \right)^{2} \right)^{-1/2}} \\ +\mathop{\sum}\limits_{m=1}^\infty {\left( \alpha ^{2}r^{2}+\left( \left( 2m+1 \right)h+\left| z-{z}^{\prime} \right| \right)^{2} \right)^{-1/2}} \end{array} \right] $$
(B.8)

where \(\alpha =\sqrt{\sigma_r/\sigma_t}\le 1\).

For the case of diagonal observations, voltage is measured at B, positioned at \((x,y,z)=(a,0,0)=({\mathbf r},0)\). This voltage is the sum of contributions from current sources positioned at A =  (x′,y′,z′) =  (0,0,0) and D =  (x′,y′,z′) =  (a,0,h), respectively. In the case of a source at A

$$ V_{BA} =\frac{I}{2\pi \left( \sigma_r \sigma_t \right)^{1/2}}\left\{ \begin{array}{l} \frac{1}{\alpha a}+\frac{1}{\left( \alpha ^{2}a^{2}+h^{2} \right)^{1/2}} \\ +\mathop{\sum}\limits_{m=1}^\infty {\left[ \alpha ^{2}a^{2}+\left( 2mh \right)^{2} \right]^{-1/2}} +\mathop{\sum}\limits_{m=1}^\infty {\left[ \alpha ^{2}a^{2}+\left( 2m+1 \right)^{2}h^{2} \right]^{-1/2}} \end{array} \right\} $$
(B.9)

and for a source at D

$$ V_{BD} =\frac{I}{2\pi \left( \sigma_r \sigma_t \right)^{1/2}}\left\{ \frac{2}{h}+\mathop{\sum}\limits_{m=1}^\infty {\left( \frac{1}{\left( 2m+1 \right)h}+\frac{1}{2mh} \right)} \right\} $$
(B.10)

So the voltage on the electrode at B becomes

$$ V_B =V_{BA} -V_{BD} =\frac{I}{2\pi \left( \sigma_r \sigma_t \right)^{1/2}}\left\{ \begin{array}{l} \frac{1}{\alpha a}+\frac{1}{\left( \alpha ^{2}a^{2}+h^{2} \right)^{1/2}}-\frac{2}{h} \\ +\mathop{\sum}\limits_{m=1}^\infty {\left[ \begin{array}{l} \frac{1}{\left( \alpha ^{2}a^{2}+\left( {2mh} \right)^{2} \right)^{1/2}}+\frac{1}{\left( \alpha ^{2}a^{2}+\left( 2m+1 \right)^{2}h^{2} \right)^{1/2}} \\ -\frac{1}{\left( 2m+1 \right)h}-\frac{1}{2mh} \end{array} \right]} \end{array} \right\} $$
(B.11)

By symmetry, the difference between voltages at electrodes at B and C = (x,y,z) = (0,0,h) will be twice this quantity.

For the case of orthogonal observations, V BA will be the same as for the diagonal case, but the voltage contribution from the source at C will be

$$ V_{BC} =\frac{I}{2\pi \left( {\sigma_r \sigma_t } \right)^{1/2}}\left\{ {\begin{array}{l} \frac{2}{\left( {\alpha ^{2}a^{2}+h^{2}} \right)^{1/2}} \\ +\mathop{\sum}\limits_{m=1}^\infty {\left[ {\left( {\alpha ^{2}a^{2}+\left( {2mh} \right)^{2}} \right)^{{-1}/2}+\left( {\alpha ^{2}a^{2}+\left( {2m+1} \right)^{2}h^{2}} \right)^{{-1}/2}} \right]} \end{array}} \right\} $$
(B.12)

and the voltage difference V B will be

$$ V_B =V_{BA} -V_{BC} =\frac{I}{2\pi \left( \sigma_r \sigma_t \right)^{1/2}}\left[ \frac{1}{\alpha a}-\frac{1}{\left( {\alpha ^{2}a^{2}+h^{2}} \right)^{1/2}} \right]. $$
(B.13)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sadleir, R.J., Argibay, A. Modeling Skull Electrical Properties. Ann Biomed Eng 35, 1699–1712 (2007). https://doi.org/10.1007/s10439-007-9343-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-007-9343-5

Keywords

Navigation