Variation in Reported Human Head Tissue Electrical Conductivity Values

Improving computational models of deep brain stimulation through experimental calibration

BACKGROUND

Deep brain stimulation has become a well-established clinical tool to treat movement disorders. Nevertheless, the knowledge of processes initiated by the stimulation remains limited. To address this knowledge gap, computational models are developed to gain deeper insight. However, their predictive power remains constrained by model uncertainties and a lack of validation and calibration.

NEW METHOD

Exemplified with rodent microelectrodes, we present a workflow for validating electrode model geometry using microscopy and impedance spectroscopy in vitro before implantation. We address uncertainties in the tissue distribution and dielectric properties and outline a concept for calibrating the computational model based on in vivo impedance spectroscopy measurements.

RESULTS

The standard deviation of the volume of tissue activated across the 18 characterized electrodes was approximately 32.93%, underscoring the importance of electrode characterization. Thus, the workflow significantly enhances the model predictions' credibility of neural activation exemplified in a rodent model.

COMPARISON WITH EXISTING METHODS

Computational models are frequently employed without validation or calibration, relying instead on manufacturers' specifications. Our approach provides an accessible method to obtain a validated and calibrated electrode geometry, which significantly enhances the reliability of the computational model that relies on this electrode.

CONCLUSION

By reducing the uncertainties of the model, the accuracy in predicting neural activation is increased. The entire workflow is realized in open-source software, making it adaptable for other use cases, such as deep brain stimulation in humans. Additionally, the framework allows for the integration of further experiments, enabling live updates and refinements to computational models.

Electrical impedance tomography meets reduced order modelling: a framework for faster and more reliable electrical conductivity estimations

Objective.Inclusion of individualised electrical conductivities of head tissues is crucial for the accuracy of electrical source imaging techniques based on electro/magnetoencephalography and the efficacy of transcranial electrical stimulation. Parametric electrical impedance tomography (pEIT) is a method to cheaply and non-invasively estimate them using electrode arrays on the scalp to apply currents and measure the resulting potential distribution. Conductivities are then estimated by iteratively fitting a forward model to the measurements, incurring a prohibitive computational cost that is generally lowered at the expense of accuracy. Reducing the computational cost associated with the forward solutions would improve the accessibility of this method and unlock new capabilities.Approach.We introduce reduced order modelling (ROM) to massively speed up the calculations of these solutions for arbitrary conductivity values.Main results.We demonstrate this new ROM-pEIT framework using a realistic head model with six tissue compartments, with minimal errors in both the approximated numerical solutions and conductivity estimations. We show that the computational complexity required to reach a multi-parameter estimation with a negligible relative error is reduced by more than an order of magnitude when using this framework. Furthermore, we illustrate the benefits of this new framework in a number of practical cases, including its application to real pEIT data from three subjects.Significance.Results suggest that this framework can transform the use of pEIT for seeking personalised head conductivities, making it a valuable tool for researchers and clinicians.

Proposal for local SAR safety margin in pediatric neuro-imaging using 7 T MRI and parallel transmission

Objective.Ultra-high field MRI with parallel transmission (pTx) provides a powerful neuroimaging tool with potential application in pediatrics. The use of pTx, however, necessitates a dedicated local specific absorption rate (SAR) management strategy, able to predict and monitor the peak local SAR (pSAR10g). In this work, we address the pSAR10gassessment for an in-house built 7 T 16Tx32Rx pediatric head coil, using the concept of virtual observation points (VOPs) for SAR estimation.Approach. We base our study on full-wave electromagnetic simulations performed on a database of 64 numerical anatomical head models of children aged between 4 and 16 years. We built VOPs on different subsets of this database ofN= 2 up to 30 models, and cross-validated the pSAR10gprediction using non-intersecting subsets, each containing 30 models. We thereby propose a minimum anatomical safety factor (ASF) to apply to the VOP set to enforce safety, despite intersubject variability. Our analysis relies on the computation of the worst case SAR to VOP-SAR ratio, independent of the pTx RF excitation.Main results.The interpolation model provides that the minimum ASF decreases as1+5.37⋅N-0.75withN. Using all 64 models to build VOPs leads to an estimated ASF of 1.24 when considering the VOP validity for an infinite number of subjects.Significance.We propose a general simulation workflow to guide ASF estimation and quantify the trade-off between the number of numerical models available for VOP construction and the safety factor. The approach would apply to any simulation dataset and any pTx setup.

In silico modeling of electric field modulation by transcranial direct current stimulation in stroke patients with skull burr holes: Implications for safe clinical application

BACKGROUND

Transcranial direct current stimulation (tDCS) has emerged as a promising tool for stroke rehabilitation, supported by evidence demonstrating its beneficial effects on post-stroke recovery. However, patients with skull defects, such as burr holes, have been excluded from tDCS due to limited knowledge regarding the effect of skull defects on the electric field.

OBJECTIVE

We investigated the effect of burr holes on the electric field induced by tDCS and identified the electrode location that modulates the electric field.

METHODS

We generated mesh models of the heads of five patients with burr holes and five age-matched control patients who had never undergone brain surgery, based on magnetic resonance imaging. Then we conducted tDCS simulations, with the cathode fixed in one position and the anode in various positions. Regression analysis was employed to investigate the relationship between the electric field at the burr hole and the distance from the burr hole to the anode.

RESULTS

In patients with burr holes, the electric field intensity increased as the anode approached the burr hole, reaching a maximum electric field when the anode covered it, with this pattern remaining consistent across all patient models. Assuming the holes were filled with cerebrospinal fluid, the maximum electric field was 1.20 ± 0.20 V/m (mean ± standard deviation, SD). When the anode was positioned more than 60 mm away from the burr hole, the electric field at the burr hole remained low and constant, with an average value of 0.29 ± 0.04V/m (mean ± SD). In contrast, for all patients without burr holes, the electric field intensity stayed constant regardless of the anode's position, with a maximum amplitude of 0.36 ± 0.04 V/m (mean ± SD). Furthermore, when the burr hole was assumed to be filled with scar tissue, the mean peak electric field was 0.93 ± 0.16 V/m, indicating that the electric field strength varies depending on the conductivity of the tissue filling the burr hole.

CONCLUSION

Based on the simulations, the minimum recommended distance from the burr hole to the anode is 60 mm to prevent unintended stimulation of the brain cortex during tDCS. These findings will contribute to the development of safe and effective tDCS treatments for patients with burr holes.

Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity

Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of individual microcircuits across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.

The impact of EEG electrode density on the mapping of cortical activity networks in infants

OBJECTIVE

Electroencephalography (EEG) is widely used for assessing infant's brain activity, and multi-channel recordings support studies on functional cortical networks. Here, we aimed to assess how the number of recording electrodes affects the quality and level of details accessible in studying infant's cortical networks.

METHODS

Dense array EEG recordings with 124 channels from N=20 infants were used as the reference, and lower electrode numbers were subsampled to simulate recording setups with 63, 31, and 19 electrodes, respectively. Cortical activity networks were computed for each recording setup and different frequencies using amplitude and phase correlation measures. The effects of the recording setup were systematically assessed on global, nodal, and edge levels.

RESULTS

Compared to the reference 124-channel recording setup, lowering electrode density affected network measures in a modality- and frequency-specific manner. The global network features were essentially comparable with 63 or 31 channels. However, the analytic reliability of the local network measures, both at nodal and edge levels, was proportional to the electrode density. The low-frequency amplitude correlations were most robust to the number of recording electrodes, whereas higher frequency phase correlation networks were most sensitive to the density of recording electrodes.

CONCLUSIONS

Our findings suggest strong and predictable effects of recording setup on the network analyses. Higher electrode number supports studies on networks with phase correlations, higher frequency, and finer spatial details.

SIGNIFICANCE

The relationship between the recording setup and reliability of network analyses is essential for the prospective design of research data collection, as well as for guiding analytic strategies when using already collected EEG data from infants.

M/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate. Traditional ESIs, which depend on explicitly defined regularization priors, have struggled to set optimal priors and accurately localize brain sources. To overcome this, we introduced a data-driven, deep learning-based ESI approach without the need for these priors. We proposed a four-layered convolutional neural network (4LCNN) designed to locate both subcortical and cortical sources underlying M/EEG signals. We also employed a sophisticated realistic head conductivity model using the state-of-the-art segmentation method of ten different head tissues from individual MRI data to generate realistic training data. This is the first attempt at deep learning-based ESI targeting subcortical regions. Our method showed excellent accuracy in source localization, particularly in subcortical areas compared to other methods. This was validated through M/EEG simulations, evoked responses, and invasive recordings. The potential for accurate source localization of the 4LCNNs demonstrated in this study suggests future contributions to various research endeavors such as the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases, and basic brain functions.

Effect of in utero exposure to antiepileptic drugs on cortical networks and neurophysiological outcomes at 6 years

OBJECTIVE

The human brain undergoes an activity-dependent organization during late gestation, making it very sensitive to all effects on the spontaneous neuronal activity. Pregnant mothers with epilepsy are treated with antiepileptic drugs (AEDs) that may reach the fetus and cause altered cortical network activity after birth. However, it is not known whether these functional effects of intrauterine AED exposure persist later in childhood.

METHODS

We studied cortical activity networks computed from electroencephalographic recordings during sleep of 25, 6-year-old children with in utero exposure to AEDs and 21 without exposure. The frequency-specific networks were determined for N1 and N2 sleep states, and the study groups were compared for sleep-state-specific changes and dynamic differences between sleep states. Finally, we correlated these difference networks with the children's neurophysiological performance at 6 years.

RESULTS

We found brain-wide changes in the cortical activity networks and their sleep-state dynamics in the children with intrauterine AED exposure. Moreover, the strength of cortical network connectivity was significantly associated with multiple domains of neurocognitive performance, in particular, verbal comprehension, processing speed, and IQ. Our findings together suggest that fetal AED exposure causes very long-lasting changes in the cortical networks with significant links to early school-age cognitive performance.

SIGNIFICANCE

AED treatment of pregnant mothers is indicated for maternal health reasons; however, the long-term neurodevelopmental effects on the offspring are poorly understood. Our present study shows that in utero exposure to AEDs causes persisting changes in the cortical activity networks, which can be measured with electroencephalography at 6 years of age. Moreover, these network changes correlate to the child's neurocognitive performance at the same age. These findings together suggest a pathway for how fetal drug exposures may cause persisting and neurocognitively meaningful changes in cortical connectivity patterns.

Individuals with psychosis receive less electric field strength during transcranial direct current stimulation compared to healthy controls

Recent research has examined the effectiveness of transcranial direct current stimulation (tDCS) as an adjunctive treatment for antipsychotics, finding mixed results on cognitive, positive, and negative symptoms. We tested if individuals with psychosis have reduced electric field strength compared to healthy controls and assessed the potential causal factors. We hypothesized that either cortical thinning due to the disorder or increased scalp thickness due to secondary effects of the disorder were causal factors. Using the Psychosis Human Connectome Project dataset, we simulated electric field models for 136 individuals with psychosis, 73 first-degree relatives, and 43 healthy controls. We compared group differences of electric field strength at bilateral dorsolateral prefrontal cortex (dlPFC), targeted with two montages (Fp1-Fp2 & F3-Fp2) commonly used to treat cognitive impairment. We additionally compared groups on scalp, skull, and cerebrospinal fluid thickness at bilateral dlPFC and the three electrode locations. Mediation analyses assessed if tissue thickness and BMI were causal factors for group differences while controlling for age and sex. Individuals with psychosis had lower electric field strength for bilateral dlPFC for both montages. Scalp thickness was also greater for individuals with psychosis, but cerebrospinal fluid thickness was not significantly different. BMI was a significant mediator for the group difference seen in both scalp thickness and electric field strength. Future treatment studies using tDCS in the psychosis population should include electric field modeling to assess its effectiveness given the increased risk of obesity. Individualized montages based on head models may also improve effectiveness.

Reconstruction of intra- and extra-neurite conductivity tensors via conductivity at Larmor frequency and DWI data patterns

The developed magnetic resonance electrical properties tomography (MREPT) techniques visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data. In biological tissues, electrical conductivity is influenced by ion concentrations and mobility. To visualize the anisotropic conductivity tensor of biological tissues, we use the Einstein-Smoluchowski equation, which links the diffusion coefficient to particle mobility. By assuming a correlation between ion mobility and water diffusivity, we aim to decompose the internal isotropic conductivity at Larmor frequency into anisotropic conductivity tensors within the intra- and extra-neurite compartments. The multi-compartment spherical mean technique (MC-SMT), utilizing both high and low b-value diffusion-weighted imaging (DWI) data, characterizes the diffusion of water molecules within and across the intra- and extra-neurite compartments by analyzing the microstructural intricacies and the foundational architectural arrangement of the brain's tissues. By analyzing the relationships between the measured DWI data, the microscopic diffusion signal, and the fiber orientation distribution function, we predict the DWI data for the intra- and extra-neurite compartments using spherical harmonic decomposition. Using the predicted DWI data for the intra- and extra-neurite compartments, we develop a conductivity tensor imaging method that operates specifically within the separated compartments. Human brain experiments, involving four healthy volunteers and a brain tumor patient, were performed to assess and confirm the reliability of the proposed method.

The role of white matter variability in TMS neuromodulatory effects

BACKGROUND

Transcranial Magnetic Stimulation (TMS) is a widely used tool to explore the causal role of focal brain regions in cognitive processing. TMS effects over attentional processes are consistent and replicable, while at the same time subjected to individual variability. This individual variability needs to be understood to better comprehend TMS effects, and most importantly, its clinical applications.

OBJECTIVE

This study aimed to explore the role of white matter variability in TMS neuromodulatory effects on behavior in healthy participants (N = 50).

METHODS

Participants completed an attentional task in which orienting and alerting cues preceded near-threshold targets. Continuous Theta Burst Stimulation (cTBS) was applied over the left frontal eye field (FEF) or an active vertex condition. White matter was explored with diffusion-weighted imaging tractography and Tract-Based Spatial Statistics (TBSS).

RESULTS

Behaviorally, TMS over the left FEF slowed down reaction times (especially in the alerting task), impaired accuracy in the objective task, and reduced the proportion of seen targets (as compared to the vertex condition). Attentional effects increased, overall, when TMS was applied to the left FEF as compared to the vertex condition. Correlations between white matter and TMS effects showed i) reduced TMS effects associated with the microstructural properties of long-range white matter pathways such as the superior longitudinal fasciculus (SLF), and interhemispheric fibers of the corpus callosum (CC), and ii) increased TMS effects in participants with high integrity of the CC connecting the stimulated region with the opposite hemisphere. Additionally, variability in attentional effects was also related to white matter, showing iii) increased alerting effects in participants with low integrity of association, commissural, and projection fibers, and iv) increased orienting effects in participants with high integrity of the right SLF III.

CONCLUSION

All these observations highlight the importance of taking into account individual variability in white matter for the understanding of cognitive processing and brain neuromodulation effects.

Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications

Objective.Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.Approach.We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.Main results.In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuchet al,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.Significance.This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locationsin vivo. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.

Investigating the effect of template head models on Event-Related Potential source localization: a simulation and real-data study

Introduction

Event-Related Potentials (ERPs) are valuable for studying brain activity with millisecond-level temporal resolution. While the temporal resolution of this technique is excellent, the spatial resolution is limited. Source localization aims to identify the brain regions generating the EEG data, thus increasing the spatial resolution, but its accuracy depends heavily on the head model used. This study compares the performance of subject-specific and template-based head models in both simulated and real-world ERP localization tasks.

Methods

Simulated data mimicking realistic ERPs was created to evaluate the impact of head model choice systematically, after which subject-specific and template-based head models were used for the reconstruction of the data. The different modeling approaches were also applied to a face recognition dataset.

Results

The results indicate that the template models capture the simulated activity less accurately, producing more spurious sources and identifying less true sources correctly. Furthermore, the results show that while creating more accurate and detailed head models is beneficial for the localization accuracy when using subject-specific head models, this is less the case for template head models. The main N170 source of the face recognition dataset was correctly localized to the fusiform gyrus, a known face processing area, using the subject-specific models. Apart from the fusiform gyrus, the template models also reconstructed several other sources, illustrating the localization inaccuracies.

Discussion

While template models allow researchers to investigate the neural generators of ERP components when no subject-specific MRIs are available, it could lead to misinterpretations. Therefore, it is important to consider a priori knowledge and hypotheses when interpreting results obtained with template head models, acknowledging potential localization errors.

Unpacking Galvanic Vestibular Stimulation using simulations and relating current flow to reported motions: Comparison across common and specialized electrode placements

BACKGROUND

Galvanic Vestibular Stimulation (GVS) is a non-invasive electrical stimulation technique that is typically used to probe the vestibular system. When using direct current or very low frequency sine, GVS causes postural sway or perception of illusory (virtual) motions. GVS is commonly delivered using two electrodes placed at the mastoids, however, placements involving additional electrodes / locations have been employed. Our objective was to systematically evaluate all known GVS electrode placements, compare induced current flow, and how it relates to the archetypal sway and virtual motions. The ultimate goal is to help users in having a better understanding of the effects of different placements.

METHODS

We simulated seven GVS electrode placements with same total injected current using an ultra-high resolution model. Induced electric field (EF) patterns at the cortical and the level of vestibular organs (left and right) were determined. A range of current flow metrics including potential factors such as inter-electrode separation, percentage of current entering the cranial cavity, and symmetricity were calculated. Finally, we relate current flow to reported GVS motions.

RESULTS

As expected, current flow patterns are electrode placement specific. Placements with two electrodes generally result in higher EF magnitude. Placements with four electrodes result in lower percentage of current entering the cranial cavity. Symmetric placements do not result in similar EF values in the left and the right organs respectively- highlighting inherent anatomical asymmetry of the human head. Asymmetric placements were found to induce as much as ~3-fold higher EF in one organ over the other. The percentage of current entering the cranial cavity varies between ~15% and ~40% depending on the placement.

CONCLUSIONS

We expect our study to advance understanding of GVS and provide insight on probable mechanism of action of a certain electrode placement choice. The dataset generated across several metrics will support hypothesis testing relating empirical outcomes to current flow patterns. Further, the differences in current flow will guide stimulation strategy (what placement and how much scalp current to use) and facilitate a quantitatively informed rational / optimal decision.

Quasistatic approximation in neuromodulation

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI

This work was inspired by the observation that a majority of MR-electrical properties tomography studies are based on direct comparisons with ex vivo measurements carried out on post-mortem samples in the 90's. As a result, the in vivo conductivity values obtained from MRI in the megahertz range in different types of tissues (brain, liver, tumors, muscles, etc.) found in the literature may not correspond to their ex vivo equivalent, which still serves as a reference for electromagnetic modelling. This study aims to pave the way for improving current databases since the definition of personalized electromagnetic models (e.g. for Specific Absorption Rate estimation) would benefit from better estimation. Seventeen healthy volunteers underwent MRI of both brain and thorax/abdomen using a three-dimensional ultrashort echo-time (UTE) sequence. We estimated conductivity (S/m) in several classes of macroscopic tissue using a customized reconstruction method from complex UTE images, and give general statistics for each of these regions (mean-median-standard deviation). These values are used to find possible correlations with biological parameters such as age, sex, body mass index and/or fat volume fraction, using linear regression analysis. In short, the collected in vivo values show significant deviations from the ex vivo values in conventional databases, and we show significant relationships with the latter parameters in certain organs for the first time, e.g. a decrease in brain conductivity with age.

Addressing the sources of inter-subject variability in E-field parameters in anodal tDCS stimulation over motor cortical network

Objetive: .Although transcranial direct current stimulation constitutes a non-invasive neuromodulation technique with promising results in a great variety of applications, its clinical implementation is compromised by the high inter-subject variability reported. This study aims to analyze the inter-subject variability in electric fields (E-fields) over regions of the cortical motor network under two electrode montages: the classical C3Fp2 and an alternative P3F3, which confines more the E-field over this region.Approach.Computational models of the head of 98 healthy subjects were developed to simulate the E-field under both montages. E-field parameters such as magnitude, focality and orientation were calculated over three regions of interest (ROI): M1S1, supplementary motor area (SMA) and preSMA. The role of anatomical characteristics as a source of inter-subject variability on E-field parameters and individualized stimulation intensity were addressed using linear mixed-effect models.Main results.P3F3 showed a more confined E-field distribution over M1S1 than C3Fp2; the latter elicited higher E-fields over supplementary motor areas. Both montages showed high inter-subject variability, especially for the normal component over C3Fp2. Skin, bone and CSF ROI volumes showed a negative association with E-field magnitude irrespective of montage. Grey matter volume and montage were the main sources of variability for focality. The curvature of gyri was found to be significantly associated with the variability of normal E-fields.Significance.Computational modeling proves useful in the assessment of E-field variability. Our simulations predict significant differences in E-field magnitude and focality for C3Fp2 and P3F3. However, anatomical characteristics were also found to be significant sources of E-field variability irrespective of electrode montage. The normal E-field component better captured the individual variability and low rate of responder subjects observed in experimental studies.

Bioelectronic Direct Current Stimulation at the Transition Between Reversible and Irreversible Charge Transfer

Many biological processes rely on endogenous electric fields (EFs), including tissue regeneration, cell development, wound healing, and cancer metastasis. Mimicking these biological EFs by applying external direct current stimulation (DCS) is therefore the key to many new therapeutic strategies. During DCS, the charge transfer from electrode to tissue relies on a combination of reversible and irreversible electrochemical processes, which may generate toxic or bio-altering substances, including metal ions and reactive oxygen species (ROS). Poly(3,4-ethylenedioxythiophene) (PEDOT) based electrodes are emerging as suitable candidates for DCS to improve biocompatibility compared to metals. This work addresses whether PEDOT electrodes can be tailored to favor reversible biocompatible charge transfer. To this end, different PEDOT formulations and their respective back electrodes are studied using cyclic voltammetry, chronopotentiometry, and direct measurements of H2O2 and O2. This combination of electrochemical methods sheds light on the time dynamics of reversible and irreversible charge transfer and the relationship between capacitance and ROS generation. The results presented here show that although all electrode materials investigated generate ROS, the onset of ROS can be delayed by increasing the electrode's capacitance via PEDOT coating, which has implications for future bioelectronic devices that allow longer reversibly driven pulse durations during DCS.

A boundary element method of bidomain modeling for predicting cellular responses to electromagnetic fields

Objective.Commonly used cable equation approaches for simulating the effects of electromagnetic fields on excitable cells make several simplifying assumptions that could limit their predictive power. Bidomain or 'whole' finite element methods have been developed to fully couple cells and electric fields for more realistic neuron modeling. Here, we introduce a novel bidomain integral equation designed for determining the full electromagnetic coupling between stimulation devices and the intracellular, membrane, and extracellular regions of neurons.Approach.Our proposed boundary element formulation offers a solution to an integral equation that connects the device, tissue inhomogeneity, and cell membrane-induced E-fields. We solve this integral equation using first-order nodal elements and an unconditionally stable Crank-Nicholson time-stepping scheme. To validate and demonstrate our approach, we simulated cylindrical Hodgkin-Huxley axons and spherical cells in multiple brain stimulation scenarios.Main Results.Comparison studies show that a boundary element approach produces accurate results for both electric and magnetic stimulation. Unlike bidomain finite element methods, the bidomain boundary element method does not require volume meshes containing features at multiple scales. As a result, modeling cells, or tightly packed populations of cells, with microscale features embedded in a macroscale head model, is simplified, and the relative placement of devices and cells can be varied without the need to generate a new mesh.Significance.Device-induced electromagnetic fields are commonly used to modulate brain activity for research and therapeutic applications. Bidomain solvers allow for the full incorporation of realistic cell geometries, device E-fields, and neuron populations. Thus, multi-cell studies of advanced neuronal mechanisms would greatly benefit from the development of fast-bidomain solvers to ensure scalability and the practical execution of neural network simulations with realistic neuron morphologies.

Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

BACKGROUND

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

NEW METHOD

Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

RESULTS

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures.

COMPARISON WITH EXISTING METHOD

Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences.

CONCLUSIONS

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Causal functional maps of brain rhythms in working memory

Human working memory is a key cognitive process that engages multiple functional anatomical nodes across the brain. Despite a plethora of correlative neuroimaging evidence regarding the working memory architecture, our understanding of critical hubs causally controlling overall performance is incomplete. Causal interpretation requires cognitive testing following safe, temporal, and controllable neuromodulation of specific functional anatomical nodes. Such experiments became available in healthy humans with the advance of transcranial alternating current stimulation (tACS). Here, we synthesize findings of 28 placebo-controlled studies (in total, 1,057 participants) that applied frequency-specific noninvasive stimulation of neural oscillations and examined working memory performance in neurotypical adults. We use a computational meta-modeling method to simulate each intervention in realistic virtual brains and test reported behavioral outcomes against the stimulation-induced electric fields in different brain nodes. Our results show that stimulating anterior frontal and medial temporal theta oscillations and occipitoparietal gamma rhythms leads to significant dose-dependent improvement in working memory task performance. Conversely, prefrontal gamma modulation is detrimental to performance. Moreover, we found distinct spatial expression of theta subbands, where working memory changes followed orbitofrontal high-theta modulation and medial temporal low-theta modulation. Finally, all these results are driven by changes in working memory accuracy rather than processing time measures. These findings provide a fresh view of the working memory mechanisms, complementary to neuroimaging research, and propose hypothesis-driven targets for the clinical treatment of working memory deficits.

Biophysical Principles and Computational Modeling of Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community.

OBJECTIVE

In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS.

RESULTS

Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response.

CONCLUSION

This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

A Miniaturized, Low-Frequency Magnetoelectric Wireless Power Transfer System for Powering Biomedical Implants

This work experimentally demonstrates the operation of a miniaturized magnetoelectric (ME) wireless power transfer (WPT) system by incorporating a ME transducer and a suitable interface power management circuit (PMC) for potentially powering implantable medical devices (IMD) wirelessly. A ME heterostructure is micromachined to obtain desired device dimensions of 3.5 × 5 mm 2 and to restrict the operating frequency at a clinically approved frequency of 50 kHz. The proposed work also aims to address the trade-off between the device miniaturization, power attenuation and limiting the specific absorption rate (SAR) in the human tissue. By limiting the operating frequency to 50 kHz, the SAR is reduced to less than 1 μW/kg. The fabricated device is characterized with low-intensity AC magnetic field up to 40 μT without using any DC bias, resulting in 0.4 V output voltage and 6.6 μW power across 8 k Ω load. Alignment misorientation between the Tx and Rx is studied for in-plane and out-of-plane angular rotations to confirm the device's reliability against angular misalignment. By eliminating the bulky biasing magnets, the proposed device achieves a significant size reduction compared to the previously reported works. In addition, a self-powered interface PMC is incorporated with the ME system. The PMC generates 3.5 V regulated DC voltage from the input AC voltage range 0.7 V to 3.3 V. The PMC is fabricated on a 2-layered PCB and the over all ME WPT system consumes 12 × 12 mm 2 area. The overall PMC has intrinsic current consumption less than 550 nA with peak power conversion efficiency higher than 85 %. The in vitro cytotoxicity analysis in the human hepatic cell line WRL-68 confirmed the biocompatibility of the Parylene-C encapsulated ME device for up to 7 days, suggesting its potential use in implantable electronic devices for biomedical and clinical applications.

Global sensitivity of EEG source analysis to tissue conductivity uncertainties

Introduction

To reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface.

Methods

We employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions.

Results

For sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity.

Discussion

Our results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.

Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning

Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly in non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community at https://github.com/lab-smile/GRACE.

In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression

Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of α5 subunit-containing GABAA receptor (α5-PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of α5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of α5-PAM on tonic inhibition as measured in human neurons, and tested in silico α5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that α5-PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an α5-PAM dose-response and identified simulated EEG biomarker candidates. Our results serve to de-risk and facilitate α5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors

EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.

How to assess the accuracy of volume conduction models? A validation study with stereotactic EEG data

Introduction

Volume conduction models of the human head are used in various neuroscience fields, such as for source reconstruction in EEG and MEG, and for modeling the effects of brain stimulation. Numerous studies have quantified the accuracy and sensitivity of volume conduction models by analyzing the effects of the geometrical and electrical features of the head model, the sensor model, the source model, and the numerical method. Most studies are based on simulations as it is hard to obtain sufficiently detailed measurements to compare to models. The recording of stereotactic EEG during electric stimulation mapping provides an opportunity for such empirical validation.

Methods

In the study presented here, we used the potential distribution of volume-conducted artifacts that are due to cortical stimulation to evaluate the accuracy of finite element method (FEM) volume conduction models. We adopted a widely used strategy for numerical comparison, i.e., we fixed the geometrical description of the head model and the mathematical method to perform simulations, and we gradually altered the head models, by increasing the level of detail of the conductivity profile. We compared the simulated potentials at different levels of refinement with the measured potentials in three epilepsy patients.

Results

Our results show that increasing the level of detail of the volume conduction head model only marginally improves the accuracy of the simulated potentials when compared to in-vivo sEEG measurements. The mismatch between measured and simulated potentials is, throughout all patients and models, maximally 40 microvolts (i.e., 10% relative error) in 80% of the stimulation-recording combination pairs and it is modulated by the distance between recording and stimulating electrodes.

Discussion

Our study suggests that commonly used strategies used to validate volume conduction models based solely on simulations might give an overly optimistic idea about volume conduction model accuracy. We recommend more empirical validations to be performed to identify those factors in volume conduction models that have the highest impact on the accuracy of simulated potentials. We share the dataset to allow researchers to further investigate the mismatch between measurements and FEM models and to contribute to improving volume conduction models.

Electric field simulation and appropriate electrode positioning for optimized transcranial direct current stimulation of stroke patients: an in Silico model

Transcranial Direct Current Stimulation (tDCS) has benefits for motor rehabilitation in stroke patients, but its clinical application is limited due to inter-individual heterogeneous effects. Recently, optimized tDCS that considers individual brain structure has been proposed, but the utility thereof has not been studied in detail. We explored whether optimized tDCS provides unique electrode positions for each patient and creates a higher target electric field than the conventional approach. A comparative within-subject simulation study was conducted using data collected for a randomized controlled study evaluating the effect of optimized tDCS on upper extremity function in stroke patients. Using Neurophet tES LAB 3.0 software, individual brain models were created based on magnetic resonance images and tDCS simulations were performed for each of the conventional and optimized configurations. A comparison of electrode positions between conventional tDCS and optimized tDCS was quantified by calculation of Euclidean distances. A total of 21 stroke patients were studied. Optimized tDCS produced a higher electric field in the hand motor region than conventional tDCS, with an average improvement of 20% and a maximum of 52%. The electrode montage for optimized tDCS was unique to each patient and exhibited various configurations that differed from electrode placement of conventional tDCS. Optimized tDCS afforded a higher electric field in the target of a stroke patient compared to conventional tDCS, which was made possible by appropriately positioning the electrodes. Our findings may encourage further trials on optimized tDCS for motor rehabilitation after stroke.

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.

Simulation-based feasibility study of monitoring of intracerebral hemorrhages and detection of secondary hemorrhages using electrical impedance tomography

Purpose

We present a simulation-based feasibility study of electrical impedance tomography (EIT) for continuous bedside monitoring of intracerebral hemorrhages (ICH) and detection of secondary hemorrhages.

Approach

We simulated EIT measurements for six different hemorrhage sizes at two different hemorrhage locations using an anatomically detailed computational head model. Using this dataset, we test the ICH monitoring and detection performance of our tailor-made, patient-specific stroke-monitoring algorithm that utilizes a novel combination of nonlinear region-of-interest difference imaging, parallel level sets regularization and a prior-conditioned least squares algorithm. We compare the results of our algorithm to the results of two reference algorithms, a total variation regularized absolute imaging algorithm and a linear difference imaging algorithm.

Results

The tailor-made stroke-monitoring algorithm is capable of indicating smaller changes in the simulated hemorrhages than either of the reference algorithms, indicating better monitoring and detection performance.

Conclusions

Our simulation results from the anatomically detailed head model indicate that EIT equipped with a patient-specific stroke-monitoring algorithm is a promising technology for the unmet clinical need of having a technology for continuous bedside monitoring of brain status of acute stroke patients.

Effect of optimized transcranial direct current stimulation on motor cortex activation in patients with sub-acute or chronic stroke: a study protocol for a single-blinded cross-over randomized control trial

Introduction

Transcranial direct current stimulation (tDCS) has shown positive but inconsistent results in stroke rehabilitation. This could be attributed to inter-individual variations in brain characteristics and stroke lesions, which limit the use of a single tDCS protocol for all post-stroke patients. Optimizing the electrode location in tDCS for each individual using magnetic resonance imaging (MRI) to generate three-dimensional computer models and calculate the electric field (E-field) induced by tDCS at a specific target point in the primary motor cortex may help reduce these inconsistencies. In stroke rehabilitation, locating the optimal position that generates a high E-field in a target area can influence motor recovery. Therefore, this study was designed to determine the effect of personalized tDCS electrode positions on hand-knob activation in post-stroke patients.

Method

This is a crossover study with a sample size of 50 participants, who will be randomly assigned to one of six groups and will receive one session of either optimized-active, conventional-active, or sham tDCS, with 24 h between sessions. The tDCS parameters will be 1 mA (5 × 5 cm electrodes) for 20 min. The motor-evoked potential (MEP) will be recorded before and after each session over the target area (motor cortex hand-knob) and the MEP hotspot. The MEP amplitude at the target location will be the primary outcome.

Discussion

We hypothesize that the optimized-active tDCS session would show a greater increase in MEP amplitude over the target area in patients with subacute and chronic stroke than conventional and sham tDCS sessions.Clinical trial registration: https://cris.nih.go.kr, identifier KCT0007536.

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation

Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.

The impact of brain lesions on tDCS-induced electric fields

Transcranial direct current stimulation (tDCS) can enhance motor and language rehabilitation after stroke. Though brain lesions distort tDCS-induced electric field (E-field), systematic accounts remain limited. Using electric field modelling, we investigated the effect of 630 synthetic lesions on E-field magnitude in the region of interest (ROI). Models were conducted for two tDCS montages targeting either primary motor cortex (M1) or Broca's area (BA44). Absolute E-field magnitude in the ROI differed by up to 42% compared to the non-lesioned brain depending on lesion size, lesion-ROI distance, and lesion conductivity value. Lesion location determined the sign of this difference: lesions in-line with the predominant direction of current increased E-field magnitude in the ROI, whereas lesions located in the opposite direction decreased E-field magnitude. We further explored how individualised tDCS can control lesion-induced effects on E-field. Lesions affected the individualised electrode configuration needed to maximise E-field magnitude in the ROI, but this effect was negligible when prioritising the maximisation of radial inward current. Lesions distorting tDCS-induced E-field, is likely to exacerbate inter-individual variability in E-field magnitude. Individualising electrode configuration and stimulator output can minimise lesion-induced variability but requires improved estimates of lesion conductivity. Individualised tDCS is critical to overcome E-field variability in lesioned brains.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

Background

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

New Method

Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

Results

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures.

Comparison with existing methods

Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences.

Conclusions

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Non-invasive temporal interference electrical stimulation of the human hippocampus

Deep brain stimulation (DBS) via implanted electrodes is used worldwide to treat patients with severe neurological and psychiatric disorders. However, its invasiveness precludes widespread clinical use and deployment in research. Temporal interference (TI) is a strategy for non-invasive steerable DBS using multiple kHz-range electric fields with a difference frequency within the range of neural activity. Here we report the validation of the non-invasive DBS concept in humans. We used electric field modeling and measurements in a human cadaver to verify that the locus of the transcranial TI stimulation can be steerably focused in the hippocampus with minimal exposure to the overlying cortex. We then used functional magnetic resonance imaging and behavioral experiments to show that TI stimulation can focally modulate hippocampal activity and enhance the accuracy of episodic memories in healthy humans. Our results demonstrate targeted, non-invasive electrical stimulation of deep structures in the human brain.

Concept of a fully-implantable system to monitor tumor recurrence

Current treatment for glioblastoma includes tumor resection followed by radiation, chemotherapy, and periodic post-operative examinations. Despite combination therapies, patients face a poor prognosis and eventual recurrence, which often occurs at the resection site. With standard MRI imaging surveillance, histologic changes may be overlooked or misinterpreted, leading to erroneous conclusions about the course of adjuvant therapy and subsequent interventions. To address these challenges, we propose an implantable system for accurate continuous recurrence monitoring that employs optical sensing of fluorescently labeled cancer cells and is implanted in the resection cavity during the final stage of tumor resection. We demonstrate the feasibility of the sensing principle using miniaturized system components, optical tissue phantoms, and porcine brain tissue in a series of experimental trials. Subsequently, the system electronics are extended to include circuitry for wireless energy transfer and power management and verified through electromagnetic field, circuit simulations and test of an evaluation board. Finally, a holistic conceptual system design is presented and visualized. This novel approach to monitor glioblastoma patients is intended to early detect recurrent cancerous tissue and enable personalization and optimization of therapy thus potentially improving overall prognosis.

Functional Enhancement and Characterization of an Electrophysiological Mapping Electrode Probe with Carbonic, Directional Macrocontacts

Electrophysiological mapping (EM) using acute electrode probes is a common procedure performed during functional neurosurgery. Due to their constructive specificities, the EM probes are lagging in innovative enhancements. This work addressed complementing a clinically employed EM probe with carbonic and circumferentially segmented macrocontacts that are operable both for neurophysiological sensing ("recording") of local field potentials (LFP) and for test stimulation. This paper illustrates in-depth the development that is based on the direct writing of functional materials. The unconventional fabrication processes were optimized on planar geometry and then transferred to the cylindrically thin probe body. We report and discuss the constructive concept and architecture of the probe, characteristics of the electrochemical interface deduced from voltammetry and chronopotentiometry, and the results of in vitro and in vivo recording and pulse stimulation tests. Two- and three-directional macrocontacts were added on probes having shanks of 550 and 770 μm diameters and 10-23 cm lengths. The graphitic material presents a ~2.7 V wide, almost symmetric water electrolysis window, and an ultra-capacitive charge transfer. When tested with clinically relevant 150 μs biphasic current pulses, the interfacial polarization stayed safely away from the water window for pulse amplitudes up to 9 mA (135 μC/cm2). The in vivo experiments on adult rat models confirmed the high-quality sensing of LFPs. Additionally, the in vivo-prevailing increase in the electrode impedance and overpotential are discussed and modeled by an ionic mobility-reducing spongiform structure; this restricted diffusion model gives new applicative insight into the in vivo-uprisen stimulation overpotential.

CutFEM forward modeling for EEG source analysis

Introduction

Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can be solved using the finite element method (FEM). FEM offers flexibility when modeling anisotropic tissue conductivities but requires a volumetric discretization, a mesh, of the head domain. Structured hexahedral meshes are easy to create in an automatic fashion, while tetrahedral meshes are better suited to model curved geometries. Tetrahedral meshes, thus, offer better accuracy but are more difficult to create.

Methods

We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory-evoked potentials.

Results

CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption, and computational speed while being able to mesh arbitrarily touching compartments.

Discussion

CutFEM balances numerical accuracy, computational efficiency, and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.

Transcranial application of magnetic pulses for improving brain drug delivery efficiency via intranasal injection of magnetic nanoparticles

As the blood-brain barrier (BBB) hinders efficient drug delivery to the brain, drug delivery via the intranasal pathway, bypassing the BBB, has received considerable attention. However, intranasal administration still has anatomical and physiological limitations, necessitating further solutions to enhance effectiveness. In this study, we used transcranial magnetic stimulation (TMS) on fluorescent magnetic nanoparticles (MNPs) of different sizes (50, 100, and 300 nm) to facilitate MNP's transportation and delivery to the brain parenchyma. To validate this concept, anesthetized rats were intranasally injected with the MNPs, and TMS was applied to the center of the head. As the result, a two-fold increase in brain MNP delivery was achieved using TMS compared with passive intranasal administration. In addition, histological analysis that was performed to investigate the safety revealed no gross or microscopic damages to major organs caused by the nanoparticles. While future studies should establish the delivery conditions in humans, we expect an easy clinical translation in terms of device safety, similar to the use of conventional TMS. The strategy reported herein is the first critical step towards effective drug transportation to the brain.

P53 status, and G2/M cell cycle arrest, are determining factors in cell-death induction mediated by ELF-EMF in glioblastoma

The average survival of patients with glioblastoma is 12-15 months. Therefore, finding a new treatment method is important, especially in cases that show resistance to treatment. Extremely low-frequency electromagnetic fields (ELF-EMF) have characteristics and capabilities that can be proposed as a new cancer treatment method with low side effects. This research examines the antitumor effect of ELF-EMF on U87 and U251 glioblastoma cell lines. Flowcytometry determined the viability/apoptosis and distribution of cells in different phases of the cell cycle. The size of cells was assessed by TEM. Important cell cycle regulation genes mRNA expression levels were investigated by real-time PCR. ELF-EMF induced apoptosis in U87cells much more than U251 (15% against 2.43%) and increased G2/M cell population in U87 (2.56%, p value < 0.05), and S phase in U251 (2.4%) (data are normalized to their sham exposure). The size of U87 cells increased significantly after ELF-EMF exposure (overexpressing P53 in U251 cells increased the apoptosis induction by ELF-EMF). The expression level of P53, P21, and MDM2 increased and CCNB1 decreased in U87. Among the studied genes, MCM6 expression decreased in U251. Increasing expression of P53, P21 and decreasing CCNB1, induction of cell G2/M cycle arrest, and consequently increase in the cell size can be suggested as one of the main mechanisms of apoptosis induction by ELF-EMF; furthermore, our results demonstrate the possible footprint of P53 in the apoptosis induction by ELF-EMF, as U87 carry the wild type of P53 and U251 has the mutated form of this gene.

The effect of deep brain structure modeling on transcranial direct current stimulation-induced electric fields: An in-silico study

To study transcranial direct current stimulation (tDCS) and its effect on the brain, it could be useful to predict the distribution of the electric field induced in the brain with given tDCS parameters. As a solution, simulation with realistic computational models using magnetic resonance images (MRIs) have been widely used in the fields. With the recent advance of deep learning-based segmentation techniques of the brain, questions have been raised about if tDCS-induced electric field is affected by the deep brain structures. This study aimed to investigate the effect of the deep brain structure modeling on the induced electric field. To this end, we generated models with and without the deep brain structures by using an open MRI dataset comprising tDCS parameters, electric field simulation results and in-vivo intracranial recordings in the deep brain structures. We investigated the difference between the simulation results of the two models with a statistical analysis. Our results indicated that tDCS-induced electric fields and current flow in the brain are significantly different when the deep brain structures are considered.

[The influence of tissue conductivity on the calculation of electric field in the transcranial magnetic stimulation head model]

In transcranial magnetic stimulation (TMS), the conductivity of brain tissue is obtained by using diffusion tensor imaging (DTI) data processing. However, the specific impact of different processing methods on the induced electric field in the tissue has not been thoroughly studied. In this paper, we first used magnetic resonance image (MRI) data to create a three-dimensional head model, and then estimated the conductivity of gray matter (GM) and white matter (WM) using four conductivity models, namely scalar (SC), direct mapping (DM), volume normalization (VN) and average conductivity (MC), respectively. Isotropic empirical conductivity values were used for the conductivity of other tissues such as the scalp, skull, and cerebrospinal fluid (CSF), and then the TMS simulations were performed when the coil was parallel and perpendicular to the gyrus of the target. When the coil was perpendicular to the gyrus where the target was located, it was easy to get the maximum electric field in the head model. The maximum electric field in the DM model was 45.66% higher than that in the SC model. The results showed that the conductivity component along the electric field direction of which conductivity model was smaller in TMS, the induced electric field in the corresponding domain corresponding to the conductivity model was larger. This study has guiding significance for TMS precise stimulation.

Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults

Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by  ∼  6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.

Slope of the power spectral density flattens at low frequencies (<150 Hz) with healthy aging but also steepens at higher frequency (>200 Hz) in human electroencephalogram

The power spectral density (PSD) of the brain signals is characterized by two distinct features: oscillations, which are represented as distinct "bumps," and broadband aperiodic activity, that reduces in power with increasing frequency and is characterized by the slope of the power falloff. Recent studies have shown a change in the slope of the aperiodic activity with healthy aging and mental disorders. However, these studies analyzed slopes over a limited frequency range (<100 Hz). To test whether the PSD slope is affected over a wider frequency range with aging and mental disorder, we analyzed the slope till 800 Hz in electroencephalogram data recorded from elderly subjects (>49 years) who were healthy (n = 217) or had mild cognitive impairment (MCI; n = 11) or Alzheimer's Disease (AD; n = 5). Although the slope reduced up to ~ 150 Hz with healthy aging (as shown previously), surprisingly, at higher frequencies (>200 Hz), it increased with age. These results were observed in all electrodes, for both eyes open and eyes closed conditions, and for different reference schemes. However, slopes were not significantly different in MCI/AD subjects compared with healthy controls. Overall, our results constrain the biophysical mechanisms that are reflected in the PSD slopes in healthy and pathological aging.

Optimizing Individual Targeting of Fronto-Amygdala Network with Transcranial Magnetic Stimulation (TMS): Biophysical, Physiological and Behavioral Variations in People with Methamphetamine Use Disorder

Background

Previous studies in people with substance use disorders (SUDs) have implicated both the frontopolar cortex and amygdala in drug cue reactivity and craving, and amygdala-frontopolar coupling is considered a marker of early relapse risk. Accumulating data highlight that the frontopolar cortex can be considered a promising therapeutic target for transcranial magnetic stimulation (TMS) in SUDs. However, one-size-fits-all approaches to TMS targets resulted in substantial variation in both physiological and behavioral outcomes. Individualized TMS approaches to target cortico-subcortical circuits like amygdala-frontopolar have not yet been investigated in SUDs.

Objective

Here, we (1) defined individualized TMS target location based on functional connectivity of the amygdala-frontopolar circuit while people were exposed to drug-related cues, (2) optimized coil orientation based on maximizing electric field (EF) perpendicular to the individualized target, and (3) harmonized EF strength in targeted brain regions across a population.

Method

MRI data including structural, resting-state, and task-based fMRI data were collected from 60 participants with methamphetamine use disorders (MUDs). Craving scores based on a visual analog scale were collected immediately before and after the MRI session. We analyzed inter-subject variability in the location of TMS targets based on the maximum task-based connectivity between the left medial amygdala (with the highest functional activity among subcortical areas during drug cue exposure) and frontopolar cortex using psychophysiological interaction (PPI) analysis. Computational head models were generated for all participants and EF simulations were calculated for fixed vs. optimized coil location (Fp1/Fp2 vs. individualized maximal PPI location), orientation (AF7/AF8 vs. orientation optimization algorithm), and stimulation intensity (constant vs. adjusted intensity across the population).

Results

Left medial amygdala with the highest (mean ± SD: 0.31±0.29) functional activity during drug cue exposure was selected as the subcortical seed region. Amygdala-to-whole brain PPI analysis showed a significant cluster in the prefrontal cortex (cluster size: 2462 voxels, cluster peak in MNI space: [25 39 35]) that confirms cortico-subcortical connections. The location of the voxel with the most positive amygdala-frontopolar PPI connectivity in each participant was considered as the individualized TMS target (mean ± SD of the MNI coordinates: [12.6 64.23 -0.8] ± [13.64 3.50 11.01]). Individual amygdala-frontopolar PPI connectivity in each participant showed a significant correlation with VAS scores after cue exposure (R=0.27, p=0.03). Averaged EF strength in a sphere with r = 5mm around the individualized target location was significantly higher in the optimized (mean ± SD: 0.99 ± 0.21) compared to the fixed approach (Fp1: 0.56 ± 0.22, Fp2: 0.78 ± 0.25) with large effect sizes (Fp1: p = 1.1e-13, Hedges'g = 1.5, Fp2: p = 1.7e-5, Hedges'g = 1.26). Adjustment factor to have identical 1 V/m EF strength in a 5mm sphere around the individualized targets ranged from 0.72 to 2.3 (mean ± SD: 1.07 ± 0.29).

Conclusion

Our results show that optimizing coil orientation and stimulation intensity based on individualized TMS targets led to stronger electric fields in the targeted brain regions compared to a one-size-fits-all approach. These findings provide valuable insights for refining TMS therapy for SUDs by optimizing the modulation of cortico-subcortical circuits.

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.

Transfer Learning in Trajectory Decoding: Sensor or Source Space?

In this study, across-participant and across-session transfer learning was investigated to minimize the calibration time of the brain-computer interface (BCI) system in the context of continuous hand trajectory decoding. We reanalyzed data from a study with 10 able-bodied participants across three sessions. A leave-one-participant-out (LOPO) model was utilized as a starting model. Recursive exponentially weighted partial least squares regression (REW-PLS) was employed to overcome the memory limitation due to the large pool of training data. We considered four scenarios: generalized with no update (Gen), generalized with cumulative update (GenC), and individual models with cumulative (IndC) and non-cumulative (Ind) updates, with each one trained with sensor-space features or source-space features. The decoding performance in generalized models (Gen and GenC) was lower than the chance level. In individual models, the cumulative update (IndC) showed no significant improvement over the non-cumulative model (Ind). The performance showed the decoder's incapability to generalize across participants and sessions in this task. The results suggested that the best correlation could be achieved with the sensor-space individual model, despite additional anatomical information in the source-space features. The decoding pattern showed a more localized pattern around the precuneus over three sessions in Ind models.

Self-assembled three-dimensional hydrogels based on graphene derivatives and cerium oxide nanoparticles: scaffolds for co-culture of oligodendrocytes and neurons derived from neural stem cells

Stem cell-based therapies have shown promising results for the regeneration of the nervous system. However, the survival and integration of the stem cells in the neural circuitry is suboptimal and might compromise the therapeutic outcomes of this approach. The development of functional scaffolds capable of actively interacting with stem cells may overcome the current limitations of stem cell-based therapies. In this study, three-dimensional hydrogels based on graphene derivatives and cerium oxide (CeO₂) nanoparticles are presented as prospective supports allowing neural stem cell adhesion, migration and differentiation. The morphological, mechanical and electrical properties of the resulting hydrogels can be finely tuned by controlling several parameters of the self-assembly of graphene oxide sheets, namely the amount of incorporated reducing agent (ascorbic acid) and CeO₂ nanoparticles. The intrinsic properties of the hydrogels, as well as the presence of CeO₂ nanoparticles, clearly influence the cell fate. Thus, stiffer adhesion substrates promote differentiation to glial cell lineages, while softer substrates enhance mature neuronal differentiation. Remarkably, CeO₂ nanoparticle-containing hydrogels support the differentiation of neural stem cells to neuronal, astroglial and oligodendroglial lineage cells, promoting the in vitro generation of nerve tissue grafts that might be employed in neuroregenerative cell therapies.