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Blenkmann AO, Leske SL, Llorens A, Lin JJ, Chang EF, Brunner P, Schalk G, Ivanovic J, Larsson PG, Knight RT, Endestad T, Solbakk AK. Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods. J Neurosci Methods 2024; 404:110056. [PMID: 38224783 DOI: 10.1016/j.jneumeth.2024.110056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/27/2023] [Accepted: 01/03/2024] [Indexed: 01/17/2024]
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.
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Affiliation(s)
- Alejandro Omar Blenkmann
- Department of Psychology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway.
| | - Sabine Liliana Leske
- 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
| | - Anaïs Llorens
- 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
| | - Jack J Lin
- Department of Neurology and Center for Mind and Brain, University of California, Davis, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, USA
| | - Peter Brunner
- 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
| | - Gerwin Schalk
- 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
| | | | | | - Robert Thomas Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Tor Endestad
- 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
| | - Anne-Kristin Solbakk
- 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
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Blenkmann AO, Leske SL, Llorens A, Lin JJ, Chang E, Brunner P, Schalk G, Ivanovic J, Larsson PG, Knight RT, Endestad T, Solbakk AK. Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539503. [PMID: 37214984 PMCID: PMC10197594 DOI: 10.1101/2023.05.08.539503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Precise electrode localization is important for maximizing the utility of intracranial EEG data. Electrodes are typically localized from post-implantation CT artifacts, but algorithms can fail due to low signal-to-noise ratio, unrelated artifacts, or high-density electrode arrays. Minimizing these errors usually requires time-consuming visual localization and can still result in inaccurate localizations. In addition, surgical implantation of grids and strips typically introduces non-linear brain deformations, which result in anatomical registration errors when post-implantation CT images are fused with the pre-implantation MRI images. Several projection methods are currently available, but they either fail to produce smooth solutions or do not account for brain deformations. To address these shortcomings, we propose two novel algorithms for the anatomical registration of intracranial electrodes that are almost fully automatic and provide highly accurate results. We first present GridFit, an algorithm that simultaneously localizes all contacts in grids, strips, or depth arrays by fitting flexible models to the electrodes' CT artifacts. We observed localization errors of less than one millimeter (below 8% relative to the inter-electrode distance) and robust performance under the presence of noise, unrelated artifacts, and high-density implants when we ran ~6000 simulated scenarios. Furthermore, we validated the method with real data from 20 intracranial patients. As a second registration step, we introduce CEPA, a brain-shift compensation algorithm that combines orthogonal-based projections, spring-mesh models, and spatial regularization constraints. When tested with real data from 15 patients, anatomical registration errors were smaller than those obtained for well-established alternatives. Additionally, CEPA accounted simultaneously for simple mechanical deformation principles, which is not possible with other available methods. Inter-electrode distances of projected coordinates smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. Moreover, in an additional validation procedure, we found that modeling resting-state high-frequency activity (75-145 Hz ) in five patients further supported our new algorithm. Together, GridFit and CEPA constitute a versatile set of tools for the registration of subdural grid, strip, and depth electrode coordinates that provide highly accurate results even in the most challenging implantation scenarios. The methods presented here are implemented in the iElectrodes open-source toolbox, making their use simple, accessible, and straightforward to integrate with other popular toolboxes used for analyzing electrophysiological data.
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Affiliation(s)
- Alejandro Omar Blenkmann
- Department of Psychology, University of Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway
| | - Sabine Liliana Leske
- Department of Musicology, University of Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway
| | - Anaïs Llorens
- Department of Psychology, University of Oslo, Norway
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Jack J. Lin
- Department of Neurology and Center for Mind and Brain, University of California, Davis, USA
| | - Edward Chang
- Department of Neurological Surgery, University of California, San Francisco, USA
| | - Peter Brunner
- Department of Neurology, Albany Medical College, Albany, NY, USA
- National Center for Adaptive Neurotechnologies, Albany, NY, USA
| | - Gerwin Schalk
- 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
| | | | | | - Robert Thomas Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Tor Endestad
- 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
| | - Anne-Kristin Solbakk
- 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
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Blenkmann AO, Solbakk AK, Ivanovic J, Larsson PG, Knight RT, Endestad T. Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms. Front Neuroinform 2022; 16:788685. [PMID: 36277477 PMCID: PMC9582989 DOI: 10.3389/fninf.2022.788685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance. Results We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods. Conclusion We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.
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Affiliation(s)
- Alejandro O. Blenkmann
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
| | - Anne-Kristin Solbakk
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | | | | | - Robert T. Knight
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Tor Endestad
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
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