@article {4561, title = {Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods}, journal = {Journal of Neuroscience Methods}, volume = {404}, year = {2024}, pages = {110056}, author = {Blenkmann, Alejandro Omar and Leske, Sabine Liliana and Llorens, Ana{\"\i}s and Lin, Jack J and Chang, Edward F and Brunner, Peter and Schalk, Gerwin and Ivanovic, Jugoslav and Larsson, P{\r a}l Gunnar and Knight, Robert Thomas and others} } @article {4584, title = {Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.}, journal = {J Neurosci Methods}, volume = {404}, year = {2024}, month = {2024 Apr}, pages = {110056}, abstract = {

BACKGROUND: Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic~algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.

NEW METHODS: We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes{\textquoteright} CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.

RESULTS: We tested GridFit on \~{}6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1~mm errors). Validation with data from 20 challenging patients showed 99\% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with <~0.4~mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.

COMPARISON WITH EXISTING METHODS: GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.

CONCLUSION: GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.

}, keywords = {Brain, Cerebral Cortex, Electrodes, Electrodes, Implanted, Electroencephalography, Humans, Magnetic Resonance Imaging}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2024.110056}, author = {Blenkmann, Alejandro Omar and Leske, Sabine Liliana and Llorens, Ana{\"\i}s and Lin, Jack J and Chang, Edward F and Brunner, Peter and Schalk, Gerwin and Ivanovic, Jugoslav and Larsson, P{\r a}l Gunnar and Knight, Robert Thomas and Endestad, Tor and Solbakk, Anne-Kristin} }