Patient-specific solution of the electrocorticography forward problem in deforming brain

TL;DR

This paper presents a patient-specific, biomechanics-based method for accurately modeling the brain's geometry and tissue conductivity after electrode implantation, improving the solution of the electrocorticography forward problem.

Contribution

It introduces a non-rigid registration technique combining preoperative MRI and postoperative CT to account for brain deformation caused by electrode implantation, enabling more accurate FEM-based modeling.

Findings

Significant differences in electric potential predictions between original and deformed brain models.

Improved accuracy in the computed lead field matrix for the forward problem.

Demonstrated the importance of accounting for brain deformation in ECoG modeling.

Abstract

Invasive intracranial electroencephalography (iEEG) or electrocorticography (ECoG) measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling they can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG or ECoG forward problem, which is a crucial prerequisite for solving the inverse problem in epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models.