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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.

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 motor evoked potentials. Besides insights into structure-function relationships, this lays the foundation for TMS dosing metrics based on subject-specific cortical field thresholds. Motor evoked potentials (MEPs) for different coil positions/orientations are combined with electric field modelling. Compared to previous approaches this routine exploits the causal nature of neuronal activation functions to pinpoint MEPs to their cortical origin.After constructing an individual head model, MEPs are recorded, and the induced electric fields computed. Finally, the cortical muscle representations are determined by relating the induced cortical fields to MEP amplitudes. Subsequently, the coil position which optimally stimulates this cortical position can be identified.The protocol requires 2 hours of manual preparation and 10 hours for the automatized head model construction, one TMS session (2h), 12h of computational post-processing, and an optional second TMS session (0.5h). A basic level of computer science expertise and standard TMS neuronavigation equipment suffices.

Decision-making is increasingly being recognised to play a role in learning motor skills. Understanding the neural processes regulating motor decision-making is therefore essential to identify mechanisms that contribute to motor skill learning. In decision-making tasks, the frontopolar cortex (FPC) is involved in tracking the reward of different alternative choices, as well as their reliability. Whether this FPC function extends to reward landscapes associated with a continuous movement dimension remains unknown. Here we used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants completed a motor sequence learning task using trial-wise reward feedback to discover a hidden performance goal along a continuous dimension: timing. As a control condition, we modulated contralateral motor cortex (left M1) activity with tDCS, which has been shown to benefit motor skill learning but less consistently reward-based motor learning. Each active tDCS condition was contrasted to sham stimulation. Right FPC-tDCS led to faster learning primarily through a regulation of exploration, without concurrent modulation of motor noise. A Bayesian computational model revealed that following rFPC-tDCS, participants had a higher expectation of reward, consistent with their faster learning. These higher reward estimates were inferred to be less volatile, and thus participants under rFPC-tDCS deemed the mapping between movement and reward to be more stable. Relative to sham, lM1-tDCS did not significantly modulate main behavioral outcomes. The results indicate that brain regions previously linked to decision-making, such as the FPC, are relevant for motor skill learning.

Simulating transcranial electric stimulation is actively researched as knowledge about the distribution of the electrical field is decisive for understanding the variability in the elicited stimulation effect. Several software pipelines comprehensively solve this task in an automated manner for standard use-cases. However, simulations for non-standard applications such as uncommon electrode shapes or the creation of head models from non-optimized T1-weighted imaging data and the inclusion of irregular structures are more difficult to accomplish.We address these limitations and suggest a comprehensive workflow to simulate transcranial electric stimulation based on open-source tools. The workflow covers the head model creation from MRI data, the electrode modeling, the modeling of anisotropic conductivity behavior of the white matter, the numerical simulation and visualization.Skin, skull, air cavities, cerebrospinal fluid, white matter, and gray matter are segmented semi-automatically from T1-weighted MR images. Electrodes of arbitrary number and shape can be modeled. The meshing of the head model is implemented in a way to preserve feature edges of the electrodes and is free of topological restrictions of the considered structures of the head model. White matter anisotropy can be computed from diffusion-tensor imaging data.Our solver application was verified analytically and by contrasting tDCS simulation results with another simulation pipeline (SimNIBS 3.0). An agreement in both cases underlines the validity of our workflow.Our suggested solutions facilitate investigations of irregular structures in patients (e.g. lesions, implants) or of new electrode types. For a coupled use of the described workflow, we provide documentation and disclose the full source code of the developed tools.

The interaction of electromagnetic (EM) fields with the human body during magnetic resonance imaging (MRI) is complex and subject specific. MRI radiofrequency (RF) coil performance and safety assessment typically includes numerical EM simulations with a set of human body models. The dimensions of mesh elements used for discretization of the EM simulation domain must be adequate for correct representation of the MRI coil elements, different types of human tissue, and wires and electrodes of additional devices. Examples of such devices include those used during electroencephalography, transcranial magnetic stimulation, and transcranial direct current stimulation, which record complementary information or manipulate brain states during MRI measurement. The electrical contact within and between tissues, as well as between an electrode and the skin, must also be preserved. These requirements can be fulfilled with anatomically correct surface-based human models and EM solvers based on unstructured meshes. Here, we report (i) our workflow used to generate the surface meshes of a head and torso model from the segmented AustinMan dataset, (ii) head and torso model mesh optimization for three-dimensional EM simulation in ANSYS HFSS, and (iii) several case studies of MRI RF coil performance and safety assessment.

Purpose: Simulating the interaction of the human body with electromagnetic fields is an active field of research. Individualized models are increasingly being used, as anatomical differences affect the simulation results. We introduce a processing pipeline for creating individual surface-based models of the human head and torso for application in simulation software based on unstructured grids. The pipeline is designed for easy applicability and is publicly released on figshare. Methods: The pipeline covers image acquisition, segmentation, generation of segmentation masks, and surface mesh generation of the single, external boundary of each structure of interest. Two gradient-echo sequences are used for image acquisition. Structures of the head and body are segmented using several atlas-based approaches. They consist of bone/skull, subarachnoid cerebrospinal fluid, gray matter, white matter, spinal cord, lungs, the sinuses of the skull, and a combined class of all other structures including skin. After minor manual preparation, segmentation images are processed to segmentation masks, which are binarized images per segmented structure free of misclassified voxels and without an internal boundary. The proposed workflow is applied to 2 healthy subjects. Results: Individual differences of the subjects are well represented. The models are proven to be suitable for simulation of the RF electromagnetic field distribution. Conclusion: Image segmentation, creation of segmentation masks, and surface mesh generation are highly automated. Manual interventions remain for preparing the segmentation images prior to segmentation mask generation. The generated surfaces exhibit a single boundary per structure and are suitable inputs for simulation software.

We built four prototypes of high resolution surface-based human head models that can be simulated in a reasonable time by a commercially available frequency domain solver — ANSYS HFSS — and evaluated the influence of cerebrospinal fluid and of the upper part of the human torso on field propagation estimates of traveling wave excitation at 297.2 MHz. Combining neighboring triangular faces located in the same plane into a single one is an approach that achieves simulations of high-resolution human models previously not accessible to tetrahedral-mesh-based solvers. If electrical contact between anatomically connected parts of CSF is correctly considered, CSF was found to partially shield brain tissues from the incident RF field.