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Plot of scalp potentials. (Left) model that contained dura layer, (middle) model in which the dura layer was replaced with CSF and (right) difference of the two plots. The color intensity scale is same for all three plots. In general, the inclusion of the dural layer in the FEM model severely reduces the magnitude of the scalp potentials as shown in the left plot.
Source publication
The dura layer which covers the brain is less conductive than the CSF (cerebrospinal fluid) and also more conductive than the skull bone. This could significantly influence the flow of volume currents from cortex to the scalp surface which will also change the magnitude and spatial profiles of scalp potentials. This was examined with a 3-D finite e...
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... These different image analysis tools have been incorporated into different head model generation and electric field calculation pipelines, such as ROAST [17] and SimNIBS [16]. The segmented number of tissues varies from simpler head models of three or four tissues (brain, skull, scalp, and eventually CSF) (e.g., [58]) to more complex head models up to, for instance, 18 tissues, including major tissues such as white matter, gray matter, eyeballs, soft bone or compact bone and details such as dura, blood vessels, fat, basal ganglia or internal air [59]. The segmentation, meshing, and forward problem-solving processes can also be performed using commercial tools. ...
... However, some surfaces may have small "spikes" or sharp points that result in numerical errors. It is also possible to use a combination of both methods, i.e., use the second alternative for complex surfaces such as the skull and scalp and use meshing from surfaces for internal layers such as CSF, gray matter, and white matter, which are concentric and closed (as performed, for instance, in [59]). ...
Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.
... The seven-layer model is also suitable for EEG studies where a more comprehensive phantom head tissue model is used. Such phantom head cases include skull, scalp, CSF, white matter, gray matter, eye, cerebellum [52], spinal cord, and dura [53]. By supplying any prerecorded data to the signal generator, the same response can be created in phantom heads, manufactured using any of these layers. ...
... As such, our modular material system is suitable for a wide range of applications. This study can offer a recipe for creating the layers as well as the ability to alter their thickness and geometry for any experiments examining the effect of only a single layer's thickness on EEG errors [53,54]. The SNR study of the manufactured layers in Figure 7a,b can provide confidence in generating synthetic brain signals for the experiment [55,56]. ...
With the recent advent of smart wearable sensors for monitoring brain activities in real-time, the scopes for using Electroencephalograms (EEGs) and Magnetoencephalography (MEG) in mobile and dynamic environments have become more relevant. However, their application in dynamic and open environments, typical of mobile wearable use, poses challenges. Presently, there is limited clinical data on using EEG/MEG as wearables. To advance these technologies at a time when large-scale clinical trials are not feasible, many researchers have turned to realistic phantom heads to further explore EEG and MEG capabilities. However, to achieve translational results, such phantom heads should have matching geometric features and electrical properties. Here, we have designed and fabricated multilayer chopped carbon fiber–PDMS reinforced composites to represent phantom head tissues. Two types of phantom layers are fabricated, namely seven-layer and four-layer systems with a goal to achieve matching electrical conductivities in each layer. Desired electrical conductivities are obtained by varying the weight fraction of the carbon fibers in PDMS. Then, the prototype system was calibrated and tested with a 32-electrode EEG cap. The test results demonstrated that the phantom effectively generates a variety of scalp potential patterns, achieved through a finite number of internal dipole generators within the phantom sample. This innovative design holds potential as a valuable test platform for assessing wearable EEG technology as well as developing an EEG analysis process.
... The exposed tips of the probe were immersed in Clorox bleach until a light gray color was observed. The two hemispheres of the sphere mold were sealed with vacuum grease and were filled with approximately 3 L of 0.9% sodium chloride (NaCl) in deionized water to emulate the conductivity of the brain (3.33 mS/cm at 20 • C) [27]; previous research has shown that 0.9% NaCl has a conductivity of 12 mS/cm at 20 • C [28]. Figure 2 illustrates the measurement apparatus. Model A and B were experimentally measured using magnets from K & J Magnets Inc (Pipersville, USA). ...
Neurostimulation devices that use rotating permanent magnets are being explored for their potential therapeutic benefits in patients with psychiatric and neurological disorders. This study aims to characterize the electric field (E-field) for ten configurations of rotating magnets using finite element analysis and phantom measurements. Various configurations were modeled, including single or multiple magnets, and bipolar or multipolar magnets, rotated at 10, 13.3, and 350 revolutions per second (rps). E-field strengths were also measured using a hollow sphere (r=9.2 cm) filled with a 0.9% sodium chloride solution and with a dipole probe. The E-field spatial distribution is determined by the magnets’ dimensions, number of poles, direction of the magnetization, and axis of rotation, while the E-field strength is determined by the magnets’ rotational frequency and magnetic field strength. The induced E-field strength on the surface of the head ranged between 0.0092 and 0.52 V/m. In the range of rotational frequencies applied, the induced E-field strengths were approximately an order or two of magnitude lower than those delivered by conventional transcranial magnetic stimulation. The impact of rotational frequency on E-field strength represents a confound in clinical trials that seek to tailor rotational frequency to individual neural oscillations. This factor could explain some of the variability observed in clinical trial outcomes.
... TMS is cleared by the United States Food and Drug Administration (FDA) for 22 major depression, anxious depression, obsessive-compulsive disorder, smoking cessation, 23 and migraine [1,2]. An alternative method to generating a time-varying magnetic field 24 involves mechanically rotating permanent magnets. Several rotating magnet devices have 25 been proposed [3][4][5], using high-speed rotating, high field strength neodymium magnets 26 to induce E-field in nearby nerve tissue. ...
... The exposed tips of the probe were immersed in Clorox bleach until a light 193 gray color was observed. The two hemispheres of the sphere mold were sealed with 194 vacuum grease and were filled with approximately 3 liters of 0.9% sodium chloride (NaCl) 195 in deionized water to emulate the conductivity of the brain (3.33 mS/cm at 20 • C) [24], in 196 which previous research have shown that 0.9% NaCl has a conductivity of 12 mS/cm at 197 20 • C [25]. Figure 2 illustrates the measurement apparatus. ...
Neurostimulation devices that use rotating permanent magnets are being explored for their potential therapeutic benefits in patients with psychiatric and neurological disorders. This study aims to characterize the electric field (E-field) for ten configurations of rotating magnets using finite element analysis and phantom measurements. Various configurations were modeled, including single or multiple magnets, bipolar or multipolar magnets, rotated at 10, 13.3, and 400 Hz. E-field strengths were also measured using a hollow sphere filled with a 0.9% sodium chloride solution and with a dipole probe. The E-field spatial distribution is determined by the magnets' dimensions, number of poles, direction of the magnetization, and axis of rotation, while the E-field strength is determined by the magnets' rotational frequency and magnetic field strength. The induced E-field strength on the surface of the head ranged between 0.0092 and 0.59 V/m. At the range of rotational frequencies applied, the induced E-field strengths were approximately an order or two of magnitude lower than those delivered by conventional transcranial magnetic stimulation. The impact of rotational frequency on E-field strength represents a previously unrecognized confound in clinical trials that seek to personalize stimulation frequency to individual neural oscillations and may represent a mechanism to explain some clinical trial results.
... We used quantitative indices to compare the spatial profiles of baseline values with the epileptic event values. This was done with the Relative Difference Measure (RDM*) and magnification factor (MAG) [Meijs et al., 1989;Ramon et al., 2014;Schimpf et al., 2002]. It is a very common technique to quantify the differences between two spatial plots. ...
Sudden phase changes are related to cortical phase transitions, which likely change in frequency and spatial distribution as epileptogenic activity evolves. A 100 s long section of micro-ECoG data obtained before and during a seizure was selected and analyzed. In addition, nine other short-duration epileptic events were also examined. The data was collected at 420 Hz, imported into MATLAB, downsampled to 200 Hz, and filtered in the 1–50 Hz band. The Hilbert transform was applied to compute the analytic phase, which was then unwrapped, and detrended to look for sudden phase changes. The phase slip rate (counts/s) and its acceleration (counts/s²) were computed with a stepping window of 1-s duration and with a step size of 5 ms. The analysis was performed for theta (3–7 Hz), alpha (7–12 Hz), and beta (12–30 Hz) bands. The phase slip rate on all electrodes in the theta band decreased while it increased for the alpha and beta bands during the seizure period. Similar patterns were observed for isolated epileptogenic events. Spatiotemporal contour plots of the phase slip rates were also constructed using a montage layout of 8 × 8 electrode positions. These plots exhibited dynamic and oscillatory formation of phase cone-like structures which were higher in the theta band and lower in the alpha and beta bands during the seizure period and epileptogenic events. These results indicate that the formation of phase cones might be an excellent biomarker to study the evolution of a seizure and also the cortical dynamics of isolated epileptogenic events.
... Source reconstructions will be the one way to see which brain areas are active at a given time point to solve this puzzle. In this approach, one needs to use anatomically realistic human head models that include the dura layer to reduce localization errors (Ramon et al., 2006(Ramon et al., , 2014. Another approach might be to use the cortical phase transition techniques described here to differentiate between perception and mental imagery. ...
Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4–7 Hz), alpha (7–12 Hz), beta (12–30 Hz), and low gamma (30–49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the ‘Eureka’ moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.
... It should be noted that the experimental technique is applicable to a broad range of conductive mediums. Without losing generality, we have maintained the conductivity of our saline water based on the data presented in (34), and (35). Accordingly, appropriate salt concentration (36) was used to prepare the desired conductive medium between the sensor and the source. ...
... The conductivities of the medium chosen were 2 mS/cm, 4.35 mS/cm, 15 mS/cm, and 20 mS/cm. The values are selected based on (34) and (35). The single source/signal generator's distances were chosen randomly. ...
Accurate detection of oscillatory electrical signals emitted from remote sources is necessary in many applications but poses several challenges. The major challenge is attributed to the source voltage and conductivity of the medium through which signals must transmit before they can be sensed by the receiving electrodes/sensors. This study introduces a novel algorithm to optimize source identification where low-voltage (mV range) signals transmit through a conductive medium. The proposed algorithm uses the measured data from different oscillatory signal sources and solves an inverse problem by minimizing a cost function to estimate all the signal properties, including the locations, frequencies, and phases. To increase the overall signal accuracy for a wide range of initial guess frequencies, we have utilized the Lomb-Scargle spectral analysis along with the Least Squares error optimization method. The data utilized in this study comes from an experimental setup that includes a bucket filled with salt-water as the conductive medium, multiple low-voltage signal sources and 32 remotely located sensors. The sources generate sine waves with amplitude of 10 mV and frequencies between 10 – 40 Hz. The average signal-to-noise ratio is approximately 10 dB. The algorithm has been validated using a single-source and multi-source setup. We observed that our algorithm can identify the source location within 10 mm from the actual source immersed inside the bucket of radius =~ 90 mm. Moreover, the frequency estimation error is nearly zero, which justifies the effectiveness of our proposed method.
... However, due to the insensitivity of MEG to skin conductivity, at least our source localizations should mainly not be affected. The brain skull interface does not only contain CSF (Jiang et al., 2020), but also the meninges (dura matter (Ramon et al., 2014), arachnoid mater, and pia mater) as well as blood vessels (Fiederer et al., 2016). Therefore, even if first simulations show that our SEF/SEP skull conductivity calibration procedure can compensate at least for parts of these individual modeling inaccuracies, the accuracy of the forward modeling should be further improved. ...
Objective
ranscranial direct current stimulation (tDCS) is a non-invasive neuro-modulation technique that delivers current through the scalp by a pair of patch electrodes (2-Patch). This study proposes a new multi-channel tDCS (mc-tDCS) optimization method, the distributed constrained maximum intensity (D-CMI) approach. For targeting the P20/N20 somatosensory source at Brodmann area 3b, an integrated combined magnetoencephalography (MEG) and electroencephalography (EEG) source analysis is used with individualized skull conductivity calibrated realistic head modeling.
Methods
Simulated electric fields (EF) for our new D-CMI method and the already known maximum intensity (MI), alternating direction method of multipliers (ADMM) and 2-Patch methods were produced and compared for the individualized P20/N20 somatosensory target for 10 subjects.
Results
D-CMI and MI showed highest intensities parallel to the P20/N20 target compared to ADMM and 2-Patch, with ADMM achieving highest focality. D-CMI showed a slight reduction in intensity compared to MI while reducing side effects and skin level sensations by current distribution over multiple stimulation electrodes.
Conclusion
Individualized D-CMI montages are preferred for our follow up somatosensory experiment to provide a good balance between high current intensities at the target and reduced side effects and skin sensations.
Significance
An integrated combined MEG and EEG source analysis with D-CMI montages for mc-tDCS stimulation potentially can improve control, reproducibility and reduce sensitivity differences between sham and real stimulations.
... We assumed for CSF a fixed conductivity value of 1.79 S/m due to the study of (Baumann et al., 1997) especially in pathological situations such as in brain atrophy, stroke lesions or hydrocephalus, the described CSF shunting effect might be much larger than presented here. Therefore, individualized SNR maps should be drawn using head models that also include dura mater (Ramon, Garguilo, Fridgeirsson, & Haueisen, 2014) and blood vessels (Fiederer et al., 2016). ...
Signal‐to‐noise ratio (SNR) maps are a good way to visualize electroencephalography (EEG) and magnetoencephalography (MEG) sensitivity. SNR maps extend the knowledge about the modulation of EEG and MEG signals by source locations and orientations and can therefore help to better understand and interpret measured signals as well as source reconstruction results thereof. Our work has two main objectives. First, we investigated the accuracy and reliability of EEG and MEG finite element method (FEM)‐based sensitivity maps for three different head models, namely an isotropic three and four‐compartment and an anisotropic six‐compartment head model. As a result, we found that ignoring the cerebrospinal fluid leads to an overestimation of EEG SNR values. Second, we examined and compared EEG and MEG SNR mappings for both cortical and subcortical sources and their modulation by source location and orientation. Our results for cortical sources show that EEG sensitivity is higher for radial and deep sources and MEG for tangential ones, which are the majority of sources. As to the subcortical sources, we found that deep sources with sufficient tangential source orientation are recordable by the MEG. Our work, which represents the first comprehensive study where cortical and subcortical sources are considered in highly detailed FEM‐based EEG and MEG SNR mappings, sheds a new light on the sensitivity of EEG and MEG and might influence the decision of brain researchers or clinicians in their choice of the best modality for their experiment or diagnostics, respectively.
... Our head models ignored the volume conduction effects of the dura ( Ramon, Garguilo, Fridgeirsson, & Haueisen, 2014 ), blood vessels ( Fiederer et al., 2016 ) as well as local skull inhomogeneities such as sutures, which could provide a path of higher conductance ( Tang et al., 2008 ;Ollikainen, Vauhkonen, Karjalainen, & Kaipio, 1999 ;Pohlmeier et al., 1997 ). In addition, following ( Baumann et al., 1997 ) for CSF conductivity, we assumed a fixed value of 1.79 S/m at body temperature, which is nearly identical to the recommended weighted mean value of 1.71 S/m of a recent meta-analysis ( McCann et al., 2019 ). ...
Skull conductivity has a substantial influence on EEG and combined EEG and MEG source analysis as well as on optimized transcranial electric stimulation. To overcome the use of standard literature values, we propose a non-invasive two-level calibration procedure to estimate skull conductivity individually in a group study with twenty healthy adults. Our procedure requires only an additional run of combined somatosensory evoked potential and field data, which can be easily integrated in EEG/MEG experiments. The calibration procedure uses the P20/N20 topographies and subject-specific realistic head models from MRI. We investigate the inter-subject variability of skull conductivity and relate it to skull thickness, age and gender of the subjects, to the individual scalp P20/N20 surface distance between the P20 potential peak and the N20 potential trough as well as to the individual source depth of the P20/N20 source. We found a considerable inter-subject variability for (calibrated) skull conductivity (8.44 ± 4.84 mS/m) and skull thickness (5.97 ± 1.19 mm) with a statistically significant correlation between them (rho = 0.52). Age showed a statistically significant negative correlation with skull conductivity (rho =-0.5). Furthermore, P20/N20 surface distance and source depth showed large inter-subject variability of 12.08 ± 3.21 cm and 15.45 ± 4.54 mm, respectively, but there was no significant correlation between them. We also found no significant differences among gender subgroups for the investigated measures. It is thus important to take the inter-subject variability of skull conductivity and thickness into account by means of using subject-specific calibrated realistic head modeling.
