Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS

Circuits in the motor cortex explain oscillatory responses to transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a popular method used to investigate brain function. Stimulation over the motor cortex evokes muscle contractions known as motor evoked potentials (MEPs) and also high-frequency volleys of electrical activity measured in the cervical spinal cord. The physiological mechanisms of these experimentally derived responses remain unclear, but it is thought that the connections between circuits of excitatory and inhibitory neurons play a vital role. Using a spiking neural network model of the motor cortex, we explained the generation of waves of activity, so called 'I-waves', following cortical stimulation. The model reproduces a number of experimentally known responses including direction of TMS, increased inhibition, and changes in strength. Using populations of thousands of neurons in a model of cortical circuitry we showed that the cortex generated transient oscillatory responses without any tuning, and that neuron parameters such as refractory period and delays influenced the pattern and timing of those oscillations. By comparing our network with simpler, previously proposed circuits, we explored the contributions of specific connections and found that recurrent inhibitory connections are vital in producing later waves that significantly impact the production of motor evoked potentials in downstream muscles (Thickbroom, 2011). This model builds on previous work to increase our understanding of how complex circuitry of the cortex is involved in the generation of I-waves.

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.

Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging

Repetitive transcranial magnetic stimulation (rTMS) is a tool that can be used to administer treatment for neuropsychiatric disorders such as major depressive disorder, although the clinical efficacy is still rather modest. Overly general stimulation protocols that consider neither patient-specific depression symptomology nor individualized brain characteristics, such as anatomy or structural and functional connections, may be the cause of the high inter- and intraindividual variability in rTMS clinical responses. Multimodal neuroimaging can provide the necessary insights into individual brain characteristics and can therefore be used to personalize rTMS parameters. Optimal coil positioning should include a three-step process: 1) identify the optimal (indirect) target area based on the exact symptom pattern of the patient; 2) derive the cortical (direct) target location based on functional and/or structural connectomes derived from functional and diffusion magnetic resonance imaging data; and 3) determine the ideal coil position by computational modeling, such that the electric field distribution overlaps with the cortical target. These TMS-induced electric field simulations, derived from anatomical and diffusion magnetic resonance imaging data, can be further applied to compute optimal stimulation intensities. In addition to magnetic resonance imaging, electroencephalography can provide complementary information regarding the ongoing brain oscillations. This information can be used to determine the optimal timing and frequency of the stimuli. The heightened benefits of these personalized stimulation approaches are logically reasoned, but speculative. Randomized clinical trials will be required to compare clinical responses from standard rTMS protocols to personalized protocols. Ultimately, an optimized clinical response may result from precision protocols derived from combinations of personalized stimulation parameters.

Influence of Transcranial Electrical Stimulation (TES) waveforms on neural excitability of a realistic axon: a simulation study

Neuromodulation caused by transcranial electrical stimulation (TES) has been used successfully to treat various neuro-degenerative diseases. Simulation models provide an essential tool to study brain and nerve stimulation. Simulation models of TES provide an opportunity to research personalization of therapy without extensive animal and human testing. A computer model of a realistic sensory axon was built by finding actual geometry of the trigeminal nerve through tractography. A finite element model of the head was solved to obtain electric potential distribution caused by TES. Different waveforms were defined to test transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) with varying amplitude and frequency. Neural activity patterns were observed. The strength-duration curve was plotted to verify the model.

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.

Multiscale Computational Model Reveals Nerve Response in a Mouse Model for Temporal Interference Brain Stimulation

There has been a growing interest in the non-invasive stimulation of specific brain tissues, while reducing unintended stimulation in surrounding regions, for the medical treatment of brain disorders. Traditional methods for non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS) or transcranial magnetic stimulation (TMS), can stimulate brain regions, but they also simultaneously stimulate the brain and non-brain regions that lie between the target and the stimulation site of the source. Temporal interference (TI) stimulation has been suggested to selectively stimulate brain regions by superposing two alternating currents with slightly different frequencies injected through electrodes attached to the scalp. Previous studies have reported promising results for TI applied to the motor area in mice, but the mechanisms are yet to be clarified. As computational techniques can help reveal different aspects of TI, in this study, we computationally investigated TI stimulation using a multiscale model that computes the generated interference current pattern effects in a neural cortical model of a mouse head. The results indicated that the threshold increased with the carrier frequency and that the beat frequency did not influence the threshold. It was also found that the intensity ratio between the alternating currents changed the location of the responding nerve, which is in agreement with previous experiments. Moreover, particular characteristics of the envelope were investigated to predict the stimulation region intuitively. It was found that regions with high modulation depth (| maximum| − | minimum| values of the envelope) and low minimum envelope (near zero) corresponded with the activation region obtained via neural computation.

Application of transcranial magnetic stimulation for major depression: Coil design and neuroanatomical variability considerations

High-frequency repeated transcranial magnetic stimulation (rTMS) as a treatment for major depressive disorder (MDD) has received FDA clearance for both the figure-of-8 coil (figure-8 coil) and the H1 coil. The FDA-cleared MDD protocols for both coils include high frequency (10-18 Hz) stimulation targeting the dorsolateral prefrontal cortex (dlPFC) at an intensity that is 120% of the right-hand resting motor threshold. Despite these similar parameters, the two coils generate distinct electrical fields (e-fields) which result in differences in the cortical stimulation they produce. Due to the differences in coil designs, the H1 coil induces a stimulation e-field that is broader and deeper than the one induced by the figure-8 coil. In this paper we review theoretical and clinical implications of these differences between the two coils and compare evidence of their safety and efficacy in treating MDD. We present the design principles of the coils, the challenges of identifying, finding, and stimulating the optimal brain target of each individual (both from functional and connectivity perspectives), and the possible implication of stimulating outside that target. There is only one study that performed a direct comparison between clinical effectiveness of the two coils, using the standard FDA-approved protocols in MDD patients. This study indicated clinical superiority of the H1 coil but did not measure long-term effects. Post-marketing data suggest that both coils have a similar safety profile in clinical practice, whereas effect size comparisons of the two respective FDA pivotal trials suggests that the H1 coil may have an advantage in efficacy. We conclude that further head-to-head experiments are needed, especially ones that will compare long-term effects and usage of similar temporal stimulation parameters and similar number of pulses.

A generalized workflow for conducting electric field-optimized, fMRI-guided, transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a noninvasive method to stimulate the cerebral cortex that has applications in psychiatry, such as in the treatment of depression and anxiety. Although many TMS targeting methods that use figure-8 coils exist, many do not account for individual differences in anatomy or are not generalizable across target sites. This protocol combines functional magnetic resonance imaging (fMRI) and iterative electric-field (E-field) modeling in a generalized approach to subject-specific TMS targeting that is capable of optimizing the stimulation site and TMS coil orientation. To apply this protocol, the user should (i) operationally define a region of interest (ROI), (ii) generate the head model from the structural MRI data, (iii) preprocess the functional MRI data, (iv) identify the single-subject stimulation site within the ROI, and (iv) conduct E-field modeling to identify the optimal coil orientation. In comparison with standard targeting methods, this approach demonstrates (i) reduced variability in the stimulation site across subjects, (ii) reduced scalp-to-cortical-target distance, and (iii) reduced variability in optimal coil orientation. Execution of this protocol requires intermediate-level skills in structural and functional MRI processing. This protocol takes ~24 h to complete and demonstrates how constrained fMRI targeting combined with iterative E-field modeling can be used as a general method to optimize both the TMS coil site and its orientation.

Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD), but its clinical efficacy remains rather modest. One reason for this could be that the propagation of rTMS effects via structural connections from the stimulated area to deeper brain structures (such as the cingulate cortices) is suboptimal.

METHODS

We investigated whether structural connectivity — derived from diffusion MRI data — could serve as a biomarker to predict treatment response. We hypothesized that stronger structural connections between the patient-specific stimulation position in the left dorsolateral prefrontal cortex (dlPFC) and the cingulate cortices would predict better clinical outcomes. We applied accelerated intermittent theta burst stimulation (aiTBS) to the left dlPFC in 40 patients with MDD. We correlated baseline structural connectivity, quantified using various metrics (fractional anisotropy, mean diffusivity, tract density, tract volume and number of tracts), with changes in depression severity scores after aiTBS.

RESULTS

Exploratory results (p < 0.05) showed that structural connectivity between the patient-specific stimulation site and the caudal and posterior parts of the cingulate cortex had predictive potential for clinical response to aiTBS.

LIMITATIONS

We used the diffusion tensor to perform tractography. A main limitation was that multiple fibre directions within voxels could not be resolved, which might have led to missing connections in some patients.

CONCLUSION

Stronger structural frontocingular connections may be of essence to optimally benefit from left dlPFC rTMS treatment in MDD. Even though the results are promising, further investigation with larger numbers of patients, more advanced tractography algorithms and classic daily rTMS treatment paradigms is warranted.

CLINICAL TRIAL REGISTRATION

http://clinicaltrials.gov/show/NCT01832805

Neural effects of transcranial magnetic stimulation at the single-cell level

Transcranial magnetic stimulation (TMS) can non-invasively modulate neural activity in humans. Despite three decades of research, the spatial extent of the cortical area activated by TMS is still controversial. Moreover, how TMS interacts with task-related activity during motor behavior is unknown. Here, we applied single-pulse TMS over macaque parietal cortex while recording single-unit activity at various distances from the center of stimulation during grasping. The spatial extent of TMS-induced activation is remarkably restricted, affecting the spiking activity of single neurons in an area of cortex measuring less than 2 mm in diameter. In task-related neurons, TMS evokes a transient excitation followed by reduced activity, paralleled by a significantly longer grasping time. Furthermore, TMS-induced activity and task-related activity do not summate in single neurons. These results furnish crucial experimental evidence for the neural effects of TMS at the single-cell level and uncover the neural underpinnings of behavioral effects of TMS.

Transcranial Magnetic Stimulation (TMS) can modulate human brain activity, but the extent of the cortical area activated by TMS is unclear. Here, the authors show that TMS affects monkey single neuron activity in an area less than 2 mm diameter, while TMS-induced activity and task-related activity do not summate.

Multi-Scale Computational Models for Electrical Brain Stimulation

Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models that combine a volume conductor head model and multi-compartmental models of cortical neurons have been developed to predict stimulation effects on the macroscopic and microscopic levels more precisely. As the need for better computational models continues to increase, we overview here recent multi-scale modeling studies; we focused on approaches that coupled a simplified or high-resolution volume conductor head model and multi-compartmental models of cortical neurons, and constructed realistic fiber models using diffusion tensor imaging (DTI). Further implications for achieving better precision in estimating cellular responses are discussed.

Adopting reciprocity theorem in deep transcranial magnetic stimulation problem to design an efficient single source coil array based on nerve cell direction

Deep transcranial magnetic stimulation (dTMS) plays an important role in the treatment of many diseases. Previous designs rarely considered the direction of the induced electric field (E) with respect to nerve fibers. However, it can be observed from related formulae that the tangential component of E (E _effective) has a more significant role in the stimulation of nerve cells. In this paper, a new approach is proposed for designing a single-source coil array (CA) by combining tractography and the reciprocity theorem (RT). This method is a non-iterative procedure that can directly design CAs for the stimulation of each desired target zone without any complicated and slow iterative algorithm. Specifications of CA such as the optimum spatial angle and the best placement of coils are important because the location of the coil around the head and its spatial angle have been shown to have a major effect on induced E. Adoption of the RT yields the optimum specifications of CA and maximum E _effective at the stimulation zone. This novel technique can introduce a new approach for the application of CA since it entails a high flexibility, high speed, and good accuracy.

How much detail is needed in modeling a transcranial magnetic stimulation figure-8 coil: Measurements and brain simulations

Background

Despite TMS wide adoption, its spatial and temporal patterns of neuronal effects are not well understood. Although progress has been made in predicting induced currents in the brain using realistic finite element models (FEM), there is little consensus on how a magnetic field of a typical TMS coil should be modeled. Empirical validation of such models is limited and subject to several limitations.

Methods

We evaluate and empirically validate models of a figure-of-eight TMS coil that are commonly used in published modeling studies, of increasing complexity: simple circular coil model; coil with in-plane spiral winding turns; and finally one with stacked spiral winding turns. We will assess the electric fields induced by all 3 coil models in the motor cortex using a computer FEM model. Biot-Savart models of discretized wires were used to approximate the 3 coil models of increasing complexity. We use a tailored MR based phase mapping technique to get a full 3D validation of the incident magnetic field induced in a cylindrical phantom by our TMS coil. FEM based simulations on a meshed 3D brain model consisting of five tissues types were performed, using two orthogonal coil orientations.

Results

Substantial differences in the induced currents are observed, both theoretically and empirically, between highly idealized coils and coils with correctly modeled spiral winding turns. Thickness of the coil winding turns affect minimally the induced electric field, and it does not influence the predicted activation.

Conclusion

TMS coil models used in FEM simulations should include in-plane coil geometry in order to make reliable predictions of the incident field. Modeling the in-plane coil geometry is important to correctly simulate the induced electric field and to correctly make reliable predictions of neuronal activation

The development and modelling of devices and paradigms for transcranial magnetic stimulation

Magnetic stimulation is a non-invasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain, as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modelling.

Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study

Transcranial magnetic stimulation (TMS) in combination with neuroimaging techniques allows to measure the effects of a direct perturbation of the brain. When coupled with high-density electroencephalography (TMS/hd-EEG), TMS pulses revealed electrophysiological signatures of different cortical modules in health and disease. However, the neural underpinnings of these signatures remain unclear. Here, by applying multimodal analyses of cortical response to TMS recordings and diffusion magnetic resonance imaging (dMRI) tractography, we investigated the relationship between functional and structural features of different cortical modules in a cohort of awake healthy volunteers. For each subject, we computed directed functional connectivity interactions between cortical areas from the source-reconstructed TMS/hd-EEG recordings and correlated them with the correspondent structural connectivity matrix extracted from dMRI tractography, in three different frequency bands (α, β, γ) and two sites of stimulation (left precuneus and left premotor). Each stimulated area appeared to mainly respond to TMS by being functionally elicited in specific frequency bands, that is, β for precuneus and γ for premotor. We also observed a temporary decrease in the whole-brain correlation between directed functional connectivity and structural connectivity after TMS in all frequency bands. Notably, when focusing on the stimulated areas only, we found that the structure-function correlation significantly increases over time in the premotor area controlateral to TMS. Our study points out the importance of taking into account the major role played by different cortical oscillations when investigating the mechanisms for integration and segregation of information in the human brain.

Technical aspects of neurostimulation: Focus on equipment, electric field modeling, and stimulation protocols

Neuromodulation is a field of science, medicine, and bioengineering that encompasses implantable and non-implantable technologies for the purpose of improving quality of life and functioning of humans. Brain neuromodulation involves different neurostimulation techniques: transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), which are being used both to study their effects on cognitive brain functions and to treat neuropsychiatric disorders. The mechanisms of action of neurostimulation remain incompletely understood. Insight into the technical basis of neurostimulation might be a first step towards a more profound understanding of these mechanisms, which might lead to improved clinical outcome and therapeutic potential. This review provides an overview of the technical basis of neurostimulation focusing on the equipment, the present understanding of induced electric fields, and the stimulation protocols. The review is written from a technical perspective aimed at supporting the use of neurostimulation in clinical practice.

Modeling transcranial magnetic stimulation from the induced electric fields to the membrane potentials along tractography-based white matter fiber tracts

OBJECTIVE

Transcranial magnetic stimulation (TMS) is a promising non-invasive tool for modulating the brain activity. Despite the widespread therapeutic and diagnostic use of TMS in neurology and psychiatry, its observed response remains hard to predict, limiting its further development and applications. Although the stimulation intensity is always maximum at the cortical surface near the coil, experiments reveal that TMS can affect deeper brain regions as well.

APPROACH

The explanation of this spread might be found in the white matter fiber tracts, connecting cortical and subcortical structures. When applying an electric field on neurons, their membrane potential is altered. If this change is significant, more likely near the TMS coil, action potentials might be initiated and propagated along the fiber tracts towards deeper regions. In order to understand and apply TMS more effectively, it is important to capture and account for this interaction as accurately as possible. Therefore, we compute, next to the induced electric fields in the brain, the spatial distribution of the membrane potentials along the fiber tracts and its temporal dynamics.

MAIN RESULTS

This paper introduces a computational TMS model in which electromagnetism and neurophysiology are combined. Realistic geometry and tissue anisotropy are included using magnetic resonance imaging and targeted white matter fiber tracts are traced using tractography based on diffusion tensor imaging. The position and orientation of the coil can directly be retrieved from the neuronavigation system. Incorporating these features warrants both patient- and case-specific results.

SIGNIFICANCE

The presented model gives insight in the activity propagation through the brain and can therefore explain the observed clinical responses to TMS and their inter- and/or intra-subject variability. We aspire to advance towards an accurate, flexible and personalized TMS model that helps to understand stimulation in the connected brain and to target more focused and deeper brain regions.

Dissociable Roles of Dorsolateral Prefrontal Cortex and Frontal Eye Fields During Saccadic Eye Movements

The dorsolateral prefrontal cortex (DLPFC) and the frontal eye fields (FEF) have both been implicated in the executive control of saccades, yet possible dissociable roles of each region have not been established. Specifically, both establishing a “task set” as well as suppressing an inappropriate response have been linked to DLPFC and FEF activity, with behavioral outcome measures of these mechanisms mainly being the percentage of pro-saccade errors made on anti-saccade trials. We used continuous theta-burst stimulation (cTBS) to disrupt FEF or DLPFC function in humans during an anti-saccade task to assess the causal role of these regions in these executive control processes, and in programming saccades towards (pro-saccade) or away (anti-saccade) from visual targets. After right FEF cTBS, as compared to control cTBS to the right primary somatosensory cortex (rS1), anti-saccade amplitude of the first saccade decreased and the number of anti-saccades to acquire final position increased; however direction errors to the visual target were not different. In contrast, after left DLPFC cTBS, as compared to left S1 cTBS, subjects displayed greater direction errors for contralateral anti-saccades; however, there were no impairments on the number of saccades or the saccade amplitude. These results are consistent with the notion that DLPFC is necessary for executive control of saccades, whereas FEF is necessary for visuo-motor aspects of anti-saccade programming.