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

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/f2. 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.

[1]  K. Foster,et al.  Dielectric properties of tissues and biological materials: a critical review. , 1989, Critical reviews in biomedical engineering.

[2]  Richard M. Schwartz,et al.  Enhancement of speech corrupted by acoustic noise , 1979, ICASSP.

[3]  R. Gulrajani Bioelectricity and biomagnetism , 1998 .

[4]  C. R. Deboor,et al.  A practical guide to splines , 1978 .

[5]  N. Logothetis,et al.  In Vivo Measurement of Cortical Impedance Spectrum in Monkeys: Implications for Signal Propagation , 2007, Neuron.

[6]  Philipos C. Loizou,et al.  Speech Enhancement: Theory and Practice , 2007 .

[7]  Rey Ramírez,et al.  Source localization , 2008, Scholarpedia.

[8]  Philipos C. Loizou,et al.  A multi-band spectral subtraction method for enhancing speech corrupted by colored noise , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  D. Tucker,et al.  Scalp electrode impedance, infection risk, and EEG data quality , 2001, Clinical Neurophysiology.

[10]  Jaakko Astola,et al.  Local Approximation Techniques in Signal and Image Processing (SPIE Press Monograph Vol. PM157) , 2006 .

[11]  W. Freeman,et al.  Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands , 2000, Journal of Neuroscience Methods.

[12]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[13]  R. W. Lau,et al.  The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. , 1996, Physics in medicine and biology.

[14]  D. Cohen,et al.  Comparison of the magnetoencephalogram and electroencephalogram. , 1979, Electroencephalography and clinical neurophysiology.

[15]  C. Bédard,et al.  Macroscopic models of local field potentials and the apparent 1/f noise in brain activity. , 2008, Biophysical journal.

[16]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[17]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[18]  J. B. Ranck,et al.  Specific impedance of rabbit cerebral cortex. , 1963, Experimental neurology.

[19]  H. Abdi Partial least squares regression and projection on latent structure regression (PLS Regression) , 2010 .

[20]  P. Garthwaite An Interpretation of Partial Least Squares , 1994 .

[21]  S. R. Taylor,et al.  Physical Interpretation of the Warburg Impedance , 1995 .

[22]  J. Fermaglich Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[23]  Jeffrey G. Ojemann,et al.  Power-Law Scaling in the Brain Surface Electric Potential , 2009, PLoS Comput. Biol..

[24]  G. Shepherd,et al.  Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. , 1968, Journal of neurophysiology.

[25]  W. Pritchard,et al.  The brain in fractal time: 1/f-like power spectrum scaling of the human electroencephalogram. , 1992, The International journal of neuroscience.

[26]  A.V. Oppenheim,et al.  Enhancement and bandwidth compression of noisy speech , 1979, Proceedings of the IEEE.

[27]  廣瀬雄一,et al.  Neuroscience , 2019, Workplace Attachments.

[28]  J. Sarvas Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[29]  C. Bédard,et al.  Modeling extracellular field potentials and the frequency-filtering properties of extracellular space. , 2003, Biophysical journal.

[30]  P. Baudonniere,et al.  Feedback modulates the temporal scale-free dynamics of brain electrical activity in a hypothesis testing task , 2007, Neuroscience.

[31]  P. Nunez,et al.  Electric fields of the brain , 1981 .

[32]  C Gabriel,et al.  The dielectric properties of biological tissues: I. Literature survey. , 1996, Physics in medicine and biology.

[33]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[34]  C. Bédard,et al.  Does the 1/f frequency scaling of brain signals reflect self-organized critical states? , 2006, Physical review letters.

[35]  T. Ferrée,et al.  Fluctuation Analysis of Human Electroencephalogram , 2001, physics/0105029.

[36]  A. Destexhe,et al.  The high-conductance state of neocortical neurons in vivo , 2003, Nature Reviews Neuroscience.

[37]  R. W. Lau,et al.  The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. , 1996, Physics in medicine and biology.

[38]  S. Havlin,et al.  Detecting long-range correlations with detrended fluctuation analysis , 2001, cond-mat/0102214.

[39]  Joseph Sylvester Chang,et al.  A parametric formulation of the generalized spectral subtraction method , 1998, IEEE Trans. Speech Audio Process..

[40]  Claude Bédard,et al.  Evidence for frequency-dependent extracellular impedance from the transfer function between extracellular and intracellular potentials , 2009, Journal of Computational Neuroscience.

[41]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[42]  P. Royston,et al.  Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. , 1994 .

[43]  J. Diard,et al.  Linear diffusion impedance. General expression and applications , 1999 .

[44]  Paul H. C. Eilers,et al.  Flexible smoothing with B-splines and penalties , 1996 .

[45]  J. Voipio,et al.  Millivolt-scale DC shifts in the human scalp EEG: evidence for a nonneuronal generator. , 2003, Journal of neurophysiology.

[46]  M. A. A. El-Fattah,et al.  Speech Enhancement Using an Adaptive Wiener Filtering Approach , 2008 .

[47]  E. Halgren,et al.  Cancellation of EEG and MEG signals generated by extended and distributed sources , 2009, Human brain mapping.

[48]  L. Magee Nonlocal Behavior in Polynomial Regressions , 1998 .

[49]  E. Novikov,et al.  Scale-similar activity in the brain , 1997 .

[50]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[51]  M. Alegre,et al.  Influence of filters in the detrended fluctuation analysis of digital electroencephalographic data , 2008, Journal of Neuroscience Methods.

[52]  Carsten Wolters,et al.  Volume conduction , 2007, Scholarpedia.

[53]  C. Bédard,et al.  Model of low-pass filtering of local field potentials in brain tissue. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.