A high-resolution computational localization method for transcranial magnetic stimulation mapping

Preoperative Cortical Mapping for Brain Tumor Surgery Using Navigated Transcranial Stimulation: Analysis of Accuracy

Transcranial magnetic stimulation (TMS) represents a distinctive technique for non-invasive brain stimulation. Recent advancements in image processing have enabled the enhancement of TMS by integrating magnetic resonance imaging (MRI) modalities with TMS via a neuronavigation system. The aim of this study is to assess the efficacy of navigated TMS for cortical mapping in comparison to surgical mapping using direct electrical stimulation (DES). This study involved 30 neurosurgical procedures for tumors located in or adjacent to the precentral gyrus. The DES points were compared with TMS responses based on the original distances of vectorial modules. There was a notable similarity in the points obtained from the two mapping methods. The distances between the geometric centers of TMS and DCS were 4.85 ± 1.89 mm. A strong correlation was identified between these vectorial points (r = 0.901, p < 0.001). The motor threshold in TMS was highest in the motor cortex adjacent to the tumor compared to the normal cortex (p < 0.001). Patients with deficits exhibited excellent accuracy in both methods. In view of this, TMS demonstrated reliable and precise application in brain mapping, which is a promising method for preoperative functional mapping in motor cortex tumor surgery.

Individual Prediction of Electric Field Induced by Deep-Brain Magnetic Stimulation With CNN-Transformer

Deep-brain Magnetic Stimulation (DMS) can improve the symptoms caused by Alzheimer's disease by inducing rhythmic electric field in the deep brain, and the induced electric field is rhythm-dependent. However, calculating the induced electric field requires building a voxel model of the brain for the stimulated object, which usually takes several hours. In order to obtain the rhythm-dependent electric field induced by DMS in real time, we adopt a CNN-Transformer model to predict it. A data set with a sample size of 7350 is established for the training and testing of the model. 10-fold cross validation is used to determine the optimal hyperparameters for training CNN-Transformer. The combination of 5-layer CNN and 6-layer Transformer is verified as the optimal combination of CNN-Transformer model. The experimental results show that the CNN-Transformer model can complete the prediction in 0.731s (CPU) or 0.042s (GPU), and the overall performance metrics of prediction can reach: MAE =0.0269, RMSE =0.0420, MAPE =4.61% and R2=0.9627. The prediction performance of the CNN-Transformer model for the hippocampal electric field is better than that of the brain grey matter electric field, and the stimulation rhythm has less influence on the model performance than the coil configuration. Taking the same dataset to train and test the separate CNN model and Transformer model, it is found that CNN-Transformer has better prediction performance than the separate CNN model and Transformer model in the task of predicting electric field induced by DMS.

M2M-InvNet: Human Motor Cortex Mapping From Multi-Muscle Response Using TMS and Generative 3D Convolutional Network

Transcranial magnetic stimulation (TMS) is often applied to the motor cortex to stimulate a collection of motor evoked potentials (MEPs) in groups of peripheral muscles. The causal interface between TMS and MEP is the selective activation of neurons in the motor cortex; moving around the TMS 'spot' over the motor cortex causes different MEP responses. A question of interest is whether a collection of MEP responses can be used to identify the stimulated locations on the cortex, which could potentially be used to then place the TMS coil to produce chosen sets of MEPs. In this work we leverage our previous report on a 3D convolutional neural network (CNN) architecture that predicted MEPs from the induced electric field, to tackle an inverse imaging task in which we start with the MEPs and estimate the stimulated regions on the motor cortex. We present and evaluate five different inverse imaging CNN architectures, both conventional and generative, in terms of several measures of reconstruction accuracy. We found that one architecture, which we propose as M2M-InvNet, consistently achieved the best performance.

Verification of neuronavigated TMS accuracy using structured-light 3D scans

Objective.To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined.Approach.The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for ten healthy participants. The differences in the coil locations and orientations and the theoretical error values for electric and magnetic fields between the neuronavigated and 3D scanned coil positions were calculated. In addition, the sensitivity of the coil location on landmark accuracy was calculated.Main results.The measured distances between the neuronavigated and 3D scanned coil locations were on average 10.2 mm, ranging from 3.1 to 18.7 mm. The error in angles were on average from two to three degrees. The coil misplacement caused on average a 29% relative error in the electric field with a range from 9% to 51%. In the magnetic field, the same error was on average 33%, ranging from 10% to 58%. The misplacement of landmark points could cause a 1.8-fold error for the coil location.Significance.TMS neuronavigation with three landmark points can cause a significant error in the coil position, hampering research using highly accurate electric field calculations. Including 3D scanning to the process provides an efficient method to achieve a more accurate coil position.

Predicting the hotspot location and motor threshold prior to transcranial magnetic stimulation using electric field modelling

Objective.To investigate whether the motor threshold (MT) and the location of the motor hotspot in transcranial magnetic stimulation (TMS) can be predicted with computational models of the induced electric field.Approach.Individualized computational models were constructed from structural magnetic resonance images of ten healthy participants, and the induced electric fields were determined with the finite element method. The models were used to optimize the location and direction of the TMS coil on the scalp to produce the largest electric field at a predetermined cortical target location. The models were also used to predict how the MT changes as the magnetic coil is moved to various locations over the scalp. To validate the model predictions, the motor evoked potentials were measured from the first dorsal interosseous (FDI) muscle with TMS in the ten participants. Both computational and experimental methods were preregistered prior to the experiments.Main results.Computationally optimized hotspot locations were nearly as accurate as those obtained using manual hotspot search procedures. The mean Euclidean distance between the predicted and the measured hotspot locations was approximately 1.3 cm with a 0.8 cm bias towards the anterior direction. Exploratory analyses showed that the bias could be removed by changing the cortical target location that was used for the prediction. The results also indicated a statistically significant relationship (p< 0.001) between the calculated electric field and the MT measured at several locations on the scalp.Significance.The results show that the individual TMS hotspot can be located using computational analysis without stimulating the subject or patient even once. Adapting computational modelling would save time and effort in research and clinical use of TMS.

In-vivoverified anatomically aware deep learning for real-time electric field simulation

Objective.Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement.Approach.We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset.In-vivoTMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes.Significance.The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.

A fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation

When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling. We formulate, test, and disseminate a direct (i.e., non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~ 200,000 or more arbitrarily selected observation points within approximately 5 s, with the solution time itself of 3 s. The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods-an FMM-based LU decomposition. This decomposition is specific to the head model and needs to be computed only once per subject. Moreover, the solver offers unlimited spatial numerical resolution. Despite the fast execution times, the present direct solution is numerically accurate for the default model resolution. In contrast, the widely used brain modeling software SimNIBS employs a first-order finite element method that necessitates additional mesh refinement, resulting in increased computational cost. However, excellent agreement between the two methods is observed for various practical test cases following mesh refinement, including a biophysical modeling task. The method can be readily applied to a wide range of TMS analyses involving multiple coil positions and orientations, including image-guided neuronavigation. It can even accommodate continuous variations in coil geometry, such as flexible H-type TMS coils. The FMM-LU direct solver is freely available to academic users.

A fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation

Background

When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling.

Objective

We formulate, test, and disseminate a direct (i.e., non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~200,000 or more arbitrarily selected observation points within approximately 5 sec, with the solution time itself of 3 sec.

Method

The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods - an FMM-based LU decomposition. This decomposition is specific to the head model and needs to be computed only once per subject. Moreover, the solver offers unlimited spatial numerical resolution.

Results

Despite the fast execution times, the present direct solution is numerically accurate for the default model resolution. In contrast, the widely used brain modeling software SimNIBS employs a first-order finite element method that necessitates additional mesh refinement, resulting in increased computational cost. However, excellent agreement between the two methods is observed for various practical test cases following mesh refinement, including a biophysical modeling task.

Conclusion

The method can be readily applied to a wide range of TMS analyses involving multiple coil positions and orientations, including image-guided neuronavigation. It can even accommodate continuous variations in coil geometry, such as flexible H-type TMS coils. The FMM-LU direct solver is freely available to academic users.

Preoperative individual-target transcranial magnetic stimulation demonstrates an effect comparable to intraoperative direct electrical stimulation in language-eloquent glioma mapping and improves postsurgical outcome: A retrospective fiber-tracking and electromagnetic simulation study

Background

Efforts to resection of glioma lesions located in brain-eloquent areas must balance the extent of resection (EOR) and functional preservation. Currently, intraoperative direct electrical stimulation (DES) is the gold standard for achieving the maximum EOR while preserving as much functionality as possible. However, intraoperative DES inevitably involves risks of infection and epilepsy. The aim of this study was to verify the reliability of individual-target transcranial magnetic stimulation (IT-TMS) in preoperative mapping relative to DES and evaluate its effectiveness based on postsurgical outcomes.

Methods

Sixteen language-eloquent glioma patients were enrolled. Nine of them underwent preoperative nTMS mapping (n=9, nTMS group), and the other seven were assigned to the non-nTMS group and did not undergo preoperative nTMS mapping (n=7). Before surgery, online IT-TMS was performed during a language task in the nTMS group. Sites in the cortex at which this task was disturbed in three consecutive trials were recorded and regarded as positive and designated nTMS hotspots (HS_nTMS). Both groups then underwent awake surgery and intraoperative DES mapping. DES hotspots (HS_DES) were also determined in a manner analogous to HS_nTMS. The spatial distribution of HS_nTMS and HS_DES in the nTMS group was recorded, registered in a single brain template, and compared. The center of gravity (CoG) of HS_nTMS (HS_nTMS-CoG)-based and HS_DES-CoG-based diffusion tensor imaging-fiber tracking (DTI-FT) was performed. The electromagnetic simulation was conducted, and the values were then compared between the nTMS and DES groups, as were the Western Aphasia Battery (WAB) scale and fiber-tracking values.

Results

HS_nTMS and HS_DES showed similar distributions (mean distance 6.32 ± 2.6 mm, distance range 2.2-9.3 mm, 95% CI 3.9-8.7 mm). A higher fractional anisotropy (FA) value in nTMS mapping (P=0.0373) and an analogous fiber tract length (P=0.2290) were observed. A similar distribution of the electric field within the brain tissues induced by nTMS and DES was noted. Compared with the non-nTMS group, the integration of nTMS led to a significant improvement in language performance (WAB scores averaging 78.4 in the nTMS group compared with 59.5 in the non-nTMS group, P=0.0321 < 0.05) as well as in brain-structure preservation (FA value, P=0.0156; tract length, P=0.0166).

Conclusion

Preoperative IT-TMS provides data equally crucial to DES and thus facilitates precise brain mapping and the preservation of linguistic function.

Precise motor mapping with transcranial magnetic stimulation

We describe a routine to precisely localize cortical muscle representations within the primary motor cortex with transcranial magnetic stimulation (TMS) based on the functional relation between induced electric fields at the cortical level and peripheral muscle activation (motor-evoked potentials; MEPs). Besides providing insights into structure-function relationships, this routine lays the foundation for TMS dosing metrics based on subject-specific cortical electric field thresholds. MEPs for different coil positions and orientations are combined with electric field modeling, exploiting the causal nature of neuronal activation to pinpoint the cortical origin of the MEPs. This involves constructing an individual head model using magnetic resonance imaging, recording MEPs via electromyography during TMS and computing the induced electric fields with numerical modeling. The cortical muscle representations are determined by relating the TMS-induced electric fields to the MEP amplitudes. Subsequently, the coil position to optimally stimulate the origin of the identified cortical MEP can be determined by numerical modeling. The protocol requires 2 h of manual preparation, 10 h for the automated head model construction, one TMS session lasting 2 h, 12 h of computational postprocessing and an optional second TMS session lasting 30 min. A basic level of computer science expertise and standard TMS neuronavigation equipment suffices to perform the protocol.

Mapping Brain Motor Functions Using Transcranial Magnetic Stimulation with a Volume Conductor Model and Electrophysiological Experiments

Transcranial magnetic stimulation (TMS) activates brain cells in a noninvasive manner and can be used for mapping brain motor functions. However, the complexity of the brain anatomy prevents the determination of the exact location of the stimulated sites, resulting in the limitation of the spatial resolution of multiple targets. The aim of this study is to map two neighboring muscles in cortical motor areas accurately and quickly. Multiple stimuli were applied to the subject using a TMS stimulator to measure the motor-evoked potentials (MEPs) in the corresponding muscles. For each stimulation condition (coil location and angle), the induced electric field (EF) in the brain was computed using a volume conductor model for an individualized head model of the subject constructed from magnetic resonance images. A post-processing method was implemented to determine a TMS hotspot using EF corresponding to multiple stimuli, considering the amplitude of the measured MEPs. The dependence of the computationally estimated hotspot distribution on two target muscles was evaluated (n = 11). The center of gravity of the first dorsal interosseous cortical representation was lateral to the abductor digiti minimi by a minimum of 2 mm. The localizations were consistent with the putative sites obtained from previous EF-based studies and fMRI studies. The simultaneous cortical mapping of two finger muscles was achieved with only several stimuli, which is one or two orders of magnitude smaller than that in previous studies. Our proposal would be useful in the preoperative mapping of motor or speech areas to plan brain surgery interventions.

Dosing Transcranial Magnetic Stimulation of the Primary Motor and Dorsolateral Prefrontal Cortices With Multi-Scale Modeling

Transcranial magnetic stimulation (TMS) can depolarize cortical neurons through the intact skin and skull. The characteristics of the induced electric field (E-field) have a major impact on specific outcomes of TMS. Using multi-scale computational modeling, we explored whether the stimulation parameters derived from the primary motor cortex (M1) induce comparable macroscopic E-field strengths and subcellular/cellular responses in the dorsolateral prefrontal cortex (DLPFC). To this aim, we calculated the TMS-induced E-field in 16 anatomically realistic head models and simulated the changes in membrane voltage and intracellular calcium levels of morphologically and biophysically realistic human pyramidal cells in the M1 and DLPFC. We found that the conventional intensity selection methods (i.e., motor threshold and fixed intensities) produce variable macroscopic E-fields. Consequently, it was challenging to produce comparable subcellular/cellular responses across cortical regions with distinct folding characteristics. Prospectively, personalized stimulation intensity selection could standardize the E-fields and the subcellular/cellular responses to repetitive TMS across cortical regions and individuals. The suggested computational approach points to the shortcomings of the conventional intensity selection methods used in clinical settings. We propose that multi-scale modeling has the potential to overcome some of these limitations and broaden our understanding of the neuronal mechanisms for TMS.

Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression

Transcranial magnetic stimulation (TMS) is a powerful tool to investigate causal structure-function relationships in the human brain. However, a precise delineation of the effectively stimulated neuronal populations is notoriously impeded by the widespread and complex distribution of the induced electric field. Here, we propose a method that allows rapid and feasible cortical localization at the individual subject level. The functional relationship between electric field and behavioral effect is quantified by combining experimental data with numerically modeled fields to identify the cortical origin of the modulated effect. Motor evoked potentials (MEPs) from three finger muscles were recorded for a set of random stimulations around the primary motor area. All induced electric fields were nonlinearly regressed against the elicited MEPs to identify their cortical origin. We could distinguish cortical muscle representation with high spatial resolution and localized them primarily on the crowns and rims of the precentral gyrus. A post-hoc analysis revealed exponential convergence of the method with the number of stimulations, yielding a minimum of about 180 random stimulations to obtain stable results. Establishing a functional link between the modulated effect and the underlying mode of action, the induced electric field, is a fundamental step to fully exploit the potential of TMS. In contrast to previous approaches, the presented protocol is particularly easy to implement, fast to apply, and very robust due to the random coil positioning and therefore is suitable for practical and clinical applications.

A probabilistic transcranial magnetic stimulation localization method

Objective.Transcranial magnetic stimulation (TMS) can be used to safely and noninvasively activate brain tissue. However, the characteristic parameters of the neuronal activation have been largely unclear. In this work, we propose a novel neuronal activation model and develop a method to infer its parameters from measured motor evoked potential signals.Approach.The connection between neuronal activation due to an induced electric field and a measured motor threshold is modeled. The posterior distribution of the model parameters are inferred from measurement data using Bayes' formula. The measurements are the active motor thresholds obtained with multiple stimulating coil locations, and the parameters of the model are the location, preferred direction of activation, and threshold electric field value of the activation site. The posterior distribution is sampled using a Markov chain Monte Carlo method. We quantify the plausibility of the model by calculating the marginal likelihood of the measured thresholds. The method is validated with synthetic data and applied to motor threshold measurements from the first dorsal interosseus muscle in five healthy participants.Main results.The method produces a probability distribution for the activation location, from which a minimal volume where the activation occurs with 95% probability can be derived. For eight or nine stimulating coil locations, the smallest such a volume obtained was approximately 100 mm³. The 95% probability volume intersected the pre-central gyral crown and the anterior wall of the central sulcus, and the preferred direction was perpendicular to the central sulcus, both findings being consistent with the literature. Furthermore, it was not possible to rule out if the activation occurred either in the white or grey matter. In one participant, two distinct activations sites were found while others exhibited a unique site.Significance.The method is both generic and robust, and it lays a foundation for a framework that enables accurate analysis and characterization of TMS activation mechanisms.

Dosimetry Analysis in Non-brain Tissues During TMS Exposure of Broca's and M1 Areas

For human protection, the internal electric field is used as a dosimetric quantity for electromagnetic fields lower than 5–10 MHz. According to international standards, in this frequency range, electrostimulation is the main adverse effect against which protection is needed. One of the topics to be investigated is the quantification of the internal electric field threshold levels of perception and pain. Pain has been reported as a side effect during transcranial magnetic stimulation (TMS), especially during stimulation of the Broca’s (speech) area of the brain. In this study, we designed an experiment to conduct a dosimetry analysis to quantify the internal electric field corresponding to perception and pain thresholds when targeting the Broca’s and M1 areas from magnetic stimulator exposure. Dosimetry analysis was conducted using a multi-scale analysis in an individualized head model to investigate electrostimulation in an axonal model. The main finding is that the stimulation on the primary motor cortex has higher perception and pain thresholds when compared to Broca’s area. Also, TMS-induced electric field applied to Broca’s area exhibited dependence on the coil orientation at lower electric field threshold which was found to be related to the location and thickness of pain fibers. The derived dosimetry quantities provide a scientific rationale for the development of human protection guidelines and the estimation of possible side effects of magnetic stimulation in clinical applications.

Systematic Review on the Safety and Tolerability of Transcranial Direct Current Stimulation in Children and Adolescents

BACKGROUND

Transcranial direct current stimulation (tDCS) is a safe, tolerable, and acceptable technique in adults. However, there is limited evidence for its safety in youth. Although limited, there are a handful of important empirical articles that have evaluated safety and tolerability outcomes in youth. However, a synthesis of pediatric safety studies is not currently available.

OBJECTIVE

To synthesize objective evidence regarding the safety and tolerability of pediatric tDCS based on the current state of the literature.

METHODS

Our search and report used PRISMA guidelines. Our method systematically examined investigations purposefully designed to evaluate the safety, tolerability, and acceptability of tDCS in healthy and atypical youth that were submitted to three databases, from the beginning of the database to November 2019. Safety considerations were evaluated by studies utilizing neuroimaging, physiological changes, performance on tasks, and by analyzing reported and objective side effects; tolerability via rate of adverse events; and acceptability via rate of dropouts.

RESULTS

We report on 203 sham sessions, 864 active sessions up to 2 mA, and 303 active hours of stimulation in 156 children. A total of 4.4% of the active sessions were in neurotypical controls, with the other 95.6% in clinical subjects.

CONCLUSION

In spite of the fact that the current evidence is sporadic and scarce, the presently reviewed literature provides support for the safety, tolerability, and acceptability, of tDCS in youth for 1-20 sessions of 20 min up to 2 mA. Future pediatric tDCS research is encouraged.

Effect of skin conductivity on the electric field induced by transcranial stimulation techniques in different head models

This study aims at quantifying the effect that using different skin conductivity values has on the estimation of the electric (E)-field distribution induced by transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) in the brain of two anatomical models. The induced E-field was calculated with numerical simulations inside MIDA and Duke models, assigning to the skin a conductivity value estimated from a multi-layered skin model and three values taken from literature. The effect of skin conductivity variations on the local E-field induced by tDCS in the brain was up to 70%. In TMS, minor local differences, in the order of 20%, were obtained in regions of interest for the onset of possible side effects. Results suggested that an accurate model of the skin is necessary in all numerical studies that aim at precisely estimating the E-field induced during TMS and tDCS applications. This also highlights the importance of further experimental studies on human skin characterization, especially at low frequencies.

Comparing navigated transcranial magnetic stimulation mapping and "gold standard" direct cortical stimulation mapping in neurosurgery: a systematic review

The objective of this systematic review is to create an overview of the literature on the comparison of navigated transcranial magnetic stimulation (nTMS) as a mapping tool to the current gold standard, which is (intraoperative) direct cortical stimulation (DCS) mapping. A search in the databases of PubMed, EMBASE, and Web of Science was performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and recommendations were used. Thirty-five publications were included in the review, describing a total of 552 patients. All studies concerned either mapping of motor or language function. No comparative data for nTMS and DCS for other neurological functions were found. For motor mapping, the distances between the cortical representation of the different muscle groups identified by nTMS and DCS varied between 2 and 16 mm. Regarding mapping of language function, solely an object naming task was performed in the comparative studies on nTMS and DCS. Sensitivity and specificity ranged from 10 to 100% and 13.3–98%, respectively, when nTMS language mapping was compared with DCS mapping. The positive predictive value (PPV) and negative predictive value (NPV) ranged from 17 to 75% and 57–100% respectively. The available evidence for nTMS as a mapping modality for motor and language function is discussed.

Spatial extent of cortical motor hotspot in navigated transcranial magnetic stimulation

BACKGROUND

Motor mapping with navigated transcranial magnetic stimulation (nTMS) requires defining a "hotspot", a stimulation site consistently producing the highest-amplitude motor-evoked potentials (MEPs). The exact location of the hotspot is difficult to determine, and the spatial extent of high-amplitude MEPs usually remains undefined due to MEP variability and the spread of the TMS-induced electric field (E-field). Therefore, here we aim to define the hotspot as a sub-region of a motor map.

NEW METHOD

We analyzed MEP amplitude distributions in motor mappings of 30 healthy subjects in two orthogonal directions on the motor cortex. Based on the widths of these distributions, the hotspot extent was estimated as an elliptic area. In addition, E-field distributions induced by motor map edge stimulations were simulated for ten subjects, and the E-field attenuation was analyzed to obtain another estimate for hotspot extent.

RESULTS

The median MEP-based hotspot area was 13 mm² (95% confidence interval (CI) = [10, 18] mm²). The mean E-field-based hotspot area was 26 mm² (95% CI = [13, 38] mm²).

COMPARISON WITH EXISTING METHODS

In contrast to the conventional hotspot, the new definition considers its spatial extent, indicating the most easily excited area where subsequent nTMS stimuli should be targeted for maximal response. The E-field-based hotspot provides an estimate for the extent of cortical structures where the E-field is close to its maximum.

CONCLUSIONS

The nTMS hotspot should be considered as an area rather than a single qualitatively defined spot due to MEP variability and E-field spread.

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is a commonly used clinical procedure. However, personalized TMS requires a pipeline for individual head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This study proposes a novel approach for fast and automatic estimation of the electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1- and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on the water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field distributions of the brain than that provided by conventional approaches.

Group-level analysis of induced electric field in deep brain regions by different TMS coils

Deep transcranial magnetic stimulation (dTMS) is a non-invasive technique used for the treatment of depression and obsessive compulsive disorder. In this study, we computationally evaluated group-level dosage for dTMS to characterize the targeted deep brain regions to overcome the limitations of using individualized head models to characterize coil performance in a population. We used an inter-subject registration method adapted to the deep brain regions that enable projection of computed electric fields (EFs) from individual realistic head models (n  =  18) to the average space of deep brain regions. The computational results showed consistent group-level hotspots of the EF in the deep brain regions. The halo circular assembly coils induced the highest EFs in deep brain regions (up to 50% of the maximum EF in the cortex) for optimized positioning. In terms of the trade-off between field spread and penetration, the performance of the H7 coil was the best. The computational model allowed the optimization of generalized dTMS-induced EF on deep region targets despite inter-individual differences while informing and possibly minimizing unintended stimulation of superficial regions and possible mixed stimulation effects from deep and cortical areas. These results will facilitate the decision process during dTMS interventions in clinical practice.

The effect of coil placement and orientation on the assessment of focal excitability in motor mapping with navigated transcranial magnetic stimulation

BACKGROUND

Navigated transcranial magnetic stimulation (nTMS) is used for mapping muscle representations in the primary motor cortex. We used sulcus-aligned mapping and electric field (E-field) modeling to investigate the excitability of the motor hand area for further understanding the methodological limitations of nTMS.

NEW METHOD

We studied 10 healthy volunteers to locate the cortical target eliciting the largest responses (the hotspot) in the first dorsal interosseous (FDI) muscle. Six additional targets were placed along the central sulcus at 5-mm distances. Resting motor thresholds (rMTs) and optimal coil orientations were determined at all targets, and a conventional motor mapping was conducted. The cortical E-fields, induced by stimulating the targets with rMT intensities and optimal coil orientations, were modeled in a realistic head geometry to estimate the activated cortical sites.

RESULTS

The rMTs increased with increasing distance from the hotspot (p < 0.001). The greatest motor-evoked potential (MEP) amplitudes occurred with the coil perpendicular to the sulcus and with the coil pointing towards the hotspot or the center of gravity of the motor map. The E-field strengths at the hotspot (99±26 V/m) remained above previously estimated thresholds for activation.

COMPARISON WITH EXISTING METHODS

Depending on the target location, optimal coil orientations may deviate significantly from the conventional perpendicular-to-sulcus angle, which is often assumed optimal. These orientations seem to maintain the E-field stable in the hand knob, regardless of the sulcal shape near the stimulated target.

CONCLUSIONS

The coil orientation is crucial for the accuracy of motor mapping, and the apparent motor map may extend due to remote hotspot activation.

Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons

BACKGROUND

Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements TMS activates and how stimulation parameters affect the neural response.

OBJECTIVE

To develop a multi-scale computational model to quantify the effect of TMS parameters on the direct response of individual neurons.

METHODS

We integrated morphologically-realistic neuronal models with TMS-induced electric fields computed in a finite element model of a human head to quantify the cortical response to TMS with several combinations of pulse waveforms and current directions.

RESULTS

TMS activated with lowest intensity intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 5 pyramidal cells had the lowest thresholds, but layer 2/3 pyramidal cells and inhibitory basket cells were also activated at most intensities. Direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, rather than the field component normal to the cortical surface. Varying the induced current direction caused a waveform-dependent shift in the activation site and provided a potential mechanism for experimentally observed differences in thresholds and latencies of muscle responses.

CONCLUSIONS

This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other cortical stimulation modalities. It also serves as a foundation for more detailed network models of the response to TMS, which may include endogenous activity, synaptic connectivity, inputs from intrinsic and extrinsic axonal projections, and corticofugal axons in white matter.

Statistical Model of Motor-Evoked Potentials

Motor-evoked potentials (MEPs) are widely used for biomarkers and dose individualization in transcranial stimulation. The large variability of MEPs requires sophisticated methods of analysis to extract information fast and correctly. Development and testing of such methods relies on the availability for realistic models of MEP generation, which are presently lacking. This paper presents a statistical model that can simulate long sequences of individualized MEP amplitude data with properties matching experimental observations. The MEP model includes three sources of trial-to-trial variability: excitability fluctuations, variability in the neural and muscular pathways, and physiological and measurement noise. It also generates virtual human subject data from statistics of population variability. All parameters are extracted as statistical distributions from experimental data from the literature. The model exhibits previously described features, such as stimulus-intensity-dependent MEP amplitude distributions, including bimodal ones. The model can generate long sequences of test data for individual subjects with specified parameters or for subjects from a virtual population. The presented MEP model is the most detailed to date and can be used for the development and implementation of dosing and biomarker estimation algorithms for transcranial stimulation.

Real-time estimation of electric fields induced by transcranial magnetic stimulation with deep neural networks

BACKGROUND

Transcranial magnetic stimulation (TMS) plays an important role in treatment of mental and neurological illnesses, and neurosurgery. However, it is difficult to target specific brain regions accurately because the complex anatomy of the brain substantially affects the shape and strength of the electric fields induced by the TMS coil. A volume conductor model can be used for determining the accurate electric fields; however, the construction of subject-specific anatomical head structures is time-consuming.

OBJECTIVE

The aim of this study is to propose a method to estimate electric fields induced by TMS from only T1 magnetic resonance (MR) images, without constructing a subject-specific anatomical model.

METHODS

Very large sets of electric fields in the brain of subject-specific anatomical models, which are constructed from T1 and T2 MR images, are computed by a volume conductor model. The relation between electric field distribution and T1 MR images is used for machine learning. Deep neural network (DNN) models are applied for the first time to electric field estimation.

RESULTS

By determining the relationships between the T1 MR images and electric fields by DNN models, the process of electric field estimation is markedly accelerated (to 0.03 s) due to the absence of a requirement for anatomical head structure reconstruction and volume conductor computation. Validation shows promising estimation accuracy, and rapid computations of the DNN model are apt for practical applications.

CONCLUSION

The study showed that the DNN model can estimate the electric fields from only T1 MR images and requires low computation time, suggesting the possibility of using machine learning for real-time electric field estimation in navigated TMS.

Corticomotoneuronal Model for Intraoperative Neurophysiological Monitoring During Direct Brain Stimulation

Intraoperative neurophysiological monitoring during brain surgery uses direct cortical stimulation to map the motor cortex by recording muscle activity induced by the excitation of alpha motor neurons (MNs). Computational models have been used to understand local brain stimulation. However, a computational model revealing the stimulation process from the cortex to MNs has not yet been proposed. Thus, the aim of the current study was to develop a corticomotoneuronal (CMN) model to investigate intraoperative stimulation during surgery. The CMN combined the following three processes into one system for the first time: (1) induction of an electric field in the brain based on a volume conductor model; (2) activation of pyramidal neuron (PNs) with a compartment model; and (3) formation of presynaptic connections of the PNs to MNs using a conductance-based synaptic model coupled with a spiking model. The implemented volume conductor model coupled with the axon model agreed with experimental strength-duration curves. Additionally, temporal/spatial and facilitation effects of CMN synapses were implemented and verified. Finally, the integrated CMN model was verified with experimental data. The results demonstrated that our model was necessary to describe the interaction between frequency and pulses to assess the difference between low-frequency and multi-pulse high-frequency stimulation in cortical stimulation. The proposed model can be used to investigate the effect of stimulation parameters on the cortex to optimize intraoperative monitoring.

Intraoperative direct subcortical stimulation: comparison of monopolar and bipolar stimulation

Intraoperative subcortical electrical stimulation is used to identify and preserve white matter tracts so that tumor resection can be performed while avoiding postsurgical deficits. The effects of the stimulating electrodes in identifying the white matter tracts have not been characterized; thus, different hospitals use different electrode configurations. Computational modeling can be used to conduct a systematic assessment of the effects of the stimulating electrode parameters. However, no realistic computational model of subcortical electrical stimulation has been implemented and verified. In this study, we investigated the interaction between the corticospinal tract (CST) and subcortical stimulation and compared different electrode configurations during monopolar and bipolar stimulation. For that, we computed the induced electric field in a realistic human head model coupled with a CST axon model. The implemented model was verified with available experimental data that were acquired during subcortical stimulation, and a systematic sensitivity analysis of parameters related to the stimulation was conducted. The results showed that the optimal stimulation varies according to the surgery conditions. If the CST was close to the resection border, bipolar stimulation could produce more selective activation. Monopolar stimulation was more robust and more effective for the CST far from the stimulation point.