Precise motor mapping with robotic TMS versus finger tapping fMRI activation locations

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Abstract Submission 

Zijian Feng¹, Benjamin Kalloch¹, Ole Numssen¹, Gesa Hartwigsen², Jens Haueisen³, Yufeng Zang⁴, Thomas Knösche¹, Konstantin Weise¹

¹Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, ²Leipzig University, Leipzig, Saxony, ³Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Ilmenau, Thuringia, ⁴Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Han, Hangzhou, Zhejiang

Zijian Feng  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony

Benjamin Kalloch  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony

Ole Numssen  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony

Gesa Hartwigsen  
Leipzig University
Leipzig, Saxony

Jens Haueisen  
Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics
Ilmenau, Thuringia

Yufeng Zang  
Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Han
Hangzhou, Zhejiang

Thomas Knösche  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony

Konstantin Weise  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony

Transcranial Magnetic Stimulation (TMS) offers a non-invasive method to stimulate cortical neurons, allowing to map causal structure-function relationships. Traditional TMS mapping methods, using fixed coil orientations and target grids, often struggle with accurately pinpointing neural structures responsible for effects like motor evoked potentials (MEPs) due to the spatial unfocality of the TMS-induced electric field (e-field) (Weise et al., 2023, Numssen et al., 2023). To address this, we recently proposed and validated a novel TMS-mapping approach that increases the precision of TMS mapping by considering the variance of the e-field across multiple coil positions and orientations (Weise et al., 2023, Numssen et al., 2021, Weise et al., 2020). Additionally, robotic TMS approaches have been reported to facilitate improved efficiency, tolerability, and precision in deriving high-fidelity motor maps (Grab et al., 2018).
This study compares TMS-based and functional magnetic resonance imaging (fMRI) based cortical localizations of finger muscle representations in the primary motor. Previous study observed that the fMRI-based activation for a thumb tapping task was positioned more laterally and anteriorly compared to TMS abductor pollicis brevis hotspot (Wang et al., 2020). Here, our objective is to compare the identified cortical position of the first dorsal interosseous (FDI) muscle hotspot, as determined through robotic TMS using our precise localization approach, with the peak activation of index finger tapping in fMRI.

In our robotic TMS study, fifteen right-handed participants (10 females, average age 24.7 ± 1.7 years) were examined. For an overview of the experimental design and the general workflow, refer to Figure 1A from Weise et al. (2023). Before TMS, they underwent T1-weighted, T2-weighted, DWI, and finger tapping fMRI scans. Participants performed an event-related finger tapping task, responding to visual cues with right index finger movements. The TMS-induced electric field (E-field) was computed using a finite element model (FEM) using SimNIBS (Saturnino et al., 2019). Administering 200 single TMS pulses at 170% MT with varied coil positions and orientations yielded robust cortical maps. To identify the cortical origin of the MEPs we utilized nonlinear regression of a log-transformed sigmoidal function of fit the local E-field to the MEP amplitude (Numssen et al., 2021). Optimal parameters for cortical elements were identified using the Levenberg-Marquardt algorithm, with R² values indicating the goodness of fit, illustrated on cortical congruence maps. Figure 1B is an illustrative representation of an example result from an individual participant.

Our findings revealed a notable discrepancy relationship between the fMRI activation and the TMS hotspot in the brain (Figure 1C). A statistically significant difference was noted along the x-axis (t = -2.48, p = 0.026), indicating that the fMRI activation was more laterally positioned relative to the TMS hotspot. No significant differences were noted along the y and z axes. The mean Euclidean distance between the fMRI activation and TMS hotspot was approximately 8.47 millimeters.

Our precise localization revealed that peak fMRI activation during index finger tapping tasks is typically more lateral compared to the FDI hotspot identified by robotic TMS. This highlights the distinction between neural networks activated by passive cortical stimulation and active movement. TMS evokes direct neural activity, while fMRI detects subsequent hemodynamic changes, which might not coincide spatially with the initial activation site. This discrepancy could be attributed to the intricate dynamics of cortical activation, neurovascular coupling, or activation spread in adjacent areas during task performance.

Non-invasive Magnetic/TMS ¹

Non-Invasive Stimulation Methods Other ²

Other - Finger tapping activation; TMS mapping; Hotspot