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BACKGROUND: Transcranial direct current stimulation (tDCS) is used to modulate neuronal activity, but the exact mechanism of action (MOA) is unclear. This study investigates tDCS-induced modulation of the corticospinal excitability and the underlying MOA. By anesthetizing the scalp before applying tDCS and by stimulating the cheeks, we investigated whether stimulation of peripheral and/or cranial nerves contributes to the effects of tDCS on corticospinal excitability. MATERIALS AND METHODS: In a randomized cross-over study, four experimental conditions with anodal direct current stimulation were compared in 19 healthy volunteers: 1) tDCS over the motor cortex (tDCS-MI), 2) tDCS over the motor cortex with a locally applied topical anesthetic (TA) on the scalp (tDCS-MI + TA), 3) DCS over the cheek region (DCS-C), and 4) sham tDCS over the motor cortex(sham). tDCS was applied for 20 minutes at 1 mA. Motor evoked potentials (MEPs) were measured before tDCS and immediately, 15, 30, 45, and 60 minutes after tDCS. A questionnaire was used to assess the tolerability of tDCS. RESULTS: A significant MEP amplitude increase compared with baseline was found 30 minutes after tDCS-MI, an effect still observed 60 minutes later; no time∗condition interaction effect was detected. In the other three conditions (tDCS-MI + TA, DCS-C, sham), no significant MEP modulation was found. The questionnaire indicated that side effects are significantly lower when the local anesthetic was applied before stimulation than in the other three conditions. CONCLUSIONS: The significant MEP amplitude increase observed from 30 minutes on after tDCS-MI supports the modulatory effect of tDCS on corticospinal neurotransmission. This effect lasted one hour after stimulation. The absence of a significant modulation when a local anesthetic was applied suggests that effects of tDCS are not solely established through direct cortical stimulation but that stimulation of peripheral and/or cranial nerves also might contribute to tDCS-induced modulation.
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Conventional transcranial electric stimulation(tES) using standard anatomical positions for the electrodes and standard stimulation currents is frequently not sufficiently selective in targeting and reaching specific brain locations, leading to suboptimal application of electric fields. Recent advancements in in vivo electric field characterization may enable clinical researchers to derive better relationships between the electric field strength and the clinical results. Subject-specific electric field simulations could lead to improved electrode placement and more efficient treatments. Through this narrative review, we present a processing workflow to personalize tES for focal epilepsy, for which there is a clear cortical target to stimulate. The workflow utilizes clinical imaging and electroencephalography data and enables us to relate the simulated fields to clinical outcomes. We review and analyze the relevant literature for the processing steps in the workflow, which are the following: tissue segmentation, source localization, and stimulation optimization. In addition, we identify shortcomings and ongoing trends with regard to, for example, segmentation quality and tissue conductivity measurements. The presented processing steps result in personalized tES based on metrics like focality and field strength, which allow for correlation with clinical outcomes.
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OBJECTIVE: Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems. METHODS: The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom. RESULTS: The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin. CONCLUSION: The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems. SIGNIFICANCE: The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed.
