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Electric field calculations explain physiological responses to TMS during motor cortex stimulation

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Bungert,  Andreas
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Antunes,  A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Thielscher,  Axel
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Bungert, A., Antunes, A., & Thielscher, A. (2014). Electric field calculations explain physiological responses to TMS during motor cortex stimulation. Poster presented at 20th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2014), Hamburg, Germany.


Cite as: https://hdl.handle.net/21.11116/0000-0001-32A6-B
Abstract
Introduction: TMS modulates brain activity non-invasively by means of induced electric fields. Calculations of the field distribution using finite element methods (FEM) and realistic head models suggest that the field strength is strongly modulated by the local gyral geometry [Thielscher]. The estimated field strength peaks when it is oriented perpendicular to the targeted gyrus. When stimulating the motor cortex (M1), this coincides with the optimal coil orientation observed in physiological experiments. However, a clear link between physiology and electric fields has not been established yet. Here, we systematically vary the current direction during M1 stimulation to test whether the physiological activation thresholds follow the changes of the electric field induced in the precentral gyrus. Methods: Experiments were performed on 11 healthy subjects. Head models for field calculations were constructed based on high-resolution T1- and T2-weighted MR images using SimNIBS [Opitz, Windhoff]. Additionally, diffusion MR images were used to estimate the conductivity anisotropy in the brain [4]. The T1-weighted images were also used for neuronavigation (Visor, ANT Neuro, Netherlands). MEPs were acquired during pre-activation from the First Dorsal Interosseous (FDI) and the Abductor Digit Minimi (ADM) of the right hand in two separate sessions. Monophasic TMS pulses were applied over left M1 with a standard figure-of-eight coil. The coil was rotated in 9 different directions in 22.5 degree steps from anterior-lateral over anterior-medial to posterior-medial. Input-output (IO) curves were measured and sigmoidal functions were fitted to the curves to robustly estimate the active motor threshold (aMT) for each orientation. For each coil orientation, the corresponding electric field distribution was estimated using SimNIBS, assuming a fixed stimulation intensity of 1 maximal stimulator output. The resulting electric field maps were subsequently converted into MNI-space for group analysis using fnirt (FSL, Oxford). The working hypothesis holds that equal electric field strengths in the hand area of M1 cause muscle response of equal strength, irrespective of coil orientation. Thus, the field strength values estimated for a fixed stimulation intensity should be anti-correlated with aMT changes across orientations: Orientations with high aMT should induce weak fields in the hand area, and vice versa. We numerically assessed this dependence by weighting the field strength in each voxel by the aMT. The mean of this weighted field strength across coil orientations was determined and divided by the standard deviation. Regions in which field strength and aMT are strongly anti-correlated experience high values of the resulting index. Note that this index has no bias towards high electric fields. Since areas far from the target site are unlikely to be affected by TMS, we applied a mask excluding all regions with less than 25 of the maximum of the average electric field. Results: An exemplary IO-curve is shown in Figure 2a. AMT experiences the expected dependence on coil orientation (Figure 2b). Figure 3a shows an exemplary electric field in the brain in MNI-space. Figure 3b shows the averaged field across all subjects and coil orientation, thresholded at 25 of the maximum. The MT-weighted field strengths (group average) experience very high values in the hand knob region of the left precentral gyrus for both fingers (Fig. 4). Conclusions: The experiments confirm that the electric field predictions based on realistic models co-vary with changes in physiological effects. In particular, similar field strengths in the motor areas generate similar MEP-responses, independent of the orientation of the TMS-coil. Next steps include a statistical analysis of the descriptive results presented here. The experiment is one of the first demonstrations that electric field calculations can be used to locate the effective target site of TMS.