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

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

doi:10.1016/j.jneumeth.2024.110056

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

J Neurosci Methods. 2024 Apr:404:110056. doi: 10.1016/j.jneumeth.2024.110056. Epub 2024 Jan 14.

Authors

Affiliations

¹ Department of Psychology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway. Electronic address: [email protected].
² Department of Musicology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway; Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway.
³ Department of Psychology, University of Oslo, Norway; Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA; Université de Franche-Comté, SUPMICROTECH, CNRS, Institut FEMTO-ST, 25000 Besançon, France; Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team TURC, 75014 Paris, France.
⁴ Department of Neurology and Center for Mind and Brain, University of California, Davis, USA.
⁵ Department of Neurological Surgery, University of California, San Francisco, USA.
⁶ Department of Neurology, Albany Medical College, Albany, NY, USA; National Center for Adaptive Neurotechnologies, Albany, NY, USA; Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA.
⁷ Department of Neurology, Albany Medical College, Albany, NY, USA; National Center for Adaptive Neurotechnologies, Albany, NY, USA; Tianqiao and Chrissy Chen Institute, Chen Frontier Lab for Applied Neurotechnology, Shanghai, China; Fudan University/Huashan Hospital, Department of Neurosurgery, Shanghai, China.
⁸ Department of Neurosurgery, Oslo University Hospital, Norway.
⁹ Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA.
¹⁰ Department of Psychology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway; Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway.
¹¹ Department of Psychology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway; Department of Neurosurgery, Oslo University Hospital, Norway; Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway.

PMID: 38224783
DOI: 10.1016/j.jneumeth.2024.110056

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' 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: Depth electrodes; EEG (iEEG); Electrocorticography (ECoG); Intracranial; Simulations; Stereo electroencephalography (SEEG); Subcortical grids; Subdural grids.

Publication types

Review
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Brain / diagnostic imaging
Cerebral Cortex / diagnostic imaging
Electrodes
Electrodes, Implanted
Electroencephalography* / methods
Humans
Magnetic Resonance Imaging* / methods

Abstract

Publication types

MeSH terms

Grants and funding