Minimum-Norm Estimation of Motor Representations in Navigated TMS Mappings

Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms

Background

There are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously.

Methods

Twelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed.

Results

The projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms).

Conclusion

The association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping.

Optimization of Signal Space Separation for Optically Pumped Magnetometer in Magnetoencephalography

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising.

Selective Stimulus Intensity during Hotspot Search Ensures Faster and More Accurate Preoperative Motor Mapping with nTMS

INTRODUCTION

Navigated transcranial magnetic stimulation (nTMS) has emerged as one of the most innovative techniques in neurosurgical practice. However, nTMS motor mapping involves rigorous steps, and the importance of an accurate execution method has not been emphasized enough. In particular, despite strict adherence to procedural protocols, we have observed high variability in map activation according to the choice of stimulation intensity (SI) right from the early stage of hotspot localization. We present a retrospective analysis of motor mappings performed between March 2020 and July 2022, where the SI was only chosen with rigorous care in the most recent ones, under the guide of an expert neurophysiologist.

MATERIALS AND METHODS

In order to test the ability to reduce inaccurate responses and time expenditure using selective SI, data were collected from 16 patients who underwent mapping with the random method (group A) and 15 patients who underwent mapping with the proposed method (group B). The parameters considered were resting motor threshold (%), number of stimuli, number of valid motor evoked potentials (MEPs), number of valid MEPs considered true positives (TPs), number of valid MEPs considered false positives (FPs), ratio of true-positive MEPs to total stimuli, ratio of true-positive MEPs to valid MEPs, minimum amplitude, maximum amplitude and mapping time for each patient.

RESULTS

The analysis showed statistically significant reductions in total stimulus demand, procedural time and number of false-positive MEPs. Significant increases were observed in the number of true-positive MEPs, the ratio of true-positive MEPs to total stimuli and the ratio of true-positive MEPs to valid MEPs. In the subgroups analyzed, there were similar trends, in particular, an increase in true positives and a decrease in false-positive responses.

CONCLUSIONS

The precise selection of SI during hotspot search in nTMS motor mapping could provide reliable cortical maps in short time and with low employment of resources. This method seems to ensure that a MEP really represents a functionally eloquent cortical point, making mapping more intuitive even in less experienced centers.

TMS Motor Mapping Methodology and Reliability: A Structured Review

Motor cortical representation can be probed non-invasively using a transcranial magnetic stimulation (TMS) technique known as motor mapping. The mapping technique can influence features of the maps because of several controllable elements. Here we review the literature on six key motor mapping parameters, as well as their influence on outcome measures and discuss factors impacting their selection. 132 of 1,587 distinct records were examined in detail and synthesized to form the basis of our review. A summary of mapping parameters, their impact on outcome measures and feasibility considerations are reported to support the design and interpretation of TMS mapping studies.

Mapping of Motor Function with Neuronavigated Transcranial Magnetic Stimulation: A Review on Clinical Application in Brain Tumors and Methods for Ensuring Feasible Accuracy

Navigated transcranial magnetic stimulation (nTMS) has developed into a reliable non-invasive clinical and scientific tool over the past decade. Specifically, it has undergone several validating clinical trials that demonstrated high agreement with intraoperative direct electrical stimulation (DES), which paved the way for increasing application for the purpose of motor mapping in patients harboring motor-eloquent intracranial neoplasms. Based on this clinical use case of the technique, in this article we review the evidence for the feasibility of motor mapping and derived models (risk stratification and prediction, nTMS-based fiber tracking, improvement of clinical outcome, and assessment of functional plasticity), and provide collected sets of evidence for the applicability of quantitative mapping with nTMS. In addition, we provide evidence-based demonstrations on factors that ensure methodological feasibility and accuracy of the motor mapping procedure. We demonstrate that selection of the stimulation intensity (SI) for nTMS and spatial density of stimuli are crucial factors for applying motor mapping accurately, while also demonstrating the effect on the motor maps. We conclude that while the application of nTMS motor mapping has been impressively spread over the past decade, there are still variations in the applied protocols and parameters, which could be optimized for the purpose of reliable quantitative mapping.

Mapping of multiple muscles with transcranial magnetic stimulation: absolute and relative test-retest reliability

The spatial accuracy of transcranial magnetic stimulation (TMS) may be as small as a few millimeters. Despite such great potential, navigated TMS (nTMS) mapping is still underused for the assessment of motor plasticity, particularly in clinical settings. Here, we investigate the within-limb somatotopy gradient as well as absolute and relative reliability of three hand muscle cortical representations (MCRs) using a comprehensive grid-based sulcus-informed nTMS motor mapping. We enrolled 22 young healthy male volunteers. Two nTMS mapping sessions were separated by 5-10 days. Motor evoked potentials were obtained from abductor pollicis brevis (APB), abductor digiti minimi, and extensor digitorum communis. In addition to individual MRI-based analysis, we studied normalized MNI MCRs. For the reliability assessment, we calculated intraclass correlation and the smallest detectable change. Our results revealed a somatotopy gradient reflected by APB MCR having the most lateral location. Reliability analysis showed that the commonly used metrics of MCRs, such as areas, volumes, centers of gravity (COGs), and hotspots had a high relative and low absolute reliability for all three muscles. For within-limb TMS somatotopy, the most common metrics such as the shifts between MCR COGs and hotspots had poor relative reliability. However, overlaps between different muscle MCRs were highly reliable. We, thus, provide novel evidence that inter-muscle MCR interaction can be reliably traced using MCR overlaps while shifts between the COGs and hotspots of different MCRs are not suitable for this purpose. Our results have implications for the interpretation of nTMS motor mapping results in healthy subjects and patients with neurological conditions.

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.

Accuracy of Estimating the Area of Cortical Muscle Representations from TMS Mapping Data Using Voronoi Diagrams

Motor evoked potentials (MEPs) caused by transcranial magnetic stimulation (TMS) provide a possibility of noninvasively mapping cortical muscle representations for clinical and research purposes. The interpretation of such results is complicated by the high variability in MEPs and the lack of a standard optimal mapping protocol. Comparing protocols requires the determination of the accuracy of estimated representation parameters (such as the area), which is problematic without ground truth data. We addressed this problem and obtained two main results: (1) the development of a bootstrapping-based approach for estimating the within-session variability and bias of representation parameters and (2) estimations of the area and amplitude-weighted area accuracies for motor representations using this approach. The method consists in the simulation of TMS mapping results by subsampling MEPs from a single map with a large number of stimuli. We studied the extensor digitorum communis (EDC) and flexor digitorum superficialis (FDS) muscle maps of 15 healthy subjects processed using Voronoi diagrams. We calculated the (decreasing) dependency of the errors in the area and weighted area on the number of stimuli. This result can be used to choose a number of stimuli sufficient for studying the effects of a given size (e.g., the protocol with 150 stimuli leads to relative errors of 7% for the area and 11% for the weighted area in 90% of the maps). The approach is applicable to other parameters (e.g., the center of gravity) and other map processing methods, such as spline interpolation.

Optimization of the Navigated TMS Mapping Algorithm for Accurate Estimation of Cortical Muscle Representation Characteristics

Navigated transcranial magnetic stimulation (nTMS) mapping of cortical muscle representations allows noninvasive assessment of the state of a healthy or diseased motor system, and monitoring changes over time. These applications are hampered by the heterogeneity of existing mapping algorithms and the lack of detailed information about their accuracy. We aimed to find an optimal motor evoked potential (MEP) sampling scheme in the grid-based mapping algorithm in terms of the accuracy of muscle representation parameters. The abductor pollicis brevis (APB) muscles of eight healthy subjects were mapped three times on consecutive days using a seven-by-seven grid with ten stimuli per cell. The effect of the MEP variability on the parameter accuracy was assessed using bootstrapping. The accuracy of representation parameters increased with the number of stimuli without saturation up to at least ten stimuli per cell. The detailed sampling showed that the between-session representation area changes in the absence of interventions were significantly larger than the within-session fluctuations and thus could not be explained solely by the trial-to-trial variability of MEPs. The results demonstrate that the number of stimuli has no universally optimal value and must be chosen by balancing the accuracy requirements with the mapping time constraints in a given problem.

Optimized preoperative motor cortex mapping in brain tumors using advanced processing of transcranial magnetic stimulation data

Background and objective

Transcranial magnetic stimulation (TMS) is a useful technique to help localize motor function prior to neurosurgical procedures. Adequate modelling of the effect of TMS on the brain is a prerequisite to obtain reliable data.

Methods

Twelve patients were included with perirolandic tumors to undergo TMS-based motor mapping. Several models were developed to analyze the mapping data, from a projection to the nearest brain surface to motor evoked potential (MEP) amplitude informed weighted average of the induced electric fields over a multilayer detailed individual head model. The probability maps were compared with direct cortical stimulation (DCS) data in all patients for the hand and in three for the foot. The gold standard was defined as the results of the DCS sampling (with on average 8 DCS-points per surgery) extrapolated over the exposed cortex (of the tailored craniotomy), and the outcome parameters were based on the similarity of the probability maps with this gold standard.

Results

All models accurately gauge the location of the motor cortex, with point-cloud based mapping algorithms having an accuracy of 83–86%, with similarly high specificity. To delineate the whole area of the motor cortex representation, the model based on the weighted average of the induced electric fields calculated with a realistic head model performs best. The optimal single threshold to visualize the field based maps is 40% of the maximal value for the anisotropic model and 50% for the isotropic model, but dynamic thresholding adds information for clinical practice.

Conclusions

The method with which TMS mapping data are analyzed clearly affects the predicted area of the primary motor cortex representation. Realistic electric field based modelling is feasible in clinical practice and improves delineation of the motor cortex representation compared to more simple point-cloud based methods.

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Probability maps of the motor cortex representation were created from a TMS mapping.

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The MEP-weighted averaged tissue specific induced fields based map performed best.

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This map can gauge both motor cortex outline and hotspot, by varying the threshold.

TMSmap - Software for Quantitative Analysis of TMS Mapping Results

The use of the MRI-navigation system ensures accurate targeting of TMS. This, in turn, results in TMS motor mapping becoming a routinely used procedure in neuroscience and neurosurgery. However, currently, there is no standardized methodology for assessment of TMS motor-mapping results. Therefore, we developed TMSmap—free standalone graphical interface software for the quantitative analysis of the TMS motor mapping results (http://tmsmap.ru/). In addition to the estimation of standard parameters (such as the size of cortical muscle representation and the center of gravity location), it allows estimation of the volume of cortical representations, excitability profile of the cortical surface map, and the overlap between cortical representations. The input data for the software includes the coordinates of the coil position (or electric field maximum) and the corresponding response in each stimulation point. TMSmap has been developed for versatile assessment and comparison of TMS maps relating to different experimental interventions including, but not limited to longitudinal, pharmacological and clinical studies (e.g., stroke recovery). To illustrate the use of TMSmap we provide examples of the actual TMS motor-mapping analysis of two healthy subjects and one chronic stroke patient.