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Cortical Surface Electrode Localization Uncertainty

By Chantel M. Charlebois^1,2, Kimia Shayestehfard³, Daria Nesterovich Anderson^1,2, Andrew Janson^1,2, Jeneva Cronin⁴, Moritz Dannhauer², David Caldwell⁴, Sumientra Rempersad³, Larry Sorenson⁴, Jeff Ojemann⁵, Dana Brooks³, Rob MacLeod^1,2, Christopher R. Butson^1,2, Alan Dorval¹

¹Department of Biomedical Engineering; ²Scientific Computing and Imaging (SCI) Institute, University of Utah; ³Department of Electrical & Computer Engineering, Northeastern University; ⁴Department of Bioengineering; ⁵Department of Neurological Surgery, University of Washington

Electrocorticography (ECoG) is an invasive technique commonly used to monitor patients with intractable epilepsy to aid in seizure onset localization and eloquent cortex mapping. Modeling accurate electrode locations is necessary to make predictions about stimulation of seizure focus localization.

• Brain shift occurs after surgical implantation of the ECoG array. When the post-operative CT is co-registered to the pre-operative MRI the electrodes appear to be inside the brain instead of on the cortical surface
• The electrode localization and projection to the cortical surface are based off of thresholding the CT. CT acquisition between patients and centers differs, therefore we want to use a threshold that is insensitive to these differences

Aim: Determine if the CT threshold range affects electrode localization and the resulting simulation of clinical ECoG measurements during stimulation.

We created three finite element meshes with the three different electrode localizations based on their threshold range and solved the bioelectric field problem for bipolar stimulation between electrodes 18 (0.5 mA source) and 23 (-0.5 mA sink), shown in above image. We compared simulations for three different electrode-localizations based on a small, medium, and large CT threshold range to clinical recordings. The three threshold models did not have large voltage differences when simulating clinical stimulation. Moving forward, we can use any of the threshold ranges because they did not greatly differ in their simulation solutions. This insensitivity to the threshold range gives us more confidence in the electrode locations of our models.

Support from a Joint US (NFS) German (DRG) Collaborative Research in Computational Neuroscience grant, IIS-1515168; an NSF CAREER award, 1351112; and an NIH P 41, GM103545, "Center for Integrative Biomedical Computing".

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