Bioelectromagnetics最新文献

Neuromodulation with low-intensity focused ultrasound (LIFUS) holds significant promise for noninvasive treatment of neurological disorders, but its success relies heavily on accurately targeting specific brain regions. Computational model predictions can be used to optimize LIFUS, but uncertain acoustic tissue properties can affect prediction accuracy. The Monte Carlo method is often used to quantify the impact of uncertainties, but many iterations are generally needed for accurate estimates. We studied a surrogate model based on polynomial chaos expansion (PCE) to quantify the uncertainty in the LIFUS acoustic intensity field caused by tissue acoustic property uncertainties. The PCE approach was benchmarked against Monte Carlo method for LIFUS in three different head models. We also investigated the effect of the number of PCE samples on the accuracy of the surrogate model. Our results show that the PCE surrogate model requires only 20 simulation samples to estimate the mean and standard deviation of the acoustic intensity field with high accuracy compared to 100 samples needed for Monte Carlo method. The root mean squared percentage error (RMSPE) in the mean acoustic intensity field was less than 1.5%, with a maximum error of less than 0.5 W/cm² (< 1% of the focus peak intensity in water), while the RMSPE in the standard deviation was less than 9%, with a maximum error of less than 0.3 W/cm². The accuracy of the PCE surrogate model, and the limited number of iterations it requires makes it a promising tool for quantifying the uncertainty in the acoustic intensity field in LIFUS applications.

Temporal interference stimulation (TIS) is a new form of transcranial electrical stimulation (tES) that has been proposed as a method for targeted, non-invasive stimulation of deep brain structures. While TIS holds promise for a variety of clinical and non-clinical applications, little data is yet available regarding its effects in humans and its mechanisms of action. In order 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. Part I of this effort, described here, comprises a summary of the current knowledge pertaining to the safety of TIS and related techniques. Specifically, we provide: i) a broad overview of the electrophysiological impacts neurostimulation, ii) a review of the (bio-)physical principles underlying the mechanisms of action of transcranial alternating/direct stimulation (tACS/tDCS), deep brain stimulation (DBS), and TIS, and iii) a comprehensive survey of the adverse effects (AEs) associated with each technique as reported in the scientific literature and regulatory and clinical databases. In Part II, we perform an in silico study to determine field exposure metrics for tDCS/tACS and DBS under normal (safe) operating conditions and infer frequency-dependent current thresholds for TIS that result in equivalent levels of exposure.

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.