Ambiguity in the interpretation of the low-frequency dielectric properties of biological tissues

Simulation study on high spatio-temporal resolution acousto-electrophysiological neuroimaging

Objective.Acousto-electrophysiological neuroimaging (AENI) is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will be modulated onto the ultrasonic frequency. The region's electrical activity can subsequently be retrieved via demodulation of the measured signal. In this study, the feasibility of this hypothesized technique is tested.Approach.This is done by calculating the forward electroencephalography response under quasi-static assumptions. The head is simplified as a set of concentric spheres. Two sizes are evaluated representing human and mouse brains. Moreover, feasibility is assessed for wet and dry transcranial, and for cortically placed electrodes. The activity sources are modeled by dipoles, with their current intensity profile drawn from a power-law power spectral density.Results.It is shown that mechanical vibration modulates the endogenous activity onto the ultrasonic frequency. The signal strength depends non-linearly on the alignment between dipole orientation, vibration direction and recording point. The strongest signal is measured when these three dependencies are perfectly aligned. The signal strengths are in the pV-range for a dipole moment of 5 nAm and ultrasonic pressures within Food and Drug Administration (FDA)-limits. The endogenous activity can then be accurately reconstructed via demodulation. Two interference types are investigated: vibrational and static. Depending on the vibrational interference, it is shown that millimeter resolution signal detection is possible also for deep brain regions. Subsequently, successful demodulation depends on the static interference, that at MHz-range has to be sub-picovolt.Significance.Our results show that mechanical vibration is a possible underlying mechanism of acousto-electrophyisological neuroimaging. This paper is a first step towards improved understanding of the conditions under which AENI is feasible.

Study on the safety assessment and protection design of human exposure to low-frequency magnetic fields in electric vehicles

As the power performance of electric vehicles continues to improve, the human body may be exposed to electromagnetic threats in the cabin. This study tested an electric vehicle to analyze the low-frequency magnetic field distribution in the cabin and to assess the safety of human low-frequency magnetic field exposure. A simulation analysis of human electromagnetic exposure was carried out to obtain the magnetic flux density, induced electric field strength and induced current density, and the test results were much lower than the limits specified in GB8702-2014 and the International Commission on Non-Ionizing Radiation Protection, and the relative error between the simulation results and the test results was <15%. This paper investigates the frequency, driving current, vehicle body material and cable layout to explore the law of human body induced electromagnetic field changing with power cable current, and provides theoretical reference for the design of human body low-frequency magnetic field protection.

Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images

Electric fields find use in tissue engineering but also in sensor applications besides the broad classical application range. Accurate numerical models of electrical stimulation devices can pave the way for effective therapies in cartilage regeneration. To this end, the dielectric properties of the electrically stimulated tissue have to be known. However, knowledge of the dielectric properties is scarce. Electric field-based methods such as impedance spectroscopy enable determining the dielectric properties of tissue samples. To develop a detailed understanding of the interaction of the employed electric fields and the tissue, fine-grained numerical models based on tissue-specific 3D geometries are considered. A crucial ingredient in this approach is the automated generation of numerical models from biomedical images. In this work, we explore classical and artificial intelligence methods for volumetric image segmentation to generate model geometries. We find that deep learning, in particular the StarDist algorithm, permits fast and automatic model geometry and discretisation generation once a sufficient amount of training data is available. Our results suggest that already a small number of 3D images (23 images) is sufficient to achieve 80% accuracy on the test data. The proposed method enables the creation of high-quality meshes without the need for computer-aided design geometry post-processing. Particularly, the computational time for the geometrical model creation was reduced by half. Uncertainty quantification as well as a direct comparison between the deep learning and the classical approach reveal that the numerical results mainly depend on the cell volume. This result motivates further research into impedance sensors for tissue characterisation. The presented approach can significantly improve the accuracy and computational speed of image-based models of electrical stimulation for tissue engineering applications.

Experimental and numerical methods to ensure comprehensible and replicable alternating current electrical stimulation experiments

Electrical stimulation has received increasing attention for decades for its application in regenerative medicine. Applications range from bone growth stimulation over cartilage regeneration to deep brain stimulation. Despite all research efforts, translation into clinical use has not yet been achieved in all fields. Recent critical assessments have identified limited documentation and monitoring of preclinical in vitro and in vivo experiments as possible reasons hampering clinical translation. In this work, we present experimental and numerical methods to determine the crucial quantities of electrical stimulation such as the electric field or current density. Knowing the stimulation quantities contributes to comprehending the biological response to electrical stimulation and to finally developing a reliable dose-response curve. To demonstrate the methods, we consider a direct contact electrical stimulation experiment that stands representative for a broad class of stimulation experiments. Electrochemical effects are addressed and methods to integrate them into numerical simulations are evaluated. A focus is laid on affordable lab equipment and reproducible open-source software solutions. Finally, clear guidelines to ensure replicability of electrical stimulation experiments are formulated.

Optimization design of interdigitated microelectrodes with an insulation layer on the connection tracks to enhance efficiency of assessment of the cell viability

BACKGROUND

Microelectrical Impedance Spectroscopy (µEIS) is a tiny device that utilizes fluid as a working medium in combination with biological cells to extract various electrical parameters. Dielectric parameters of biological cells are essential parameters that can be extracted using µEIS. µEIS has many advantages, such as portability, disposable sensors, and high-precision results.

RESULTS

The paper compares different configurations of interdigitated microelectrodes with and without a passivation layer on the cell contact tracks. The influence of the number of electrodes on the enhancement of the extracted impedance for different types of cells was provided and discussed. Different types of cells are experimentally tested, such as viable and non-viable MCF7, along with different buffer solutions. This study confirms the importance of µEIS for in vivo and in vitro applications. An essential application of µEIS is to differentiate between the cells' sizes based on the measured capacitance, which is indirectly related to the cells' size. The extracted statistical values reveal the capability and sensitivity of the system to distinguish between two clusters of cells based on viability and size.

CONCLUSION

A completely portable and easy-to-use system, including different sensor configurations, was designed, fabricated, and experimentally tested. The system was used to extract the dielectric parameters of the Microbeads and MCF7 cells immersed in different buffer solutions. The high sensitivity of the readout circuit, which enables it to extract the difference between the viable and non-viable cells, was provided and discussed. The proposed system can extract and differentiate between different types of cells based on cells' sizes; two other polystyrene microbeads with different sizes are tested. Contamination that may happen was avoided using a Microfluidic chamber. The study shows a good match between the experiment and simulation results. The study also shows the optimum number of interdigitated electrodes that can be used to extract the variation in the dielectric parameters of the cells without leakage current or parasitic capacitance.

Dielectric properties of the non-glycated and in vitro methylglyoxal-glycated cornea of the rabbit eye

The dielectric properties of the non-glycated and in vitro methylglyoxal-glycated cornea of the rabbit eye were tested in the frequency range of 200 Hz to 100 kHz of the electric field and at temperatures of 25 to 140 °C. The denaturation temperature (T_d) for the non-glycated cornea and the non-enzymatically glycated cornea are approximately 45 and 55 °C, respectively. The mechanism of proton conduction up to T_d in a glycated cornea requires more energy, i.e. more than twice the activation energy (ΔH) than in non-glycated tissue. The dielectric spectra for both examined tissues showed the same characteristic frequency of about 7 kHz assigned to the orientation relaxation time of the polar side groups inside the corneal stroma. These results may be useful in the surgical treatment of the cornea using conductive keratoplasty and in tissue engineering for clinical applications to regenerate this tissue. The medical use of these physico-biological techniques is important because the human cornea protects all eye tissues from various environmental factors.

Quasi-static approximation error of electric field analysis for transcranial current stimulation

Objective.Numerical modeling of electric fields induced by transcranial alternating current stimulation (tACS) is currently a part of the standard procedure to predict and understand neural response. Quasi-static approximation (QSA) for electric field calculations is generally applied to reduce the computational cost. Here, we aimed to analyze and quantify the validity of the approximation over a broad frequency range.Approach.We performed electromagnetic modeling studies using an anatomical head model and considered approximations assuming either a purely ohmic medium (i.e. static formulation) or a lossy dielectric medium (QS formulation). The results were compared with the solution of Maxwell's equations in the cases of harmonic and pulsed signals. Finally, we analyzed the effect of electrode positioning on these errors.Main results.Our findings demonstrate that the QSA is valid and produces a relative error below 1% up to 1.43 MHz. The largest error is introduced in the static case, where the error is over 1% across the entire considered spectrum and as high as 20% in the brain at 10 Hz. We also highlight the special importance of considering the capacitive effect of tissues for pulsed waveforms, which prevents signal distortion induced by the purely ohmic approximation. At the neuron level, the results point a difference of sense electric field as high as 22% at focusing point, impacting pyramidal cells firing times.Significance.QSA remains valid in the frequency range currently used for tACS. However, neglecting permittivity (static formulation) introduces significant error for both harmonic and non-harmonic signals. It points out that reliable low frequency dielectric data are needed for accurate transcranial current stimulation numerical modeling.

Linking profiles of pathway activation with clinical motor improvements - A retrospective computational study

BACKGROUND

Deep brain stimulation (DBS) is an established therapy for patients with Parkinson's disease. In silico computer models for DBS hold the potential to inform a selection of stimulation parameters. In recent years, the focus has shifted towards DBS-induced firing in myelinated axons, deemed particularly relevant for the external modulation of neural activity.

OBJECTIVE

The aim of this project was to investigate correlations between patient-specific pathway activation profiles and clinical motor improvement.

METHODS

We used the concept of pathway activation modeling, which incorporates advanced volume conductor models and anatomically authentic fiber trajectories to estimate DBS-induced action potential initiation in anatomically plausible pathways that traverse in close proximity to targeted nuclei. We applied the method on two retrospective datasets of DBS patients, whose clinical improvement had been evaluated according to the motor part of the Unified Parkinson's Disease Rating Scale. Based on differences in outcome and activation levels for intrapatient DBS protocols in a training cohort, we derived a pathway activation profile that theoretically induces a complete alleviation of symptoms described by UPDRS-III. The profile was further enhanced by analyzing the importance of matching activation levels for individual pathways.

RESULTS

The obtained profile emphasized the importance of activation in pathways descending from the motor-relevant cortical regions as well as the pallidothalamic pathways. The degree of similarity of patient-specific profiles to the optimal profile significantly correlated with clinical motor improvement in a test cohort.

CONCLUSION

Pathway activation modeling has a translational utility in the context of motor symptom alleviation in Parkinson's patients treated with DBS.

Using a Digital Twin of an Electrical Stimulation Device to Monitor and Control the Electrical Stimulation of Cells in vitro

Electrical stimulation for application in tissue engineering and regenerative medicine has received increasing attention in recent years. A variety of stimulation methods, waveforms and amplitudes have been studied. However, a clear choice of optimal stimulation parameters is still not available and is complicated by ambiguous reporting standards. In order to understand underlying cellular mechanisms affected by the electrical stimulation, the knowledge of the actual prevailing field strength or current density is required. Here, we present a comprehensive digital representation, a digital twin, of a basic electrical stimulation device for the electrical stimulation of cells in vitro. The effect of electrochemical processes at the electrode surface was experimentally characterised and integrated into a numerical model of the electrical stimulation. Uncertainty quantification techniques were used to identify the influence of model uncertainties on relevant observables. Different stimulation protocols were compared and it was assessed if the information contained in the monitored stimulation pulses could be related to the stimulation model. We found that our approach permits to model and simulate the recorded rectangular waveforms such that local electric field strengths become accessible. Moreover, we could predict stimulation voltages and currents reliably. This enabled us to define a controlled stimulation setting and to identify significant temperature changes of the cell culture in the monitored voltage data. Eventually, we give an outlook on how the presented methods can be applied in more complex situations such as the stimulation of hydrogels or tissue in vivo.