Abstract:A mathematical model (sigma(omega) approximately equal to A omega alpha, where, sigma is identical with conductivity, omega = 2 pi f is identical with applied frequency (Hz), A (amplitude) and alpha (unit less) is identical with search parameters) was used to fit the frequency dependence of electrical conductivities of compact, spongiosum, and bulk layers of the live and, subsequently, dead human skull samples. The results indicate that the fit of this model to the experimental data is excellent. The ranges of… Show more
“…Previous literature has examined skull conductivity in wider frequencies, revealing a positive relationship, especially at frequencies higher than 10 kHz (Gabriel et al 1996b; Tang et al 2008), and suggest skull conductivity may exponentially increase at high frequencies. Within the relevant range for brain activity, skull conductivity increased by ~ 6.7% from 11 to 127 Hz (Dabek et al 2016) and ~ 13% from 10 to 90 Hz (Akhtari et al 2002), of which the authors developed a non-linear model for frequency dependence of different skull layers (Akhtari et al 2003). Variation as a function of frequency may be expected due to interactions between mobile electrolytes (i.e.…”
Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly-and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
“…Previous literature has examined skull conductivity in wider frequencies, revealing a positive relationship, especially at frequencies higher than 10 kHz (Gabriel et al 1996b; Tang et al 2008), and suggest skull conductivity may exponentially increase at high frequencies. Within the relevant range for brain activity, skull conductivity increased by ~ 6.7% from 11 to 127 Hz (Dabek et al 2016) and ~ 13% from 10 to 90 Hz (Akhtari et al 2002), of which the authors developed a non-linear model for frequency dependence of different skull layers (Akhtari et al 2003). Variation as a function of frequency may be expected due to interactions between mobile electrolytes (i.e.…”
Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly-and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
“…Each set of data was obtained at current amplitude of 60 μA. The overall errors in potential and phase measurements were determined by replacing the sample with a known resistor (R = 988 Ω) and a R||C circuit respectively (Akhtari et al 2003). Fig.…”
Source localization models assume brain electrical conductivities are isotropic at about 0.33 S/m. These assumptions have not been confirmed ex vivo in humans. This study determined bidirectional electrical conductivities from pediatric epilepsy surgery patients. Electrical conductivities perpendicular and parallel to the pial surface of neocortex and subcortical white matter (n = 15) were measured using the 4-electrode technique and compared with clinical variables. Mean (±SD) electrical conductivities were 0.10 ± 0.01 S/m, and varied by 243% from patient to patient. Perpendicular and parallel conductivities differed by 45%, and the larger values were perpendicular to the pial surface in 47% and parallel in 40% of patients. A perpendicular principal axis was associated with normal, while isotropy and parallel principal axes were linked with epileptogenic lesions by MRI. Electrical conductivities were decreased in patients with cortical dysplasia compared with non-dysplasia etiologies. The electrical conductivity values of freshly excised human brain tissues were approximately 30% of assumed values, varied by over 200% from patient to patient, and had erratic anisotropic and isotropic shapes if the MRI showed a lesion. Understanding brain electrical conductivity and ways to non-invasively measure them are probably necessary to enhance the ability to localize EEG sources from epilepsy surgery patients.
“…Computer simulations of the current flow during tDCS using human head models have revealed that a significant amount of the current is shunted by the well-conducting skin (~90%), while much less of the current actually reaches the brain [ 22 ]. In the case of tACS, the different frequency response of each type of conducting element between the electrodes and the brain should also be taken into account [ 23 ].…”
Section: How Can Tacs Influence Cortical Activity?mentioning
Background. Transcranial alternating current stimulation (tACS) is a relatively recent method suited to noninvasively modulate brain oscillations. Technically the method is similar but not identical to transcranial direct current stimulation (tDCS). While decades of research in animals and humans has revealed the main physiological mechanisms of tDCS, less is known about the physiological mechanisms of tACS. Method. Here, we review recent interdisciplinary research that has furthered our understanding of how tACS affects brain oscillations and by what means transcranial random noise stimulation (tRNS) that is a special form of tACS can modulate cortical functions. Results. Animal experiments have demonstrated in what way neurons react to invasively and transcranially applied alternating currents. Such findings are further supported by neural network simulations and knowledge from physics on entraining physical oscillators in the human brain. As a result, fine-grained models of the human skull and brain allow the prediction of the exact pattern of current flow during tDCS and tACS. Finally, recent studies on human physiology and behavior complete the picture of noninvasive modulation of brain oscillations. Conclusion. In future, the methods may be applicable in therapy of neurological and psychiatric disorders that are due to malfunctioning brain oscillations.
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