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.
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.
Objective: Transcranial magnetic stimulation (TMS) induces an electric field (E-field) in the cortex. To facilitate stimulation targeting, image-guided neuronavigation systems have been introduced. Such systems track the placement of the coil with respect to the head and visualize the estimated cortical stimulation location on an anatomical brain image in real time. The accuracy and precision of the neuronavigation is affected by multiple factors. Our aim was to analyze how different factors in TMS neuronavigation affect the accuracy and precision of the coil–head coregistration and the estimated E-field. Approach: By performing simulations, we estimated navigation errors due to distortions in magnetic resonance images (MRIs), head-to-MRI registration (landmark- and surface-based registrations), localization and movement of the head tracker, and localization of the coil tracker. We analyzed the effect of these errors on coil and head coregistration and on the induced E-field as determined with simplistic and realistic head models. Main results: Average total coregistration accuracies were in the range of 2.2–3.6 mm and 1°; precision values were about half of the accuracy values. The coregistration errors were mainly due to head-to-MRI registration with average accuracies 1.5–1.9 mm / 0.2–0.4° and precisions 0.5–0.8 mm / 0.1–0.2° better with surface-based registration. The other major source of error was the movement of the head tracker with average accuracy of 1.5 mm and precision of 1.1 mm. When assessed within an E-field method, the average accuracies of the peak E-field location, orientation, and magnitude ranged between 1.5–5.0 mm, 0.9–4.8°, and 4.4–8.5% across the E-field models studied. The largest errors were obtained with the landmark-based registration. When computing another accuracy measure with the most realistic E-field model as a reference, the accuracies tended to improve from about 10 mm / 15° / 25% to about 2 mm / 2° / 5% when increasing realism of the E-field model. Significance: The results of this comprehensive analysis help TMS operators to recognize the main sources of error in TMS navigation and that the coregistration errors and their effect in the E-field estimation depend on the methods applied. To ensure reliable TMS navigation, we recommend surface-based head-to-MRI registration and realistic models for E-field computations.
Background and Purpose: Despite continuing efforts in the multimodal assessment of the motor system after stroke, conclusive findings on the complementarity of functional and structural metrics of the ipsilesional corticospinal tract integrity and the role of the contralesional hemisphere are still lacking. This research aimed to find the best combination of motor system metrics, allowing the classification of patients into 3 predefined groups of upper limb motor recovery. Methods: We enrolled 35 chronic ischemic stroke patients (mean 47 [26–66] years old, 29 [6–58] months poststroke) with a single supratentorial lesion and unilateral upper extremity weakness. Patients were divided into 3 groups, depending on upper limb motor recovery: good, moderate, and bad. Nonparametric statistical tests and regression analysis were used to investigate the relationships among microstructural (fractional anisotropy (FA) ratio of the corticospinal tracts at the internal capsule (IC) level (classic method) and along the length of the tracts (Fréchet distance), and of the corpus callosum) and functional (motor evoked potentials [MEPs] for 2 hand muscles) motor system metrics. Stratification rules were also tested using a decision tree classifier. Results: IC FA ratio in the IC and MEP absence were both equally discriminative of the bad motor outcome (96% accuracy). For the 3 recovery groups’ classification, the best parameter combination was IC FA ratio and the Fréchet distance between the contralesional and ipsilesional corticospinal tract FA profiles (91% accuracy). No other metrics had any additional value for patients’ classification. MEP presence differed for 2 investigated muscles. Conclusions: This study demonstrates that better separation between 3 motor recovery groups may be achieved when considering the similarity between corticospinal tract FA profiles along its length in addition to region of interest-based assessment and lesion load calculation. Additionally, IC FA ratio and MEP absence are equally important markers for poor recovery, while for MEP probing it may be important to investigate more than one hand muscle.
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