Role of skin tissue layers and ultra-structure in transcutaneous electrical stimulation including tDCS

Individual and group-level optimization of electric field in deep brain region during multichannel transcranial electrical stimulation

Electrode montage optimization for transcranial electric stimulation (tES) is a challenging topic for targeting a specific brain region. Targeting the deep brain region is difficult due to tissue inhomogeneity, resulting in complex current flow. In this study, a simplified protocol for montage optimization is proposed for multichannel tES (mc-tES). The purpose of this study was to reduce the computational cost for mc-tES optimization and to evaluate the mc-tES for deep brain regions. Optimization was performed using a simplified protocol for montages under safety constraints with 20 anatomical head models. The optimization procedure is simplified using the surface EF of the deep brain target region, considering its small volume and non-concentric distribution of the electrodes. Our proposal demonstrated that the computational cost was reduced by >90%. A total of six-ten electrodes were necessary for robust EF in the target region. The optimization with surface EF is comparable to or marginally better than using conventional volumetric EF for deep brain tissues. An electrode montage with a mean injection current amplitude derived from individual analysis was demonstrated to be useful for targeting the deep region at the group level. The optimized montage and injection current were derived at the group level. Our proposal at individual and group levels showed great potential for clinical application.

An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling

Objective.In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.Approach.We propose, describe, and systematically investigate an AMR method using the boundary element method with fast multipole acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy. The implemented AMR method's accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a 'silver-standard' solution found by subsequent 4:1 global refinement of the adaptively-refined model.Main results.Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models.Significance.This error has especially important implications for TES dosing prediction-where the stimulation strength plays a central role-and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.

Optimizing intracranial electric field distribution through temperature-driven scalp conductivity adjustments in transcranial electrical stimulation

Transcranial electrical stimulation (TES) is a promising non-invasive neuromodulation technique. How to increase the current intensity entering the skull and reduce scalp shunting has become a key factor significantly influencing regulatory efficacy. In this study, we introduce a novel approach for optimizing TES by adjusting local scalp temperature to modulate scalp conductivity. We have developed simulation models for TES-induced electric fields and for temperature-induced alterations in scalp conductivity. Two common types of stimulation montage (M1-SO and 4 × 1 montage) were adopted for the evaluation of effectiveness. We observed that the modulation of scalp temperature has a significant impact on the distribution of the electric field within the brain during TES. As local scalp temperature decreases, there is an increase in the maximum electric field intensity within the target area, with the maximum change reaching 18.3%, when compared to the electric field distribution observed under normal scalp temperature conditions. Our study provide insights into the practical implementation challenges and future directions for this innovative methodology.

Characterization of perception by transcutaneous electrical Stimulation in terms of tingling intensity and temporal dynamics

Electrotactile feedback is a cost-effective and versatile method to provide new information or to augment intrinsic tactile feedback. As tactile feedback provides critical information for human-environment interaction, electrotactile feedback, accordingly, has many purposes to improve the quality of human-environment interaction in both direct and remote settings. However, electrotactile feedback overlays tingling sensation on top of the natural tactile feedback. To better characterize electrotactile feedback and understand the origin of the tingling sensation, a need arises to characterize the human perception of electrotactile feedback qualitatively and quantitatively, while varying the key stimulation parameters, namely amplitude and frequency. This study consists of two experiments. In the first experiment, the voltage for each subject was characterized by setting perception and discomfort thresholds. In the second experiment, subjects received electrical stimulation in 9 different combinations of voltages and frequencies. On delivering stimulation with each parameter combination, subjects reported their perception in two comparative scales-pressure vs. tingling and constant vs. pulsing. Subjects also reported the location of perception for stimulation with every parameter combination. More tingling and less pressure was reported as frequency increased, while the tingling-pressure percept was not affected by the amplitude change. Additionally, less pulsing and more constant was reported as frequency increased, while the pulsing-constant percept was not affected by the amplitude change. Concurrently, the normalized level of voltage thresholds was decreased as frequency increased. Dependency of tingling-pressure percept on stimulation frequency suggests that incongruency between the stimulation frequency and the natural firing rate of the sensory neuron would be an important factor of the tingling sensation. This study is a steppingstone to further demystify the origin of the tingling percept caused by electrical stimulation, thus broadening the use of transcutaneous electrical stimulation as a way of providing tactile cue or augmentation.

Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study

BACKGROUND

Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing.

OBJECTIVES

This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures.

METHODS

Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals.

RESULTS

In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model.

CONCLUSIONS

Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.

An Adaptive H-Refinement Method for the Boundary Element Fast Multipole Method for Quasi-static Electromagnetic Modeling

Objective

In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.

Approach

We propose, describe, and systematically investigate an AMR method using the Boundary Element Method with Fast Multipole Acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy.The implemented AMR method's accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a "silver-standard" solution found by subsequent 4:1 global refinement of the adaptively-refined model.

Main Results

Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models.

Significance

This error has especially important implications for TES dosing prediction - where the stimulation strength plays a central role - and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.

Quasi-static pipeline in electroconvulsive therapy computational modeling

BACKGROUND

Computational models of current flow during Electroconvulsive Therapy (ECT) rely on the quasi-static assumption, yet tissue impedance during ECT may be frequency specific and change adaptively to local electric field intensity.

OBJECTIVES

We systematically consider the application of the quasi-static pipeline to ECT under conditions where 1) static impedance is measured before ECT and 2) during ECT when dynamic impedance is measured. We propose an update to ECT modeling accounting for frequency-dependent impedance.

METHODS

The frequency content on an ECT device output is analyzed. The ECT electrode-body impedance under low-current conditions is measured with an impedance analyzer. A framework for ECT modeling under quasi-static conditions based on a single device-specific frequency (e.g., 1 kHz) is proposed.

RESULTS

Impedance using ECT electrodes under low-current is frequency dependent and subject specific, and can be approximated at >100 Hz with a subject-specific lumped parameter circuit model but at <100 Hz increased non-linearly. The ECT device uses a 2 μA 800 Hz test signal and reports a static impedance that approximate 1 kHz impedance. Combined with prior evidence suggesting that conductivity does not vary significantly across ECT output frequencies at high-currents (800-900 mA), we update the adaptive pipeline for ECT modeling centered at 1 kHz frequency. Based on individual MRI and adaptive skin properties, models match static impedance (at 2 μA) and dynamic impedance (at 900 mA) of four ECT subjects.

CONCLUSIONS

By considering ECT modeling at a single representative frequency, ECT adaptive and non-adaptive modeling can be rationalized under a quasi-static pipeline.

A Systematic Review and Large-Scale tES and TMS Electric Field Modeling Study Reveals How Outcome Measure Selection Alters Results in a Person- and Montage-Specific Manner

Background

Electric field (E-field) modeling is a potent tool to examine the cortical effects of transcranial magnetic and electrical stimulation (TMS and tES, respectively) and to address the high variability in efficacy observed in the literature. However, outcome measures used to report E-field magnitude vary considerably and have not yet been compared in detail.

Objectives

The goal of this two-part study, encompassing a systematic review and modeling experiment, was to provide an overview of the different outcome measures used to report the magnitude of tES and TMS E-fields, and to conduct a direct comparison of these measures across different stimulation montages.

Methods

Three electronic databases were searched for tES and/or TMS studies reporting E-field magnitude. We extracted and discussed outcome measures in studies meeting the inclusion criteria. Additionally, outcome measures were compared via models of four common tES and two TMS modalities in 100 healthy younger adults.

Results

In the systematic review, we included 118 studies using 151 outcome measures related to E-field magnitude. Structural and spherical regions of interest (ROI) analyses and percentile-based whole-brain analyses were used most often. In the modeling analyses, we found that there was an average of only 6% overlap between ROI and percentile-based whole-brain analyses in the investigated volumes within the same person. The overlap between ROI and whole-brain percentiles was montage- and person-specific, with more focal montages such as 4Ã-1 and APPS-tES, and figure-of-eight TMS showing up to 73%, 60%, and 52% overlap between ROI and percentile approaches respectively. However, even in these cases, 27% or more of the analyzed volume still differed between outcome measures in every analyses.

Conclusions

The choice of outcome measures meaningfully alters the interpretation of tES and TMS E-field models. Well-considered outcome measure selection is imperative for accurate interpretation of results, valid between-study comparisons, and depends on stimulation focality and study goals. We formulated four recommendations to increase the quality and rigor of E-field modeling outcome measures. With these data and recommendations, we hope to guide future studies towards informed outcome measure selection, and improve the comparability of studies.

Augmented Transcutaneous Stimulation Using an Injectable Electrode: A Computational Study

Minimally invasive neuromodulation technologies seek to marry the neural selectivity of implantable devices with the low-cost and non-invasive nature of transcutaneous electrical stimulation (TES). The Injectrode® is a needle-delivered electrode that is injected onto neural structures under image guidance. Power is then transcutaneously delivered to the Injectrode using surface electrodes. The Injectrode serves as a low-impedance conduit to guide current to the deep on-target nerve, reducing activation thresholds by an order of magnitude compared to using only surface stimulation electrodes. To minimize off-target recruitment of cutaneous fibers, the energy transfer efficiency from the surface electrodes to the Injectrode must be optimized. TES energy is transferred to the Injectrode through both capacitive and resistive mechanisms. Electrostatic finite element models generally used in TES research consider only the resistive means of energy transfer by defining tissue conductivities. Here, we present an electroquasistatic model, taking into consideration both the conductivity and permittivity of tissue, to understand transcutaneous power delivery to the Injectrode. The model was validated with measurements taken from (n = 4) swine cadavers. We used the validated model to investigate system and anatomic parameters that influence the coupling efficiency of the Injectrode energy delivery system. Our work suggests the relevance of electroquasistatic models to account for capacitive charge transfer mechanisms when studying TES, particularly when high-frequency voltage components are present, such as those used for voltage-controlled pulses and sinusoidal nerve blocks.

Electrical aspects of skin as a pathway to engineering skin devices

Skin is one of the indispensable organs for life. The epidermis at the outermost surface provides a permeability barrier to infectious agents, chemicals, and excessive loss of water, while the dermis and subcutaneous tissue mechanically support the structure of the skin and appendages, including hairs and secretory glands. The integrity of the integumentary system is a key for general health, and many techniques have been developed to measure and control this protective function. In contrast, the effective skin barrier is the major obstacle for transdermal delivery and detection. Changes in the electrical properties of skin, such as impedance and ionic activity, is a practical indicator that reflects the structures and functions of the skin. For example, the impedance that reflects the hydration of the skin is measured for quantitative assessment in skincare, and the current generated across a wound is used for the evaluation and control of wound healing. Furthermore, the electrically charged structure of the skin enables transdermal drug delivery and chemical extraction. This paper provides an overview of the electrical aspects of the skin and summarizes current advances in the development of devices based on these features.

High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)

BACKGROUND

Transcutaneous auricular Vagus Nerve Stimulation (taVNS) applies low-intensity electrical current to the ear with the intention of activating the auricular branch of the Vagus nerve. The sensitivity and selectivity of stimulation applied to the ear depends on current flow pattern produced by a given electrode montage (size and placement).

OBJECTIVE

We compare different electrodes designs for taVNS considering both the predicted peak electric fields (sensitivity) and their spatial distribution (selectivity).

METHODS

Based on optimized high-resolution (0.47 mm) T1 and T2 weighted MRI, we developed an anatomical model of the left ear and the surrounding head tissues including brain, CSF/meninges, skull, muscle, blood vessels, fat, cartilage, and skin. The ear was further segmented into 6 regions of interest (ROI) based on various nerve densities: cavum concha, cymba concha, crus of helix, tragus, antitragus, and earlobe. A range of taVNS electrode montages were reproduced spanning varied electrodes sizes and placements over the tragus, cymba concha, earlobe, cavum concha, and crus of helix. Electric field across the ear (from superficial skin to cartilage) for each montage at 1 mA or 2 mA taVNS, assuming an activation threshold of 6.15 V/m, 12.3 V/m or 24.6 V/m was predicted using a Finite element method (FEM). Finally, considering every ROI, we calculated the sensitivity and selectivity of each montage.

RESULTS

Current flow patterns through the ear were highly specific to the electrode montage. Electric field was maximal at the ear regions directly under the electrodes, and for a given total current, increases with decreasing electrode size. Depending on the applied current and nerves threshold, activation may also occur in the regions between multiple anterior surface electrodes. Each considered montage was selective for one or two regions of interest. For example, electrodes across the tragus restricted significant electric field to the tragus. Stimulation across the earlobe restricted significant electric field to the earlobe and the antitragus. Because of this relative selectivity, use of control ear montages in experimental studies, support testing of targeting. Relative targeting was robust across assumptions of activation threshold and tissue properties.

DISCUSSION

Computational models provide additional insight on how details in electrode shape and placement impact sensitivity (how much current is needed) and selectivity (spatial distribution), thereby supporting analysis of existing approaches and optimization of new devices. Our result suggest taVNS current patterns and relative target are robust across individuals, though (variance in) axon morphology was not represented.

Electrochemical Skin Conductance Alterations during Spinal Cord Stimulation: An Experimental Study

Despite the well-known clinical effects of spinal cord stimulation (SCS), the mechanisms of action have not yet been fully unraveled. The primary aim of this study was to measure whether electrochemical skin conductance, as a measure of peripheral sympathetic autonomic function, is altered by SCS. A second aim was to compare skin conductance levels of patients with failed back surgery syndrome (FBSS) with age- and sex-matched healthy controls. Twenty-three patients with FBSS treated with SCS participated in this study. Sudomotor function was measured with the Sudoscan^TM instrument on the hands and feet during SCS on and off states. Difference scores in skin conductance between patients and age- and sex-matched healthy controls were calculated. Normal sudomotor function at the painful lower limb was revealed for 61% of the patients when SCS was activated. Skin conductance levels were not altered between on and off states of SCS. Differences in scores between patients and healthy controls were significantly different from zero. This study showed that SCS does not influencing the sympathetic nervous system in patients with FBSS, as measured by skin conductance levels. Moreover, it suggested that there is no normalization of the functioning of the sympathetic nervous system, despite the effectiveness of SCS to reduce pain intensity.

Adaptive current-flow models of ECT: Explaining individual static impedance, dynamic impedance, and brain current density

BACKGROUND

Improvements in electroconvulsive therapy (ECT) outcomes have followed refinement in device electrical output and electrode montage. The physical properties of the ECT stimulus, together with those of the patient's head, determine the impedances measured by the device and govern current delivery to the brain and ECT outcomes.

OBJECTIVE

However, the precise relations among physical properties of the stimulus, patient head anatomy, and patient-specific impedance to the passage of current are long-standing questions in ECT research and practice. To this end, we develop a computational framework based on diverse clinical data sets.

METHODS

We developed anatomical MRI-derived models of transcranial electrical stimulation (tES) that included changes in tissue conductivity due to local electrical current flow. These "adaptive" models simulate ECT both during therapeutic stimulation using high current (∼1 A) and when dynamic impedance is measured, as well as prior to stimulation when low current (∼1 mA) is used to measure static impedance. We modeled two scalp layers: a superficial scalp layer with adaptive conductivity that increases with electric field up to a subject-specific maximum (σ_SS¯), and a deep scalp layer with a subject-specific fixed conductivity (σ_DS).

RESULTS

We demonstrated that variation in these scalp parameters may explain clinical data on subject-specific static impedance and dynamic impedance, their imperfect correlation across subjects, their relationships to seizure threshold, and the role of head anatomy. Adaptive tES models demonstrated that current flow changes local tissue conductivity which in turn shapes current delivery to the brain in a manner not accounted for in fixed tissue conductivity models.

CONCLUSIONS

Our predictions that variation in individual skin properties, rather than other aspects of anatomy, largely govern the relationship between static impedance, dynamic impedance, and ECT current delivery to the brain, themselves depend on assumptions about tissue properties. Broadly, our novel modeling pipeline opens the door to explore how adaptive-scalp conductivity may impact transcutaneous electrical stimulation (tES).

The impact of individual electrical fields and anatomical factors on the neurophysiological outcomes of tDCS: A TMS-MEP and MRI study

BACKGROUND

Transcranial direct current stimulation (tDCS), a neuromodulatory non-invasive brain stimulation technique, has shown promising results in basic and clinical studies. The known interindividual variability of the effects, however, limits the efficacy of the technique. Recently we reported neurophysiological effects of tDCS applied over the primary motor cortex at the group level, based on data from twenty-nine participants who received 15min of either sham, 0.5, 1.0, 1.5 or 2.0 mA anodal, or cathodal tDCS. The neurophysiological effects were evaluated via changes in: 1) transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEP), and 2) cerebral blood flow (CBF) measured by functional magnetic resonance imaging (MRI) via arterial spin labeling (ASL). At the group level, dose-dependent effects of the intervention were obtained, which however displayed interindividual variability.

METHOD

In the present study, we investigated the cause of the observed inter-individual variability. To this end, for each participant, a MRI-based realistic head model was designed to 1) calculate anatomical factors and 2) simulate the tDCS- and TMS-induced electrical fields (EFs). We first investigated at the regional level which individual anatomical factors explained the simulated EFs (magnitude and normal component). Then, we explored which specific anatomical and/or EF factors predicted the neurophysiological outcomes of tDCS.

RESULTS

The results highlight a significant negative correlation between regional electrode-to-cortex distance (rECD) as well as regional CSF (rCSF) thickness, and the individual EF characteristics. In addition, while both rCSF thickness and rECD anticorrelated with tDCS-induced physiological changes, EFs positively correlated with the effects.

CONCLUSION

These results provide novel insights into the dependency of the neuromodulatory effects of tDCS on individual physical factors.