Effect of Ambient Lighting on Frequency Dependence in Transcranial Electrical Stimulation-Induced Phosphenes

Inconsistencies have been found in the relationship between ambient lighting conditions and frequency-dependence in transcranial electric current stimulation (tECS) induced phosphenes. Using a within-subjects design across lighting condition (dark, mesopic [dim], photopic [bright]) and tECS stimulation frequency (10, 13, 16, 18, 20 Hz), this study determined phosphene detection thresholds in 24 subjects receiving tECS using an FPz-Cz montage. Minima phosphene thresholds were found at 16 Hz in mesopic, 10 Hz in dark and 20 Hz in photopic lighting conditions, with these thresholds being substantially lower for mesopic than both dark (60% reduction) and photopic (56% reduction), conditions. Further, whereas the phosphene threshold-stimulation frequency relation was linear in the dark (increasing with frequency) and photopic (decreasing with frequency) conditions, a quadratic function was found for the mesopic condition (where it followed the linear increase of the dark condition from 10-16 Hz, and the linear decrease of the photopic condition from 16-20 Hz). The results clearly demonstrate that ambient lighting is an important factor in the detection of tECS-induced phosphenes, and that mesopic conditions are most suitable for obtaining overall phosphene thresholds.

Intracranial Electric Field Recording During Multichannel Transcranial Electrical Stimulation

Background: Multichannel transcranial electrical stimulation (tES) modeling and optimization have been widely studied in recent years. Its theoretical bases include quasi-static assumption and linear superposition. However, there is still a lack of direct in vivo evidence to validate the simulation model and theoretical assumptions. Methods: We directly measured the multichannel tES-induced voltage changes with implanted stereotactic-electroencephalographic (sEEG) electrodes in 12 epilepsy subjects. By combining these measured data, we investigate the linear superposition and prediction accuracy of simulation models for multi-electrode stimulation and further compare the induced EF differences between transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS). Results: Our in vivo measurements demonstrated that the multi-electrode tES-induced voltages were almost equal to the sum of the voltages generated independently by bipolar stimulation. Both measured voltages and electric fields obtained in vivo were highly correlated with the predicted values in our cohort (Voltages: r = 0.92, p < 0.001; electric fields: r = 0.74, p < 0.001). Under the same stimulation intensity, the tDCS-induced peak-zero voltages were highly correlated with the values of tACS (r = 0.99, p < 0.001; s = 0.99). Conclusions: The in vivo measurements provides confirmatory results for linear superposition and quasi-static assumption within the human brain. Furthermore, we found that the individualized simulation model reliably predicted the multi-electrode tES-induced electric fields.

Modeling implanted metals in electrical stimulation applications

BackgroundMetal implants impact the dosimetry assessment in electrical stimulation techniques. Therefore, they need to be included in numerical models. While currents in the body are ionic, metals only allow electron transport. In fact, charge transfer between tissues and metals requires electric fields to drive the electrochemical reactions at the interface. Thus, metal implants may act as insulators or as conductors depending on the scenario.Objective/HypothesisThe aim of this paper is to provide a theoretical argument that guides the choice of the correct representation of metal implants using purely electrical models but considering the electrochemical nature of the problem in the technology of interest.MethodsWe built a simple model of a metal implant exposed to a homogeneous electric field of various magnitudes to represent both weak (e.g., tDCS), medium (TMS) or strong field stimulation. The same geometry was solved using two different models: a purely electric one (with different conductivities for the implant), and an electrochemical one. As an example of application, we also modeled a transcranial electrical stimulation (tES) treatment in a realistic head model with a skull plate using a high and low conductivity value for the plate.ResultsMetal implants generally act as electric insulators when exposed to electric fields up to around 100 V/m (tES and TMS range) and they only resemble a perfect conductor for fields in the order of 1000 V/m and above. The results are independent of the implant’s metal, but they depend on its geometry.Conclusion(s)Metal implants can be accurately represented by a simple electrical model of constant conductivity, but an incorrect model choice can lead to large errors in the dosimetry assessment. In particular, tES modeling with implants incorrectly treated as conductors can lead to errors of 50% in induced fields or more. Our results can be used as a guide to select the correct model in each scenario.

An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

Background To apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS. Methods EEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task. Power spectral densities of the EEG data were extracted from six frequency bands (delta, theta, alpha, beta, gamma, and full) and were independently used to train and test an artificial neural network for motor hotspot identification. The 3D coordinate information of individual motor hotspots identified by TMS were quantitatively compared with those estimated by our EEG-based motor-hotspot-identification approach to assess its feasibility. Results The minimum mean error distance between the motor hotspot locations identified by TMS and our proposed motor-hotspot-identification approach was 0.22 ± 0.03 cm, demonstrating the proof-of-concept of our proposed EEG-based approach. A mean error distance of 1.32 ± 0.15 cm was measured when using only nine channels attached to the middle of the motor cortex, showing the possibility of practically using the proposed motor-hotspot-identification approach based on a relatively small number of EEG channels. Conclusion We demonstrated the feasibility of our novel EEG-based motor-hotspot-identification method. It is expected that our approach can be used as an alternative to TMS for motor hotspot identification. In particular, its usability would significantly increase when using a recently developed portable tES device integrated with an EEG device.

Using Transcranial Electrical Stimulation in Audiological Practice: The Gaps to Be Filled

The effects of transcranial electrical stimulation (tES) approaches have been widely studied for many decades in the motor field, and are well known to have a significant and consistent impact on the rehabilitation of people with motor deficits. Consequently, it can be asked whether tES could also be an effective tool for targeting and modulating plasticity in the sensory field for therapeutic purposes. Specifically, could potentiating sensitivity at the central level with tES help to compensate for sensory loss? The present review examines evidence of the impact of tES on cortical auditory excitability and its corresponding influence on auditory processing, and in particular on hearing rehabilitation. Overall, data strongly suggest that tES approaches can be an effective tool for modulating auditory plasticity. However, its specific impact on auditory processing requires further investigation before it can be considered for therapeutic purposes. Indeed, while it is clear that electrical stimulation has an effect on cortical excitability and overall auditory abilities, the directionality of these effects is puzzling. The knowledge gaps that will need to be filled are discussed.

A Future of Current Flow Modelling for Transcranial Electrical Stimulation?

Purpose of Review Transcranial electrical stimulation (tES) is used to non-invasively modulate brain activity in health and disease. Current flow modeling (CFM) provides estimates of where and how much electrical current is delivered to in the brain during tES. It therefore holds promise as a method to reduce commonplace variability in tES delivery and, in turn, the outcomes of stimulation. However, the adoption of CFM has not yet been widespread and its impact on tES outcome variability is unclear. Here, we discuss the potential barriers to effective, practical CFM-informed tES use. Recent Findings CFM has progressed from models based on concentric spheres to gyri-precise head models derived from individual MRI scans. Users can now estimate the intensity of electrical fields (E-fields), their spatial extent, and the direction of current flow in a target brain region during tES. Here. we consider the multi-dimensional challenge of implementing CFM to optimise stimulation dose: this requires informed decisions to prioritise E-field characteristics most likely to result in desired stimulation outcomes, though the physiological consequences of the modelled current flow are often unknown. Second, we address the issue of a disconnect between predictions of E-field characteristics provided by CFMs and predictions of the physiological consequences of stimulation which CFMs are not designed to address. Third, we discuss how ongoing development of CFM in conjunction with other modelling approaches could overcome these challenges while maintaining accessibility for widespread use. Summary The increasing complexity and sophistication of CFM is a mandatory step towards dose control and precise, individualised delivery of tES. However, it also risks counteracting the appeal of tES as a straightforward, cost-effective tool for neuromodulation, particularly in clinical settings.

A computational study on the optimization of transcranial temporal interfering stimulation with high-definition electrode using unsupervised neural network

Transcranial temporal interfering stimulation (tTIS) can focally stimulate deep parts of the brain, which are related to specific functions, by using beats at two high AC frequencies that do not affect the human brain. However, it has limitations in terms of calculation time and precision for optimization because of its complexity and non-linearity. We aimed to propose a method using an unsupervised neural network (USNN) for tTIS to optimize quickly the interfering current value of high-definition electrodes, which can finely stimulate the deep part of the brain, and analyze the performance and characteristics of tTIS. A computational study was conducted using 16 realistic head models. This method generated the strongest stimulation on the target, even when targeting deep areas or multi-target stimulation. The tTIS was robust with target depth compared with transcranial alternating current stimulation, and mis-stimulation could be reduced compared with the case of using two-pair inferential stimulation. Optimization of a target could be performed in 3 min. By proposing the USNN for tTIS, we showed that the electrode currents of tTIS can be optimized quickly and accurately, and the possibility of stimulating the deep part of the brain precisely with transcranial electrical stimulation was confirmed.

Transplantation of rat cranial bone-derived mesenchymal stem cells promotes functional recovery in rats with spinal cord injury

Cell-based therapy using mesenchymal stem cells (MSCs) is a novel treatment strategy for spinal cord injury (SCI). MSCs can be isolated from various tissues, and their characteristics vary based on the source. However, reports demonstrating the effect of transplanted rat cranial bone-derived MSCs (rcMSCs) on rat SCI models are lacking. In this study, we determined the effect of transplanting rcMSCs in rat SCI models. MSCs were established from collected bone marrow and cranial bones. SCI rats were established using the weight-drop method and transplanted intravenously with MSCs at 24 h post SCI. The recovery of motor function and hindlimb electrophysiology was evaluated 4 weeks post transplantation. Electrophysiological recovery was evaluated by recording the transcranial electrical stimulation motor-evoked potentials. Tissue repair after SCI was assessed by calculating the cavity ratio. The expression of genes involved in the inflammatory response and cell death in the spinal cord tissue was assessed by real-time polymerase chain reaction. The transplantation of rcMSCs improved motor function and electrophysiology recovery, and reduced cavity ratio. The expression of proinflammatory cytokines was suppressed in the spinal cord tissues of the rats that received rcMSCs. These results demonstrate the efficacy of rcMSCs as cell-based therapy for SCI.