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Comparative power spectral analysis of simultaneous electroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media

EEG and MEG power spectra

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Abstract

The resistive or non-resistive nature of the extracellular space in the brain is still debated, and is an important issue for correctly modeling extracellular potentials. Here, we first show theoretically that if the medium is resistive, the frequency scaling should be the same for electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To test this prediction, we analyzed the spectrum of simultaneous EEG and MEG measurements in four human subjects. The frequency scaling of EEG displays coherent variations across the brain, in general between 1/f and 1/f 2. In a given region, although the variability of the frequency scaling exponent was higher for MEG compared to EEG, both signals consistently scale with a different exponent. In some cases, the scaling was similar, but only when the signal-to-noise ratio of the MEG was low. Several methods of noise correction for environmental and instrumental noise were tested, and they all increased the difference between EEG and MEG scaling. In conclusion, there is a significant difference in frequency scaling between EEG and MEG, which can be explained if the extracellular medium (including other layers such as dura matter and skull) is globally non-resistive.

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Notes

  1. Examples of nonlinear effects are variations of the macroscopic conductivity σ f with the magnitude of electric field E. Such variations could appear due to ephaptic (electric-field) interactions for example. In addition, any type of linear reactivity of the medium to the electric field or magnetic induction can lead to frequency-dependent electric parameters σ, ε, μ (for a detailed discussion of such effects, see Bédard and Destexhe 2009).

  2. Note that in textbooks, these linking equations (Eqs. (2)–(4)) are often algebraic and independent of time (for example, see Eqs. (5.2–6), (5.2–7) and (5.2–8) in Gulrajani 1998). The present formulation is more general, more in the line of Landau and Lifchitz (1984).

  3. In textbooks, the electric parameters are sometimes considered as complex numbers, for example with the notion of phasor (see Section 5.3 in Gulrajani 1998), but they are usually considered frequency independent.

  4. If it was not the case, then the source terms would be a function of the produced field, which would result in more complicated equations.

  5. Note that to compare scaling exponents between studies one must take into account that the electrode montage may influence the scaling. For example, in bipolar (differential) EEG recordings, if two leads are scaling as 1/(A + f) and 1/(B + f), the difference will have regions scaling as 1/f 2.

  6. Ionic diffusion can create an impedance known as the “Warburg impedance”, which scales as \(1/\sqrt{\omega}\), giving 1/f scaling in the power spectra (Taylor and Gileadi 1995; Diard et al. 1999).

References

  • Abd El-Fattah, M. A., Dessouky, M. I., Diab, S. M., & Abd El-samie, F. E. (2008). Speech enhancement using an adaptive Wiener filtering approach. Progress in Electromagnetics Research, 4, 167–184.

    Google Scholar 

  • Abdi, H. (2010). Partial least square regression, projection on latent structure regression, PLS-regression. Computational Statistics, 2, 97–106.

    Google Scholar 

  • Ahlfors, S. P., Han, J., Lin, F. H., Witzel, T., Belliveau, J. W., Hämäläinen, M. S., et al. (2010). Cancellation of EEG and MEG signals generated by extended and distributed sources. Human Brain Mapping, 31, 140–149.

    PubMed  Google Scholar 

  • Bédard, C., & Destexhe, A. (2009). Macroscopic models of local field potentials and the apparent 1/f noise in brain activity. Biophysical Journal, 96, 2589–2603.

    Article  PubMed  Google Scholar 

  • Bédard, C., Kröger, H., & Destexhe, A. (2004). Modeling extracellular field potentials and the frequency-filtering properties of extracellular space. Biophysical Journal, 86, 1829–1842.

    Article  PubMed  Google Scholar 

  • Bédard, C., Kröger, H., & Destexhe, A. (2006a). Does the 1/f frequency-scaling of brain signals reflect self-organized critical states? Physical Review Letters, 97, 118102.

    Article  PubMed  Google Scholar 

  • Bédard, C., Kröger, H., & Destexhe, A. (2006b). Model of low-pass filtering of local field potentials in brain tissue. Physical Review E, 73, 051911.

    Article  Google Scholar 

  • Bédard, C., Rodrigues, S., Roy, N., Contreras, D., & Destexhe, A. (2010). Evidence for frequency-dependent extracellular impedance from the transfer function between extracellular and intracellular potentials. Journal of Computational Neuroscience doi:10.1007/s10827-010-0250-7.

    Google Scholar 

  • Bell, A. J., & Sejnowski, T. J. (1995). An information maximisation approach to blind separation and blind deconvolution. Neural Computation, 7, 1129–1159.

    Article  CAS  PubMed  Google Scholar 

  • Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech and Signal Processing, 27, 113–120.

    Article  Google Scholar 

  • Buiatti, M., Papo, D., Baudonnière, P. M., & van Vreeswijk, C. (2007). Feedback modulates the temporal scale-free dynamics of brain electrical activity in a hypothesis testing task. Neuroscience, 146, 1400–1412.

    Article  CAS  PubMed  Google Scholar 

  • Cuffin, B. N., & Cohen, D. (1979). Comparison of the magnetoencephalogram and electroencephalogram. EEG Clinical Neurophysiology, 47, 132–146.

    Article  CAS  Google Scholar 

  • de Boor, C. (2001). A. practical guide to splines (revised ed.). New York: Springer.

    Google Scholar 

  • 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, 9–21.

    Article  PubMed  Google Scholar 

  • Destexhe, A., Rudolph, M., & Paré, D. (2003). The high-conductance state of neocortical neurons in vivo. Nature Reviews Neuroscience, 4, 739–751.

    Article  CAS  PubMed  Google Scholar 

  • Diard, J.-P., Le Gorrec, B., & Montella, C. (1999). Linear diffusion impedance. General expression and applications. Journal of Electroanalytical Chemistry, 471, 126–131.

    Article  CAS  Google Scholar 

  • Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11, 89121.

    Article  Google Scholar 

  • Ferree, T. C., Luu P., Russell, G. S., & Tucker, D. M. (2001). Scalp electrode impedance, infection risk, and EEG data quality. Clinical Neurophysiology, 112, 536–544.

    Article  CAS  PubMed  Google Scholar 

  • Foster, K. R., & Schwan, H. P. (1989). Dielectric properties of tissues and biological materials: A critical review. Critical Reviews in Biomedical Engineering, 17, 25–104.

    CAS  PubMed  Google Scholar 

  • Freeman, W. J., Rogers, L. J., Holmes, M. D., & Silbergeld, D. L. (2000). Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands. Journal of Neuroscience Methods, 95, 111–121.

    Article  CAS  PubMed  Google Scholar 

  • Gabriel, S., Lau, R. W., & Gabriel, C. (1996a). The dielectric properties of biological tissues: I. Literature survey. Physics in Medicine & Biology, 41, 2231–2249.

    Article  CAS  Google Scholar 

  • 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 & Biology, 41, 2251–2269.

    Article  CAS  Google Scholar 

  • Gabriel, S., Lau, R. W., & Gabriel, C. (1996c). The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum tissues. Physics in Medicine & Biology, 41, 2271–2293.

    Article  CAS  Google Scholar 

  • Garthwaite, P. (1994). An interpretation of partial least squares. Journal of the American Statistical Association, 89, 122–127.

    Article  Google Scholar 

  • Gulrajani, R. M. (1998). Bioelectricity and biomagnetism. New York: Wiley.

    Google Scholar 

  • 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 Modern Physics, 65, 413–497.

    Article  Google Scholar 

  • Hwa, R. C., & Ferree, T. C. (2002a). Scaling properties of fluctuations in the human electroencephalogram. Physical Review E, 66, 021901.

    Article  Google Scholar 

  • Hwa, R. C., & Ferree, T. C. (2002b). Fluctuation analysis of human electroencephalogram. Nonlinear Phenomena in Complex Systems, 5, 302–307.

    Google Scholar 

  • Kamath, S., & Loizou, P. (2002). A multi-band spectral subtraction method for enhancing speech corrupted by colored noise. In Proceedings of ICASSP 2002 (pp. 4160–4164).

  • Kantelhardt, J., Koscielny-Bunde, E., Rego, H., Havlin, S., & Bunde, A. (2001). Detecting long-range correlations with detrended fluctuation analysis. Physica A, 295, 441–454.

    Article  Google Scholar 

  • Katkovnik, V., Egiazarian, K., & Astola, J. (2006). Local approximation in signal and image processing. Bellingham: SPIE.

    Book  Google Scholar 

  • Landau, L., & Lifchitz, E. (1984). Electrodynamics of continuous media. Moskow: MIR.

    Google Scholar 

  • Lim, J. S., & Oppenheim, A. V. (1979). Enhancement and band width compression of noisy speech. Proceedings of the IEEE, 67, 1586–1604.

    Article  Google Scholar 

  • Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J. (2001). Long-range temporal correlations and scaling behavior in human brain oscillations. Journal of Neuroscience, 21, 1370–1377.

    CAS  PubMed  Google Scholar 

  • Logothetis, N. K., Kayser, C., & Oeltermann, A. (2007). In vivo measurement of cortical impedance spectrum in monkeys: Implications for signal propagation. Neuron, 55, 809–823.

    Article  CAS  PubMed  Google Scholar 

  • Loizou, P. C. (2007). Speech enhancement: Theory and practice. Boca Raton: CRC.

    Google Scholar 

  • Magee, L. (1998). Nonlocal behavior in polynomical regression. The American Statistician, 52, 20–22.

    Article  Google Scholar 

  • Miller, K. J., Sorensen, L. B., Ojemann, J. G., & den Nijs, M. (2009). Power-law scaling in the brain surface electric potential. PLoS Computers in Biology, 5, e1000609.

    Article  Google Scholar 

  • Nenonen, J., Kajola, M., Simola, J., & Ahonen, A. (2004). Total information of multichannel MEG sensor arrays. In E. Halgren, A. Ahlfors, M. Hamalainen, & D. Cohen (Eds.), Proceedings of the 14th international conference on biomagnetism (Biomag2004), Boston, MA (pp. 630–631).

    Google Scholar 

  • Novikov, E., Novikov, A., Shannahoff-Khalsa, D., Schwartz, B., & Wright, J. (1997). Scale-similar activity in the brain. Physical Review E, 56, R2387–R2389.

    Article  Google Scholar 

  • Nunez, P. L., & Srinivasan, R. (2005). Electric fields of the brain. The neurophysics of EEG (2nd ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Plonsey, R. (1969). Bioelectric phenomena. New York: McGraw Hill.

    Google Scholar 

  • Pritchard, W. S. (1992). The brain in fractal time: 1/f-like power spectrum scaling of the human electroencephalogram. International Journal of Neuroscience, 66, 119–129.

    Article  CAS  PubMed  Google Scholar 

  • Rall, W., & Shepherd, G. M. (1968). Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. Journal of Neuroscience, 31, 884–915.

    CAS  Google Scholar 

  • Ramirez, R. R. (2008). Source localization. Scholarpedia, 3, 1733.

    Article  Google Scholar 

  • Ranck, Jr., J. B. (1963). Specific impedance of rabbit cerebral cortex. Experimental Neurology, 7, 144–152.

    Article  PubMed  Google Scholar 

  • Royston, P., & Altman, D. (1994). Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling. Applied Statistician, 43, 429–467.

    Article  Google Scholar 

  • Sarvas, L. (1987). Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in Medicine & Biology, 32, 11–22.

    Article  CAS  Google Scholar 

  • Sim, B. L., Tong, Y. C., Chang, J. C., & Tan, C. T. (1998). A parametric formulation of the generalized spectral subtraction method. IEEE Transactions on Speech and Audio Processing, 6, 328–337.

    Article  Google Scholar 

  • Taylor, S. R., & Gileadi, E. (1995). The physical interpretation of the Warburg impedance. Corrosion, 51, 664–671.

    Article  CAS  Google Scholar 

  • Valencia, M., Artieda, J., Alegre, M., & Maza, D. (2008). Influence of filters in the detrended fluctuation analysis of digital electroencephalographic data. Journal of Neuroscience Methods, 170, 310–316.

    Article  PubMed  Google Scholar 

  • Voipio, J., Tallgren, P., Heinonen, E., Vanhatalo, S., & Kaila, K. (2003). Millivolt-scale DC shifts in the human scalp EEG: Evidence for a nonneuronal generator. Journal of Neurophysiology, 89, 2208–2214.

    Article  PubMed  Google Scholar 

  • Wolters, C., & de Munck, J. C. (2007). Volume conduction. Scholarpedia, 2, 1738.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Philip Louizo for comments on spectral subtraction methods and Hervé Abdi for comments on Partial least square methods. We also thank anonymous reviewers for helpful comments. Research supported by the Centre National de la Recherche Scientifique (CNRS, France), Agence Nationale de la Recherche (ANR, France), the Future and Emerging Technologies program (FET, European Union; FACETS project) and the National Institutes of Health (NIH grants NS18741, EB009282 and NS44623). N.D. is supported by a fellowship from Ecole de Neurosciences de Paris (ENP). Additional information is available at http://cns.iaf.cnrs-gif.fr.

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Correspondence to Alain Destexhe.

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Action Editor: Gaute T. Einevoll

Nima Dehghani and Claude Bédard are co-first authors.

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Appendices

Appendix 1: Frequency dependence of electric field and magnetic induction

To compare the frequency dependence of magnetic induction and electric field, we evaluate them in a dendritic cable, expressed differentially. For a differential element of dendrite, in Fourier space, the current produced by a magnetic field (Ampère–Laplace law) is given by the following expression (see Appendix 2):

$$ \delta\bf{B}_f(\bf{r})= \frac{\mu_o}{4\pi} \bf{j}_f^p(\bf{r'}) \times \frac{\bf{r}- \bf{r'}}{\|\bf{r}-\bf{r'}\|^3} \delta v' $$
(20)

when the extracellular medium is resistive. Note that the source of magnetic induction is essentially given by the component of \(\bf{j}_f^p\) along the axial direction (\(j_f^{\,i}\)) within each differential element of dendrite because the perpendicular (membrane) current does not participate to producing the magnetic induction if we assume a cylindrical symmetry.

For the electric potential, we have the following differential expression for a resistive medium (see Appendix 3):

$$ \delta\bf{V}_f(\bf{r})=\frac{1}{4\pi\gamma}\frac{ \delta I_f^{\perp}(\bf{r}')}{\| \bf{r}-\bf{r'}\|} =\frac{1}{4\pi\gamma}\frac{j_f^{\,m}(\bf{r}')}{\| \bf{r}-\bf{r'}\|}\delta S' $$
(21)

where \(j_f^{\,m}\) is the transmembrane current per unit of surface.

If we consider the differential expressions for the magnetic induction (Eq. (20)) and electric potential (Eq. (21)), one can see that the frequency dependence of the ratio of their modulus is completely determined by the frequency dependence of the ratio of current density \(j_f^{\,m}\) and \(j_f^{\,i} \). In Appendix 4, we show that this ratio is quasi-independent of frequency for a resistive medium, for low frequencies (smaller that ∼10 Hz), and if the current sources are of very low correlation.

Thus, magnetic induction and electric potential can be very well approximated by:

$$\begin{array}{lll} V_{f}(\bf{r}) &=& N<V>= N<\sum\limits_{l=1}^{N} \delta V_{f}^{l}> \\ \bf{B}_f(\bf{r})&=& N<\bf{B}>= N<\sum\limits_{l=1}^{N} \delta \bf{B}_f^l> \end{array}$$
(22)

for sufficiently small differential dendritic elements (N/l large).

Because the functions of spatial and frequency are statistically independent, we can write the following expressions for the square modulus of the fields (see Eqs. (20) and (21)):

$$\begin{array}{lll} |V_{f}(\bf{r})|^2 &=& N^2|<\sum\limits_{l=1}^{N}V^l(\bf{r})G_l^m(f)>|^2 \\ &=& |V(\bf{r})|^2|G(f)|^2 \\ \|\bf{B}_f(\bf{r})\|^2 &=& N^2\|<\sum\limits_{i=1}^{N}\bf{B}^{\,i}(\bf{r})G_l^m(f)>\|^2 \\ &=& \|\bf{W}(\bf{r})\|^2|G(f)|^2 \end{array}$$
(23)

where\(G(f)=<G_l^m(f)> \), \(V^l(\bf{r})=<V^l(\bf{r})>\) and \(\bf{W}(\bf{r})=<\bf{B}^l(\bf{r})>\). Thus, the scaling of the PSDs of the electric potential and magnetic induction must be the same for low frequencies (smaller than ~10 Hz) if the medium is resistive and when the current sources have very low correlation.

Appendix 2: Differential expression for the magnetic induction

According to Maxwell equations, the magnetic induction is given by:

$$ \bf{B}_f(\bf{r})= \frac{\mu_o}{4\pi}\iiint\limits_{\rm head} \frac{\nabla'\times\bf{j}_f^p(\bf{r'})}{\|\bf{r}-\bf{r'} \|} dv' $$
(24)

where dv′ = dx ′1 dx ′2 dx ′3 and

$$ \nabla'\left(\frac{1}{\|\bf{r}-\bf{r'}\|}\right)=\frac{\bf{r}- \bf{r'}}{\|\bf{r}-\bf{r'}\|^3} $$

for a perfectly resistive medium.

We now show that this expression is equivalent to Ampere–Laplace law.

From the identity \(\nabla' \times (g\bf{A})= g(\nabla' \times \bf{A})+\nabla' g\times\bf{A}\), where \(\nabla' = \hat{e}_x\frac{\partial}{\partial x'}+\hat{e}_y\frac{\partial}{\partial y'}+\hat{e}_z\frac{\partial}{\partial z'}\), we can write:

$$\begin{array}{lll} \bf{B}_f(\bf{r})&=& \frac{\mu_o}{4\pi} \iiint\limits_{\rm head} \left[\nabla'\times \left(\frac{\bf{j}_f^p(\bf{r'})}{\|\bf{r}-\bf{r'}\|}\right) + \frac{\mu}{4\pi} \bf{j}_f^p(\bf{r'})\right. \\ &&\qquad\qquad\qquad\;\left.\times\nabla'\frac{1}{\|\bf{r}-\bf{r'}\|}\vphantom{\left(\frac{\bf{j}_f^p(\bf{r'})}{\|\bf{r}-\bf{r'}\|}\right)}\right] dv' \end{array}$$
(25)

Moreover, we also have the following identity

$$ \iiint\limits_{\rm head} \nabla' \times \left(\frac{\bf{j}_f^p(\bf{r'})}{\|\bf{r} - \bf{r'}\|}\right) dv' = -\iint\limits_{\partial {\rm head}} \frac{\bf{j}_f^p(\bf{r'})}{\|\bf{r} - \bf{r'}\|} \times \hat{n} dS' $$
(26)

where \(\hat{n}\) is a unitary vector perpendicular to the integration surface and going outwards from that surface. Extending the volume integral outside the head, the surface integral is certainly zero because the current is zero outside of the head. It follows that:

$$ \bf{B}_f(\bf{r})= \frac{\mu_o}{4\pi}\iiint\limits_{\rm head} \bf{j}_f^p(\bf{r'})\times \frac{\bf{r}- \bf{r'}}{\|\bf{r}-\bf{r'}\|^3} dv' $$
(27)

where dv′ = dx ′1 dx ′2 dx ′3 because

$$ \nabla'\left(\frac{1}{\|\bf{r}-\bf{r'}\|}\right)=\frac{\bf{r}- \bf{r'}}{\|\bf{r}-\bf{r'}\|^3} $$

Equation (27) is called the Ampère–Laplace law (see Eq. (13) in Hämäläinen et al. 1993). It is important to note that this expression for the magnetic induction is not valid when the medium is not resistive.

Finally, from the last expression, the magnetic induction for a differential element of dendrite can be written as:

$$ \delta\bf{B}_f(\bf{r})= \frac{\mu_o}{4\pi} \bf{j}_f^p(\bf{r'})\times \frac{\bf{r}- \bf{r'}}{\|\bf{r}-\bf{r'}\|^3} \delta v' $$
(28)

Appendix 3: Differential expression of the electric field and electric potential

In this appendix, we derive the differential expression for the electric field. Starting from Eq. (10), we obtain the solution for the electric potential:

$$ V_{f}(\bf{r})=-\frac{1}{4\pi\gamma_f}\iiint\limits_{\rm head} \frac{\nabla\cdot \bf{j}_f^p(\bf{r}')}{\Vert \bf{r}-\bf{r'}\Vert} dv' $$
(29)

It follows that the electric field produced by the ensemble of sources can be expressed as:

$$ \bf{E}_f(\bf{r})= - \nabla V_{f}(\bf{r}) = \frac{1}{4\pi\gamma_f}\iiint\limits_{\rm head} \nabla\cdot \bf{j}_f^p(\bf{r}') \cdot \frac{ \bf{r} - \bf{r'}}{\| \bf{r} - \bf{r'}\|^3} dv' $$
(30)

such that every differential element of dendrite produces the following electric field:

$$ \delta\bf{E}_f(\bf{r})= \frac{\nabla\cdot \bf{j}_f^p(\bf{r}')}{4\pi\gamma_f} \cdot \frac{ \bf{r}-\bf{r'}}{\| \bf{r}-\bf{r'}\|^3} \delta v' $$
(31)

The transmembrane current \(\delta I_f^{\perp}\) obeys \(\delta I_f^{\perp}=i\omega\rho_f(\bf{r}') \delta v'\) because we are in a quasi-stationary regime in a differential dendritic element. Taking into account the differential law of charge conservation \(\nabla\cdot \bf{j}_f(\bf{r}')=-i\omega\rho_f(\bf{r}')\), we have:

$$ \delta\bf{E}_f(\bf{r})=\frac{\delta I_f^{\perp}(\bf{r}')}{4\pi\gamma_f}\frac{ \bf{r}-\bf{r'}}{\| \bf{r}-\bf{r'}\|^3} =\frac{j_f^{\,m}(\bf{r}')}{4\pi\gamma_f}\frac{ \bf{r}-\bf{r'}}{\| \bf{r}-\bf{r'}\|^3}\delta S' $$
(32)

where \(j_f^{\,m}\) is the density of transmembrane current per unit surface and δS′ is the surface area of a differential dendritic element. This approximation is certainly valid for frequencies lower than 1,000 Hz because the Maxwell–Wagner time (see Bédard et al. 2006b) of the cytoplasm (\(\tau_{mw}^{cyto}=\varepsilon/\sigma \sim 10^{-10}~{\rm s}\)). is much smaller than the typical membrane time constant of a neuron (τ m ∼5 − 20 ms).

Finally the contribution of a differential element of dendrite to the electric potential at position \(\bf{r}\) is given by

$$ \delta\bf{V}_f(\bf{r})=\frac{1}{4\pi\gamma_f}\frac{ \delta I_f^{\perp}(\bf{r}')}{\| \bf{r}-\bf{r'}\|} =\frac{1}{4\pi\gamma_f}\frac{ j_f^{\,m}(\bf{r}')}{\| \bf{r}-\bf{r'}\|}\delta S' $$
(33)

We note that the expressions for the electric field and potential produced by each differential element of dendrite have the same frequency dependence because it is directly proportional to \(\frac{j_f^{\,m}}{\gamma_f}\) for the two expressions. Also note that if the medium is resistive, then γ f  = γ and the frequency dependence of the electric field and potential are solely determined by that of the transmembrane current \(j_f^{\,m}\).

Appendix 4: Frequency dependence of the ratio \( { j_f^{\,i}(\bf{x})} / {j_f^{\,m}(\bf{x})}\)

For each differential element of dendrite, we consider the standard cable model, in which the impedance of the medium is usually neglected (it is usually considered negligible compared to the membrane impedance). In this case, we have:

$$ \left\{ \begin{array}{ccc} j_f^{\,m} &=& \dfrac{V_{f}^{\,m}}{r_m}+i\omega c_m V_{f}^{\,m} \\ j_f^{\,i} &=& -\sigma\dfrac{\partial V_{f}^{\,m}}{\partial x} = -\dfrac{1}{r_i}\dfrac{\partial V_{f}^{\,m}}{\partial x} \end{array} \right .$$
(34)

where \(V_{\!f}^{\,m}\), \(j_f^{\,i}\), \(j_f^{\,m}\), c m , r m et r i are respectively the membrane potential, the current density in the axial direction, the transmembrane current density, the specific capacitance (F/m 2), the specific membrane resistance (Ω.m 2) and the cytoplasm resistivity (Ω.m).

It follows that

$$ \frac{ j_f^{\,i}(\bf{x})}{j_f^{\,m}(\bf{x})} =\frac{r_m}{r_i(1+i\omega\tau_m)} \cdot\frac{\partial}{\partial x}\big[ln(V_{f}^{\,m}(\bf{x})\big] $$
(35)

where τ m  = r m c m .

Under in vivo-like conditions, the activity of neurons is intense and of very low correlation. This is the case for desynchronized-EEG states, such as awake eyes-open conditions, where the activity of neurons is characterized by very low levels of correlations. There is also evidence that in such conditions, neurons are in “high-conductance states” (Destexhe et al. 2003), in which the synaptic activity dominates the conductance of the membrane and primes over intrinsic currents. In such conditions, we can assume that the synaptic current sources are essentially uncorrelated and dominant, such that the deterministic link between current sources will be small and can be neglected (see Bédard et al. 2010). Further assuming that the electric properties of extracellular medium are homogeneous, then each differential element of dendrite can be considered as independent and the voltages V m have similar power spectra.

In such conditions, we have:

$$ V_{f}^{\,m} (\bf{x}) = F^{\,m}(\bf{x})G^{\,m}(f) $$
(36)

Note that this expression implies that we have in general for each differential element of dendrite:

$$ \left\{ \begin{array}{ccc} j_f^{\,m} (\bf{x}) &=& F^{\,m}(\bf{x})\left(\dfrac{1+i\omega\tau_m}{r_m}\right)G^{\,m}(f) \\ j_f^{\,i} (\bf{x}) &=& -\dfrac{1}{r_i}\dfrac{\partial F^{\,m}(\bf{x})}{\partial x}G^{\,m}(f)= F^{\,i}(\bf{x})G^{\,m}(f) \end{array} \right . $$
(37)

according to Eq. (34).

It follows that

$$\begin{array}{lll} \frac{ j_f^{\,i}(\bf{x})}{ j_f^{\,m}(\bf{x})} &=&\frac{r_m}{r_i(1+i\omega\tau_m)} \cdot\frac{\partial}{\partial x}[ln(F(\bf{x}))] \\ &\approx& \frac{r_m}{r_i} \cdot\frac{\partial}{\partial x}[ln(F(\bf{x}))] \end{array}$$
(38)

Thus, for frequencies smaller than 1/(ωτ m ) (about 10 to 30 Hz for τ m of 5–20 ms), the ratio \( \frac{ j_f^{\,i}(\bf{x})}{ j_f^{\,m}(\bf{x})}\) will be frequency independent, and for each differential element of dendrite, we have:

$$ \left\{ \begin{array}{ccc} j_f^{\,m} (\bf{x}) &=& F^{\,m}(\bf{x})G^{\,m}(f)\\ j_f^{\,i} (\bf{x}) &=& F^{\,i}(\bf{x})G^{\,m}(f) \end{array} \right. $$
(39)

for frequencies smaller than ∼10 Hz.

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Dehghani, N., Bédard, C., Cash, S.S. et al. Comparative power spectral analysis of simultaneous electroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media. J Comput Neurosci (2010). https://doi.org/10.1007/s10827-010-0252-5

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  • DOI: https://doi.org/10.1007/s10827-010-0252-5

Keywords

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