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Journal article · Conference paper

Applying electric field modeling to TMS motor mapping

From

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

Max Planck Institute for Biological Cybernetics, Max Planck Society

Realistic field calculations in transcranial neurostimulation promise a better insight into the position and extent of the affected brain areas and improve the spatial specificity of stimulation. This is underlined by recent work that demonstrated a strong influence of individual gyral geometry on the strength and distribution of the induced electric field [1]. The field calculations are based on fundamental laws of electrodynamics and rely on conductivity model of individual heads, which are based on segmented structural MR images.

For the wider application and the general acceptance of electric field modeling for neurostimulation, two steps seem essential: – a demonstration that the simulated fields correlate with observable effects of neurostimulation (e.g. behavioral or electrophysiological). That is, the simulated fields contribute accurate and relevant information to the experiments; integration electric field modeling into regular TMS experiments in a user-friendly way. Methods and results.– We demonstrate the integration of the Simulation for Non-Invasive Brain Stimulation (SimNIBS, www.simnibs.de) software package with neuronavigation tools for TMS (VISOR from ANT).

The coil position and orientation for each TMS pulse is saved by VISOR. These coil positions are automatically converted into the SimNIBS format and used to carry out electric field simulations for each coil position. First results from TMS-motor mapping show how the simulated electric fields can be correlated with the motor evoked potentials of individual muscles. Conclusion.– These results demonstrate that advanced electric field simulations can be applied routinely in experiments involving TMS.

In addition, the application to TMS-motor mapping allows validating these simulations in a brain system that is well characterized.

Language: English
Year: 2012
Pages: 78-79
ISSN: 17697131 and 09877053
Types: Journal article and Conference paper
DOI: 10.1016/j.neucli.2012.11.033

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