Bioelectromagnetics最新文献

The electrical conductivity of human tissues is a major source of uncertainty when modelling the interactions between electromagnetic fields and the human body. The aim of this study is to estimate human tissue conductivities in vivo over the low-frequency range, from 30 Hz to 1 MHz. Noninvasive impedance measurements, medical imaging, and 3D surface scanning were performed on the forearms of ten volunteer test subjects. This data set was used to create subject-specific forearm models, numerically solve an electrostatic forward problem, after which the tissue conductivities could be estimated by solving a probabilistic inverse problem. The electrical conductivity of skeletal muscle was found to be highly anisotropic at frequencies below 10 kHz, with conductivities of 0.13 (95% credible interval (CrI): 0.10–0.16) S/m perpendicular and 0.56 (CrI: 0.52–0.60) S/m parallel to the muscle fibre direction. This anisotropy decreased with increasing frequency with these values being 0.65 (CrI: 0.48–1.00) S/m and 0.78 (CrI: 0.72–0.85) S/m at 1 MHz. The conductivity of subcutaneous fat was found to be almost constant across the considered frequency range, with values of 0.21 (CrI: 0.12–0.31) S/m and 0.22 (CrI: 0.07–0.37) S/m at 10 kHz and 1 MHz, respectively. Our study provides robust uncertainty bounds for human tissue conductivity values, which are crucial in the computational assessment of human electromagnetic field exposure. Additionally, our findings are applicable to other fields of modelling such as medical stimulation or measurement technologies.

As the clinical applicability of peripheral nerve stimulation (PNS) expands, the need for PNS-specific safety criteria becomes pressing. This study addresses this need, utilizing a novel machine learning and computational bio-electromagnetics modeling platform to establish a safety criterion that captures the effects of fields and currents induced on axons. Our approach is comprised of three steps: experimentation, model creation, and predictive simulation. We collected high-resolution images of control and stimulated rat sciatic nerve samples at varying stimulation intensities and performed high-resolution image segmentation. These segmented images were used to train machine learning tools for the automatic classification of morphological properties of control and stimulated PNS nerves. Concurrently, we utilized our quasi-static Admittance Method-NEURON (AM-NEURON) computational platform to create realistic nerve models and calculate induced currents and charges, both critical elements of nerve safety criteria. These steps culminate in a cellular-level correlation between morphological changes and electrical stimulation parameters. This correlation informs the determination of thresholds of electrical parameters that are found to be associated with damage, such as maximum cell charge density. The proposed methodology and resulting criteria combine experimental findings with computational modeling to generate a safety threshold curve that captures the relationship between stimulation current and the potential for axonal damage. Although focused on a specific exposure condition, the approach presented here marks a step towards developing context-specific safety criteria in PNS neurostimulation, encouraging similar analyses across varied neurostimulation scenarios. Bioelectromagnetics.

In this paper, we present the design, RF-EMF performance, and a comprehensive uncertainty analysis of the reverberation chamber (RC) exposure systems that have been developed for the use of researchers at the University of Wollongong Bioelectromagnetics Laboratory, Australia, for the purpose of investigating the biological effects of RF-EMF in rodents. Initial studies, at 1950 MHz, have focused on investigating thermophysiological effects of RF exposure, and replication studies related to RF-EMF exposure and progression of Alzheimer's disease (AD) in mice predisposed to AD. The RC exposure system was chosen as it allows relatively unconstrained movement of animals during exposures which can have the beneficial effect of minimizing stress-related, non-RF-induced biological and behavioral changes in the animals. The performance of the RCs was evaluated in terms of the uniformity of the Whole-Body Average-Specific Absorption Rate (WBA-SAR) in mice for a given RF input power level. The expanded uncertainty in WBA-SAR estimates was found to be 3.89 dB. Validation of WBA-SAR estimates based on a selected number of temperature measurements in phantom mice found that the maximum ratio of the temperature-derived WBA-SAR to the computed WBA-SAR was 1.1 dB, suggesting that actual WBA-SAR is likely to be well within the expanded uncertainties.

Temporal interference stimulation (TIS) is a new form of transcranial electrical stimulation (tES) that has been proposed as a method for targeted, noninvasive stimulation of deep brain structures. While TIS holds promise for a variety of clinical and nonclinical applications, little data is yet available regarding its effects in humans and its mechanisms of action. To inform the design and safe conduct of experiments involving TIS, researchers require quantitative guidance regarding safe exposure limits and other safety considerations. To this end, we undertook a two-part effort to determine frequency-dependent thresholds for applied currents below which TIS is unlikely to pose risk to humans in terms of heating or unwanted stimulation. In Part II of this effort, described here, we draw on a previously compiled list (see Part I) of adverse effects (AEs) reported for transcranial direct/alternating current stimulation (tDCS/ACS), deep brain stimulation (DBS), and TIS to determine biophysics-informed exposure metrics for assessing safety. Using an in silico approach, we conduct multiphysics simulations of various tACS, DBS, and TIS exposure scenarios in an anatomically detailed head and brain model. By matching the stimulation in terms of the identified exposure metrics, we infer frequency-dependent TIS parameters that produce exposure conditions equivalent to those known to be safe for tACS and DBS. Based on the results of our simulations and existing knowledge regarding tES and DBS safety, we propose frequency-dependent thresholds below which TIS voltages and currents are unlikely to pose a risk to humans. Safety-related data from ongoing and future human studies are required to verify and refine the thresholds proposed here.

Readily available animal tissue, such as ground beef, is a convenient material to represent the dielectric properties of biological tissue when validating microwave imaging and sensing hardware and techniques. The reliable use of these materials depends on the accurate characterization of their properties. In this work, the effect of physiologically relevant levels of dehydration on ex vivo tissue samples is quantified while controlling for variation within and between samples. Seven commercial ground beef samples (90% lean muscle, 10% fat) are dehydrated from 0.0% to 7.0% in 1.0% increments by weight. Dielectric measurements are collected using a conventional dielectric probe technique from 0.2 to 6 GHz. A linear mixed-effects model is used to control for within- and between-sample variation while modeling the effect of dehydration and dispersion across frequency. Significant ($��\; ��p�\; �<�\; �0.05��$) changes are noted in both permittivity and conductivity due to sample dehydration. For a 1% change in weight due to dehydration, changes in permittivity (5.1%–5.6%) and conductivity (3.2%–5.7%) are reported. These changes are important for the use of large muscle-based phantoms in microwave sensing and imaging validation, as well as the feasibility of microwave hydration assessment. The statistical model used here can be applied to similar research questions and can augment existing frameworks for reporting dielectric measurements.

Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational models of neuromodulation can allow the exploration of large parameter spaces and the automatic optimization of stimulation devices and strategies, their high time complexity hinders their use on a large scale. We recently proposed the use of machine learning-based surrogate models to estimate the activation of nerve fibers under electrical stimulation, producing a considerable speed-up with respect to biophysically accurate models of fiber excitation while retaining good predictivity. Here, we characterize the performance of four frequently employed machine learning algorithms and provide an illustrative example of their ability to generalize to unseen stimulation protocols, stimulating sites, and nerve sections. We then discuss how the ability to generalize to such scenarios is relevant to different optimization protocols, paving the way for the automatic optimization of neuromodulation applications.