Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

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’ 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.

Authors:

• Alejandro Omar Blenkmann

• Sabine Liliana Leske

• Anaïs Llorens

• Jack J. Lin

• Edward F. Chang

• Peter Brunner

• Gerwin Schalk

• Jugoslav Ivanovic

• Pål Gunnar Larsson

• Robert Thomas Knight

• Tor Endestad

• Anne-Kristin Solbakk

Date: 2024

DOI: https://doi.org/10.1016/j.jneumeth.2024.110056

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Left hemisphere dominance for bilateral kinematic encoding in the human brain

Abstract:

Neurophysiological studies in humans and nonhuman primates have revealed movement representations in both the contralateral and ipsilateral hemispheres. Inspired by clinical observations, we ask if this bilateral representation differs for the left and right hemispheres. Electrocorticography was recorded in human participants during an instructed-delay reaching task, with movements produced with either the contralateral or ipsilateral arm. Using a cross-validated kinematic encoding model, we found stronger bilateral encoding in the left hemisphere, an effect that was present during preparation and was amplified during execution. Consistent with this asymmetry, we also observed better across-arm generalization in the left hemisphere, indicating similar neural representations for right and left arm movements. Notably, these left hemisphere electrodes were centered over premotor and parietal regions. The more extensive bilateral encoding in the left hemisphere adds a new perspective to the pervasive neuropsychological finding that the left hemisphere plays a dominant role in praxis.

Authors:

• Christina M Merrick

• Tanner C Dixon

• Assaf Breska

• Jack Lin

• Edward F Chang

• David King-Stephens

• Kenneth D Laxer

• Peter B Weber

• Jose Carmena

• Robert Thomas Knight

• Richard B Ivry

Date: 2022

DOI: : https://doi.org/10.7554/eLife.69977

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