Impact of non-brain anatomy and coil orientation on inter- and intra-subject variability in TMS at midline

A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications. This review paper discusses several modern algorithms for real-time or near real-time TMS E-field modeling and their advantages and limitations. The reviewed methods include techniques such as basis representation techniques and deep neural-network-based methods. This paper also provides a review of software tools that can integrate E-field modeling with navigated TMS, including a recent software for real-time navigated E-field mapping based on deep neural-network models.

Phosphene and motor transcranial magnetic stimulation thresholds are correlated: A meta-analytic investigation

Transcranial magnetic stimulation (TMS) is commonly delivered at an intensity defined by the resting motor threshold (rMT), which is thought to represent cortical excitability, even if the TMS target area falls outside of the motor cortex. This approach rests on the assumption that cortical excitability, as measured through the motor cortex, represents a 'global' measure of excitability. Another common approach to measure cortical excitability relies on the phosphene threshold (PT), measured through the visual cortex of the brain. However, it remains unclear whether either estimate can serve as a singular measure to infer cortical excitability across different brain regions. If PT and rMT can indeed be used to infer cortical excitability across brain regions, they should be correlated. To test this, we systematically identified previous studies that measured PT and rMT to calculate an overall correlation between the two estimates. Our results, based on 16 effect sizes from eight studies, indicated that PT and rMT are correlated (ρ = 0.4), and thus one measure could potentially serve as a measure to infer cortical excitability across brain regions. Three exploratory meta-analyses revealed that the strength of the correlation is affected by different methodologies, and that PT intensities are higher than rMT. Evidence for a PT-rMT correlation remained robust across all analyses. Further research is necessary for an in-depth understanding of how cortical excitability is reflected through TMS.

Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions

The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Background

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

Objectives

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

Methods

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

Results

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

Conclusions

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

Online neurostimulation of Broca's area does not interfere with syntactic predictions: A combined TMS-EEG approach to basic linguistic combination

Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in language. Accordingly, grammar-based expectations are formed-e.g., the determiner "a" triggers the prediction for a noun-and facilitate the analysis of incoming syntactic information, which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Broca's area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during incremental language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Broca's area is functionally relevant in predictive mechanisms for language. We transiently perturbed Broca's area during the first word in a two-word construction, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Broca's area is involved in predictive mechanisms for syntax, disruptive TMS during the first word would mitigate the difference in the ERP responses for predicted and unpredicted categories in basic two-word constructions. Contrary to this hypothesis, perturbation of Broca's area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the ERP responses further confirmed this pattern. We discuss the present results considering an alternative account of the role of Broca's area in syntactic composition, namely the bottom-up integration of words into constituents, and of compensatory mechanisms within the language predictive network.

Detailed measurements and simulations of electric field distribution of two TMS coils cleared for obsessive compulsive disorder in the brain and in specific regions associated with OCD

The FDA cleared deep transcranial magnetic stimulation (Deep TMS) with the H7 coil for obsessive-compulsive disorder (OCD) treatment, following a double-blinded placebo-controlled multicenter trial. Two years later the FDA cleared TMS with the D-B80 coil on the basis of substantial equivalence. In order to investigate the induced electric field characteristics of the two coils, these were placed at the treatment position for OCD over the prefrontal cortex of a head phantom, and the field distribution was measured. Additionally, numerical simulations were performed in eight Population Head Model repository models with two sets of conductivity values and three Virtual Population anatomical head models and their homogeneous versions. The H7 was found to induce significantly higher maximal electric fields (p<0.0001, t = 11.08) and to stimulate two to five times larger volumes in the brain (p<0.0001, t = 6.71). The rate of decay of electric field with distance is significantly slower for the H7 coil (p < 0.0001, Wilcoxon matched-pairs test). The field at the scalp is 306% of the field at a 3 cm depth with the D-B80, and 155% with the H7 coil. The H7 induces significantly higher intensities in broader volumes within the brain and in specific brain regions known to be implicated in OCD (dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex (dlPFC), inferior frontal gyrus (IFG), orbitofrontal cortex (OFC) and pre-supplementary motor area (pre-SMA)) compared to the D-B80. Significant field ≥ 80 V/m is induced by the H7 (D-B80) in 15% (1%) of the dACC, 78% (29%) of the pre-SMA, 50% (20%) of the dlPFC, 30% (12%) of the OFC and 15% (1%) of the IFG. Considering the substantial differences between the two coils, the clinical efficacy in OCD should be tested and verified separately for each coil.

Investigating the Effects of Anatomical Structures on the Induced Electric Field in the Brain in Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is capable of stimulating neurons in the brain non-invasively and provides numerous possibilities for the treatment of various neurological disorders such as major depressive disorder, Parkinson's disease, obsessive compulsive disorder. TMS coils can affect the distribution of induced electric fields significantly, thus the design of TMS coils is always a popular topic in TMS studies. Yet the importance of the role of anatomical structures in the induced electric field has not been thoroughly investigated. Therefore, this work has compared the strength of electric fields induced from fifty realistic head models with twelve commercial or novel TMS coils to explore how anatomical structures affect the electric field. It has been found that the electric field strengths among the fifty head models showed highly correlated patterns. The coils were placed at two positions, where all the twelve coils were placed at the vertex and eight of them were placed at the dorsolateral prefrontal cortex of the head due to the coil geometry. Notably, fifty heterogeneous head models that are derived from MRI data were used in the simulations for examining the difference on the performance of TMS coils caused by different anatomical structures. A total of one thousand simulations have been conducted, providing a large amount of data for analysis. Clinical Relevance- This provides a basis to make treatment protocols or predictions in TMS clinical trials considering the different anatomical structures among subjects.

Effect of neuroanatomy on corticomotor excitability during and after transcranial magnetic stimulation and intermittent theta burst stimulation

Individual neuroanatomy can influence motor responses to transcranial magnetic stimulation (TMS) and corticomotor excitability after intermittent theta burst stimulation (iTBS). The purpose of this study was to examine the relationship between individual neuroanatomy and both TMS response measured using resting motor threshold (RMT) and iTBS measured using motor evoked potentials (MEPs) targeting the biceps brachii and first dorsal interosseus (FDI). Ten nonimpaired individuals completed sham‐controlled iTBS sessions and underwent MRI, from which anatomically accurate head models were generated. Neuroanatomical parameters established through fiber tractography were fiber tract surface area (FTSA), tract fiber count (TFC), and brain scalp distance (BSD) at the point of stimulation. Cortical magnetic field induced electric field strength (EFS) was obtained using finite element simulations. A linear mixed effects model was used to assess effects of these parameters on RMT and iTBS (post‐iTBS MEPs). FDI RMT was dependent on interactions between EFS and both FTSA and TFC. Biceps RMT was dependent on interactions between EFS and and both FTSA and BSD. There was no groupwide effect of iTBS on the FDI but individual changes in corticomotor excitability scaled with RMT, EFS, BSD, and FTSA. iTBS targeting the biceps was facilitatory, and dependent on FTSA and TFC. MRI‐based measures of neuroanatomy highlight how individual anatomy affects motor system responses to different TMS paradigms and may be useful for selecting appropriate motor targets when designing TMS based therapies.

Angle-tuned coils: attractive building blocks for TMS with improved depth-spread performance

Objective.A novel angle-tuned ring coil is proposed for improving the depth-spread performance of transcranial magnetic stimulation (TMS) coils and serve as the building blocks for high-performance composite coils and multisite TMS systems.Approach.Improving depth-spread performance by reducing field divergence through creating a more elliptical emitted field distribution from the coil. To accomplish that, instead of enriching the Fourier components along the planarized (x-y) directions, which requires different arrays to occupy large brain surface areas, we worked along the radial (z) direction by using tilted coil angles and stacking coil numbers to reduce the divergence of the emitted near field without occupying large head surface areas. The emitted electric field distributions were theoretically simulated in spherical and real human head models to analyze the depth-spread performance of proposed coils and compare with existing figure-8 coils. The results were then experimentally validated with field probes andin-vivoanimal tests.Main results.The proposed 'angle-tuning' concept improves the depth-spread performance of individual coils with a significantly smaller footprint than existing and proposed coils. For composite structures, using the proposed coils as basic building blocks simplifies the design and manufacturing process and helps accomplish a leading depth-spread performance. In addition, the footprint of the proposed system is intrinsically small, making them suitable for multisite stimulations of inter and intra-hemispheric brain regions with an improved spread and less electric field divergence.Significance.Few brain functions are operated by isolated single brain regions but rather by coordinated networks involving multiple brain regions. Simultaneous or sequential multisite stimulations may provide tools for mechanistic studies of brain functions and the treatment of neuropsychiatric disorders. The proposed AT coil goes beyond the traditional depth-spread tradeoff rule of TMS coils, which provides the possibility of building new composite structures and new multisite TMS tools.

Electric Field Distribution Induced by TMS: Differences Due to Anatomical Variation

Transcranial magnetic stimulation (TMS) is a well-established technique for the diagnosis and treatment of neuropsychiatric diseases. The numerical calculation of the induced electric field (EF) distribution in the brain increases the efficacy of stimulation and improves clinical outcomes. However, unique anatomical features, which distinguish each subject, suggest that personalized models should be preferentially used. The objective of the present study was to assess how anatomy affects the EF distribution and to determine to what extent personalized models are useful for clinical studies. The head models of nineteen healthy volunteers were automatically segmented. Two versions of each head model, a homogeneous and a five-tissue anatomical, were stimulated by the model of a Hesed coil (H-coil), employing magnetic quasi-static simulations. The H-coil was placed at two standard stimulating positions per model, over the frontal and central areas. The results show small, but indisputable, variations in the EFs for the homogeneous and anatomical models. The interquartile ranges in the anatomical versions were higher compared to the homogeneous ones, indicating that individual anatomical features may affect the prediction of stimulation volumes. It is concluded that personalized models provide complementary information and should be preferably employed in the context of diagnostic and therapeutic TMS studies.

Transcranial Magnetic Stimulation for the Neurological Patient: Scientific Principles and Applications

Non-invasive brain stimulation has been increasingly recognized for its potential as an investigational, diagnostic and therapeutic tool across the clinical neurosciences. Transcranial magnetic stimulation (TMS) is a non-invasive method of focal neuromodulation. Diagnostically, TMS can be used to probe cortical excitability and plasticity, as well as for functional mapping. Therapeutically, depending on the pattern employed, TMS can either facilitate or inhibit stimulated cortex potentially modulating maladaptive physiology through its effects on neuroplasticity. Despite this potential, applications of TMS in neurology have only been approved for diagnostic clinical neurophysiology, pre-surgical mapping of motor and language cortex, and the treatment of migraines. In this article, we discuss the principles of TMS and its clinical applications in neurology, including experimental applications in stroke rehabilitation, seizures, autism spectrum disorder, neurodegenerative disorders, movement disorders, tinnitus, chronic pain and functional neurological disorder. To promote increased cross-talk across neurology and psychiatry, we also succinctly review the TMS literature for the treatment of major depression and obsessive compulsive disorder. Overall, we argue that larger clinical trials that are better informed by circuit-level biomarkers and pathophysiological models will lead to an expansion of the application of TMS for patients cared for by neurologists.

White matter markers and predictors for subject-specific rTMS response in major depressive disorder

Repetitive transcranial magnetic stimulation (rTMS) has established therapeutic efficacy for major depressive disorder (MDD). While translational research has focused primarily on understanding the mechanism of action of TMS on functional activation and connectivity, the effects on structural connectivity remain largely unknown especially when rTMS is applied using subject-specific brain targets. This study aims to use novel diffusion magnetic resonance imaging (dMRI) analysis to examine microstructural changes related to rTMS treatment response using a unique cohort of 21 patients with MDD treated using rTMS with subject-specific targets. White matter dMRI microstructural measures and clinical scores were captured before and after the full course of treatment. We defined disease-relevant fiber bundles connected to different subregions of the left prefrontal cortex and analyzed changes in diffusion properties as well as correlations between the changes of dMRI measures and the changes in Hamilton Depression Rating Scale (HAMD). No significant changes were observed in tracts connected to the TMS targets. rTMS significantly increased the extra-axonal free-water volume, fractional anisotropy and decreased the radial diffusivity in anterior-medial prefrontal fiber bundles but did not lead to raw changes in lateral prefrontal tracts. That said, the microstructural changes in the lateral prefrontal white matter were significantly correlated with treatment response. Moreover, pre-rTMS dMRI measures of the dorsal anterior cingulate cortex and lateral prefrontal cortex connections are correlated with changes in HAMD scores. Microstructural changes in the anterior-medial and lateral prefrontal white matter are potentially involved in treatment response to TMS, though further investigation is needed using larger datasets.

An integrated TMS-EEG and MRI approach to explore the interregional connectivity of the default mode network

Explorations of the relation between brain anatomy and functional connections in the brain are crucial for shedding more light on network connectivity that sustains brain communication. In this study, by means of an integrative approach, we examined both the structural and functional connections of the default mode network (DMN) in a group of sixteen healthy subjects. For each subject, the DMN was extracted from the structural and functional resonance imaging data; the areas that were part of the DMN were defined as the regions of interest. Then, the target network was structurally explored by diffusion-weighted imaging, tested by neurophysiological means, and retested by means of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG). A series of correlational analyses were performed to explore the relationship between the amplitude of early-latency TMS-evoked potentials and the indexes of structural connectivity (weighted number of fibres and fractional anisotropy). Stimulation of the left or right parietal nodes of the DMN-induced activation in the contralateral parietal and frontocentral electrodes within 60 ms; this activation correlated with fractional anisotropy measures of the corpus callosum. These results showed that distant secondary activations after target stimulation can be predicted based on the target’s anatomical connections. Interestingly, structural features of the corpus callosum predicted the activation of the directly connected nodes, i.e., parietal-parietal nodes, and of the broader DMN network, i.e., parietal-frontal nodes, as identified with functional magnetic resonance imaging. Our results suggested that the proposed integrated approach would allow us to describe the contributory causal relationship between structural connectivity and functional connectivity of the DMN.

Multisite non-invasive brain stimulation in Parkinson's disease: A scoping review

BACKGROUND:

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, characterized by cardinal motor symptoms in addition to cognitive impairment. New insights concerning multisite non-invasive brain stimulation effects have been gained, which can now be used to develop innovative treatment approaches.

OBJECTIVE:

Map the researchs involving multisite non-invasive brain stimulation in PD, synthesize the available evidence and discuss future directions.

METHODS:

The databases PubMed, PsycINFO, CINAHL, LILACS and The Cochrane Library were searched from inception until April 2020, without restrictions on the date of publication or the language in which it was published. The reviewers worked in pairs and sequentially evaluated the titles, abstracts and then the full text of all publications identified as potentially relevant.

RESULTS:

Twelve articles met the inclusion criteria. The target brain regions included mainly the combination of a motor and a frontal area, such as stimulation of the primary motor córtex associated with the dorsolateral prefrontal cortex. Most of the trials showed that this modality was only more effective for the motor component, or for the cognitive and/or non-motor, separately.

CONCLUSIONS:

Despite the results being encouraging for the use of the multisite aproach, the indication for PD management should be carried out with caution and deserves scientific deepening.

Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.

Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES

In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. However, the segmentation of different tissues is a tedious task with several associated challenges specially with tissues appear in limited regions and/or low-contrast in anatomical images. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results.

Development of anatomically accurate brain phantom for experimental validation of stimulation strengths during TMS

Transcranial magnetic stimulation (TMS) is a non-invasive technique for diagnosis and treatment of various neurological conditions. However, the lack of realistic physical models to test the safety and efficacy of stimulation from magnetic fields generated by the coils has hindered the development of new TMS treatment and diagnosis protocols for several neurological conditions. We have developed an anatomically and geometrically accurate brain and head phantom with an adjustable electrical conductivity matching the average conductivity of white matter and grey matter of the human brain and the cerebrospinal fluid. The process of producing the phantom starts with segmenting the MRI images of the brain and then creating shells from the segmented and reconstructed model ready for 3-D printing and serving as a mold for the conductive polymer. Furthermore, we present SEM images and conductivity measurements of the conductive polymer composite as well as confirmation of the anatomical accuracy of the phantom with computed tomography (CT) images. Finally, we show the results of induced voltage measurements obtained from TMS on the brain phantom.

Focality of the Induced E-Field Is a Contributing Factor in the Choice of TMS Parameters: Evidence from a 3D Computational Model of the Human Brain

Transcranial magnetic stimulation (TMS) is a promising, non-invasive approach in the diagnosis and treatment of several neurological conditions. However, the specific results in the cortex of the magnitude and spatial distribution of the secondary electrical field (E-field) resulting from TMS at different stimulation sites/orientations and varied TMS parameters are not clearly understood. The objective of this study is to identify the impact of TMS stimulation site and coil orientation on the induced E-field, including spatial distribution and the volume of activation in the cortex across brain areas, and hence demonstrate the need for customized optimization, using a three-dimensional finite element model (FEM). A considerable difference was noted in E-field values and distribution at different brain areas. We observed that the volume of activated cortex varied from 3000 to 7000 mm3 between the selected nine clinically relevant coil locations. Coil orientation also changed the induced E-field by a maximum of 10%, and we noted the least optimal values at the standard coil orientation pointing to the nose. The volume of gray matter activated varied by 10% on average between stimulation sites in homologous brain areas in the two hemispheres of the brain. This FEM simulation model clearly demonstrates the importance of TMS parameters for optimal results in clinically relevant brain areas. The results show that TMS parameters cannot be interchangeably used between individuals, hemispheres, and brain areas. The focality of the TMS induced E-field along with its optimal magnitude should be considered as critical TMS parameters that should be individually optimized.

A software toolkit for TMS electric-field modeling with boundary element fast multipole method: an efficient MATLAB implementation

Objective

To present and disseminate our transcranial magnetic stimulation (TMS) modeling software toolkit, including several new algorithmic developments, and to apply this software to realistic TMS modeling scenarios given a high-resolution model of the human head including cortical geometry and an accurate coil model.

Approach

The recently developed charge-based boundary element fast multipole method (BEM-FMM) is employed as an alternative to the 1st order finite element method (FEM) most commonly used today. The BEM-FMM approach provides high accuracy and unconstrained numerical field resolution close to and across cortical interfaces. Here, the previously proposed BEM-FMM algorithm has been improved in several novel ways.

Main results

The improvements resulted in a threefold increase in computational speed while maintaining the same solution accuracy. The computational code based on the MATLAB® platform is made available to all interested researchers, along with a coil model repository and examples to create custom coils, head model repository, and supporting documentation. The presented software toolkit may be useful for post-hoc analyses of navigated TMS data using high-resolution subject-specific head models as well as accurate and fast modeling for the purposes of TMS coil/hardware development.

Significance

TMS is currently the only non-invasive neurostimulation modality that enables painless and safe supra-threshold stimulation by employing electromagnetic induction to efficiently penetrate the skull. Accurate, fast, and high resolution modeling of the electric fields may significantly improve individualized targeting and dosing of TMS and therefore enhance the efficiency of existing clinical protocols as well as help establish new application domains.

Neural correlates of visual aesthetic appreciation: insights from non-invasive brain stimulation

During the last decade, non-invasive brain stimulation techniques have been increasingly employed in the field of neuroaesthetics research to shed light on the possible causal role of different brain regions contributing to aesthetic appreciation. Here, I review studies that have employed transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) to investigate neurocognitive mechanisms mediating visual aesthetic appreciation for different stimuli categories (faces, bodies, paintings). The review first considers studies that have assessed the possible causal contribution of cortical regions in mediating aesthetic appreciation along the visual ventral and dorsal pathways (i.e., the extrastriate body area, the motion-sensitive region V5/MT+ , the lateral occipital complex and the posterior parietal cortex). It then considers TMS and tDCS studies that have targeted premotor and motor regions, as well as other areas involved in body and facial expression processing (such as the superior temporal sulcus and the somatosensory cortex) to assess their role in aesthetic evaluation. Finally, it discusses studies that have targeted medial and dorsolateral prefrontal regions leading to significant changes in aesthetic appreciation for both biological stimuli (faces and bodies) and artworks. Possible mechanisms mediating stimulation effects on aesthetic judgments are discussed. A final section considers both methodological limitations of the reviewed studies (including levels of statistical power and the need for further replication) and the future potential for non-invasive brain stimulation to significantly contribute to the understanding of the neural bases of visual aesthetic experiences.

Comparative performance of the finite element method and the boundary element fast multipole method for problems mimicking transcranial magnetic stimulation (TMS)

OBJECTIVE

A study pertinent to the numerical modeling of cortical neurostimulation is conducted in an effort to compare the performance of the finite element method (FEM) and an original formulation of the boundary element fast multipole method (BEM-FMM) at matched computational performance metrics.

APPROACH

We consider two problems: (i) a canonic multi-sphere geometry and an external magnetic-dipole excitation where the analytical solution is available and; (ii) a problem with realistic head models excited by a realistic coil geometry. In the first case, the FEM algorithm tested is a fast open-source getDP solver running within the SimNIBS 2.1.1 environment. In the second case, a high-end commercial FEM software package ANSYS Maxwell 3D is used. The BEM-FMM method runs in the MATLAB^® 2018a environment.

MAIN RESULTS

In the first case, we observe that the BEM-FMM algorithm gives a smaller solution error for all mesh resolutions and runs significantly faster for high-resolution meshes when the number of triangular facets exceeds approximately 0.25 M. We present other relevant simulation results such as volumetric mesh generation times for the FEM, time necessary to compute the potential integrals for the BEM-FMM, and solution performance metrics for different hardware/operating system combinations. In the second case, we observe an excellent agreement for electric field distribution across different cranium compartments and, at the same time, a speed improvement of three orders of magnitude when the BEM-FMM algorithm used.

SIGNIFICANCE

This study may provide a justification for anticipated use of the BEM-FMM algorithm for high-resolution realistic transcranial magnetic stimulation scenarios.