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Intracranial electric field measurements during TES: Identifying determinant factors of the electric field distribution

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Thielscher,  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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Opitz, A., Yeagle, E., Thielscher, A., Schroeder, C., Mehta, A., & Milham, M. (2017). Intracranial electric field measurements during TES: Identifying determinant factors of the electric field distribution. Brain Stimulation, 10(4), e43-e43.


Cite as: http://hdl.handle.net/21.11116/0000-0000-C57C-7
Abstract
Introduction Transcranial electric stimulation (TES) is an increasingly popular method to non-invasively modulate brain function. Recently, we have directly measured the electric field distribution in humans and non-human primates. However, in order to derive practical guidelines it is necessary to identify key factors that determine the electric field during TES in a given individual. Here, based on combined measurements and computational modeling, we identify determinant factors to be accounted for for a reliable application of TES. Methods One pre-surgical refractory epilepsy patient was implanted with subdural grid electrodes. In a single session two saline-soaked sponge electrodes (25cm 2) were attached to the scalp over the left and right temple. A 1Hz alternating current of 1mA was applied for 2 min with 10s ramp up/down. We measured the intracranial field distribution and created realistic FEM models. Results Inclusion of accurate skull and grid modeling had a marked effect on the correlation between simulated and measured electric fields, and only when accounting for both factors did we find high correlations between simulated and measured electric fields. Shifting the stimulation electrode further than 1 cm from its optimal location led to a breakdown of the correlations. Discussion We provide a direct validation of FEM models to predict electric fields in a surgical epilepsy patient. Further, we identified key factors needed for the accurate modeling of electric fields, including the precise representation of skull and ECOG grid. Our findings are a first step towards a reliable application of TES based on validated computational model.