  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.
