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Del Vecchio M, Bontemps B, Lance F, Gannerie A, Sipp F, Albertini D, Cassani CM, Chatard B, Dupin M, Lachaux JP. Introducing HiBoP: a Unity-based visualization software for large iEEG datasets. J Neurosci Methods 2024; 409:110179. [PMID: 38823595 DOI: 10.1016/j.jneumeth.2024.110179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/02/2024] [Accepted: 05/22/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Intracranial EEG data offer a unique spatio-temporal precision to investigate human brain functions. Large datasets have become recently accessible thanks to new iEEG data-sharing practices and tighter collaboration with clinicians. Yet, the complexity of such datasets poses new challenges, especially regarding the visualization and anatomical display of iEEG. NEW METHOD We introduce HiBoP, a multi-modal visualization software specifically designed for large groups of patients and multiple experiments. Its main features include the dynamic display of iEEG responses induced by tasks/stimulations, the definition of Regions and electrodes Of Interest, and the shift between group-level and individual-level 3D anatomo-functional data. RESULTS We provide a use-case with data from 36 patients to reveal the global cortical dynamics following tactile stimulation. We used HiBoP to visualize high-gamma responses [50-150 Hz], and define three major response components in primary somatosensory and premotor cortices and parietal operculum. COMPARISON WITH EXISTING METHODS(S) Several iEEG softwares are now publicly available with outstanding analysis features. Yet, most were developed in languages (Python/Matlab) chosen to facilitate the inclusion of new analysis by users, rather than the quality of the visualization. HiBoP represents a visualization tool developed with videogame standards (Unity/C#), and performs detailed anatomical analysis rapidly, across multiple conditions, patients, and modalities with an easy export toward third-party softwares. CONCLUSION HiBoP provides a user-friendly environment that greatly facilitates the exploration of large iEEG datasets, and helps users decipher subtle structure/function relationships.
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Affiliation(s)
- Maria Del Vecchio
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma 43125, Italy
| | - Benjamin Bontemps
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Florian Lance
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Adrien Gannerie
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Florian Sipp
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Davide Albertini
- Dipartimento di Medicina e Chirurgia, Università di Parma, Via Volturno 39, Parma 43125, Italy
| | - Chiara Maria Cassani
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma 43125, Italy; Department of School of Advanced Studies, University of Camerino, Italy
| | - Benoit Chatard
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Maryne Dupin
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Jean-Philippe Lachaux
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France.
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Pigorini A, Avanzini P, Barborica A, Bénar CG, David O, Farisco M, Keller CJ, Manfridi A, Mikulan E, Paulk AC, Roehri N, Subramanian A, Vulliémoz S, Zelmann R. Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity. J Neurosci Methods 2024; 408:110160. [PMID: 38734149 DOI: 10.1016/j.jneumeth.2024.110160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/10/2024] [Accepted: 05/01/2024] [Indexed: 05/13/2024]
Abstract
Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.
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Affiliation(s)
- Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy; UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy.
| | - Pietro Avanzini
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy
| | | | - Christian-G Bénar
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Olivier David
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Michele Farisco
- Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, P.O. Box 256, Uppsala, SE 751 05, Sweden; Science and Society Unit Biogem, Biology and Molecular Genetics Institute, Via Camporeale snc, Ariano Irpino, AV 83031, Italy
| | - Corey J Keller
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Alfredo Manfridi
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| | - Angelique C Paulk
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicolas Roehri
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Ajay Subramanian
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Rina Zelmann
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Monney J, Dallaire SE, Stoutah L, Fanda L, Mégevand P. Voxeloc: A time-saving graphical user interface for localizing and visualizing stereo-EEG electrodes. J Neurosci Methods 2024; 407:110154. [PMID: 38697518 DOI: 10.1016/j.jneumeth.2024.110154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/26/2024] [Accepted: 04/27/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND Thanks to its unrivalled spatial and temporal resolutions and signal-to-noise ratio, intracranial EEG (iEEG) is becoming a valuable tool in neuroscience research. To attribute functional properties to cortical tissue, it is paramount to be able to determine precisely the localization of each electrode with respect to a patient's brain anatomy. Several software packages or pipelines offer the possibility to localize manually or semi-automatically iEEG electrodes. However, their reliability and ease of use may leave to be desired. NEW METHOD Voxeloc (voxel electrode locator) is a Matlab-based graphical user interface to localize and visualize stereo-EEG electrodes. Voxeloc adopts a semi-automated approach to determine the coordinates of each electrode contact, the user only needing to indicate the deep-most contact of each electrode shaft and another point more proximally. RESULTS With a deliberately streamlined functionality and intuitive graphical user interface, the main advantages of Voxeloc are ease of use and inter-user reliability. Additionally, oblique slices along the shaft of each electrode can be generated to facilitate the precise localization of each contact. Voxeloc is open-source software and is compatible with the open iEEG-BIDS (Brain Imaging Data Structure) format. COMPARISON WITH EXISTING METHODS Localizing full patients' iEEG implants was faster using Voxeloc than two comparable software packages, and the inter-user agreement was better. CONCLUSIONS Voxeloc offers an easy-to-use and reliable tool to localize and visualize stereo-EEG electrodes. This will contribute to democratizing neuroscience research using iEEG.
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Affiliation(s)
- Jonathan Monney
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Shannon E Dallaire
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Dalhousie University, Halifax, Canada
| | - Lydia Stoutah
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Université Paris-Saclay, Paris, France
| | - Lora Fanda
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pierre Mégevand
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Neurology division, Geneva University Hospitals, Geneva, Switzerland.
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Gavina T, Gutiérrez-Lafrentz L. Between Collection and Interpretation: Targeted Rights for Unpredictable Insights. AJOB Neurosci 2024; 15:142-144. [PMID: 38568701 DOI: 10.1080/21507740.2024.2328243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
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5
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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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6
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Li G, Jiang S, Meng J, Wu Z, Jiang H, Fan Z, Hu J, Sheng X, Zhang D, Schalk G, Chen L, Zhu X. Spatio-temporal evolution of human neural activity during visually cued hand movements. Cereb Cortex 2023; 33:9764-9777. [PMID: 37464883 DOI: 10.1093/cercor/bhad242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/20/2023] Open
Abstract
Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.
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Affiliation(s)
- Guangye Li
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jianjun Meng
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zehan Wu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Haiteng Jiang
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China
- MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310058, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xinjun Sheng
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
| | - Gerwin Schalk
- Chen Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, Shanghai 200052, China
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xiangyang Zhu
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
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Lucas A, Scheid BH, Pattnaik AR, Gallagher R, Mojena M, Tranquille A, Prager B, Gleichgerrcht E, Gong R, Litt B, Davis KA, Das S, Stein JM, Sinha N. iEEG-recon: A Fast and Scalable Pipeline for Accurate Reconstruction of Intracranial Electrodes and Implantable Devices. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.12.23291286. [PMID: 37398160 PMCID: PMC10312891 DOI: 10.1101/2023.06.12.23291286] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Background Collaboration between epilepsy centers is essential to integrate multimodal data for epilepsy research. Scalable tools for rapid and reproducible data analysis facilitate multicenter data integration and harmonization. Clinicians use intracranial EEG (iEEG) in conjunction with non-invasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. These tasks are still performed manually in many epilepsy centers. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. Methods We created iEEG-recon, a scalable electrode reconstruction pipeline for semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three modules: a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon, and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. Results We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography (ECoG) and stereoelectroencephalography (SEEG) cases with a 10 minute running time per case, and ~20 min for semi-automatic electrode labeling. iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and post-implant T1-MRI visual inspections. Our use of ANTsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely used Freesurfer segmentation. Discussion iEEG-recon is a valuable tool for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting efficient data analysis, and integration into clinical workflows. The tool's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io/en/latest/.
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Affiliation(s)
- Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Brittany H. Scheid
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Akash R. Pattnaik
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Ashley Tranquille
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Brian Prager
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Ezequiel Gleichgerrcht
- Department of Neurology, Medical University of South Carolina, Charleston, SC
- Emory University, Atlanta, GA
| | | | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Sandhitsu Das
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Joel M. Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
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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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Wang A, Fan Z, Zhang Y, Wang J, Zhang X, Wang P, Mu W, Zhan G, Wang M, Zhang L, Gan Z, Kang X. Resting-state SEEG-based brain network analysis for the detection of epileptic area. J Neurosci Methods 2023; 390:109839. [PMID: 36933706 DOI: 10.1016/j.jneumeth.2023.109839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions. NEW METHOD The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes. RESULTS By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p < 0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 ± 0.08 for the epilepsy zone. CONCLUSIONS AND SIGNIFICANCE The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.
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Affiliation(s)
- Aiping Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Yuan Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Xueze Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Pengchao Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Wei Mu
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Gege Zhan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Minjie Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Lihua Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China
| | - Zhongxue Gan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China
| | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Yiwu Research Institute of Fudan University, Chengbei Road, Yiwu City, 322000 Zhejiang, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China; Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, China.
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10
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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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11
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Wu X, Li G, Jiang S, Wellington S, Liu S, Wu Z, Metcalfe B, Chen L, Zhang D. Decoding Continuous Kinetic Information of Grasp from Stereo-electroencephalographic (SEEG) Recordings. J Neural Eng 2022; 19. [PMID: 35395645 DOI: 10.1088/1741-2552/ac65b1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 04/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates. APPROACH Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares (PLS) model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network). MAIN RESULTS The current study showed that: 1) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization (ERS)) were sustained during prolonged force holding periods; 2) continuously changing grasp force can be decoded from the SEEG signals; 3) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates. SIGNIFICANCE This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.
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Affiliation(s)
- Xiaolong Wu
- Electric, Electronic and Engineering, University of Bath, Pulteney Court PD42.2,Pulteney Road,BA2 4HL, Bath, Bath, Somerset, BA2 4HL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Guangye Li
- Shanghai Jiao Tong University, Shanghai JiaoTong university, Shanghai, China, Shanghai, 200240, CHINA
| | - Shize Jiang
- Fudan University Huashan Hospital Department of Neurosurgery, Fudan University Huanshan hospital, Shanghai, Shanghai, 201906, CHINA
| | - Scott Wellington
- University of Bath, University of Bath, Bath, UK, Bath, Bath and North East Somer, BA2 7AY, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Shengjie Liu
- Shanghai Jiao Tong University, Shanghai jIaotong University, Shanghai, China, Shanghai, 200240, CHINA
| | - Zehan Wu
- Huashan Hospital Fudan University, Huashan hospital, Shanghai, China, Shanghai, Shanghai, 200040, CHINA
| | - Benjamin Metcalfe
- University of Bath, University of Bath, UK, Bath, Bath and North East Somer, BA2 7AY, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Liang Chen
- Fudan University Huashan Hospital Department of Neurosurgery, Fudan University Huashan Hospital, Shanghai, China, Shanghai, Shanghai, 201906, CHINA
| | - Dingguo Zhang
- University of Bath, University of Bath, UK, Bath, Somerset, BA2 4HL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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12
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Ye H, Fan Z, Li G, Wu Z, Hu J, Sheng X, Chen L, Zhu X. Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces. Front Neurosci 2022; 16:818214. [PMID: 35368269 PMCID: PMC8968069 DOI: 10.3389/fnins.2022.818214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/15/2022] [Indexed: 11/23/2022] Open
Abstract
As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60–140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.
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Affiliation(s)
- Huanpeng Ye
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Guangye Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zehan Wu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Liang Chen
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Xiangyang Zhu
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13
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Li G, Jiang S, Meng J, Chai G, Wu Z, Fan Z, Hu J, Sheng X, Zhang D, Chen L, Zhu X. Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings. Neuroimage 2022; 250:118969. [DOI: 10.1016/j.neuroimage.2022.118969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 01/28/2022] [Accepted: 02/02/2022] [Indexed: 01/03/2023] Open
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14
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Cai F, Wang K, Zhao T, Wang H, Zhou W, Hong B. BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis. Front Neuroinform 2022; 15:773890. [PMID: 35069168 PMCID: PMC8782204 DOI: 10.3389/fninf.2021.773890] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high spatial resolution within suspected epileptogenic zones. Neurosurgeons or technicians face the challenge of conducting a workflow of post-processing operations with the multimodal data (e.g., MRI, CT, and EEG) after the implantation surgery, such as brain surface reconstruction, electrode contact localization, and SEEG data analysis. Several software or toolboxes have been developed to take one or more steps in the workflow but without an end-to-end solution. In this study, we introduced BrainQuake, an open-source Python software for the SEEG spatiotemporal analysis, integrating modules and pipelines in surface reconstruction, electrode localization, seizure onset zone (SOZ) prediction based on ictal and interictal SEEG analysis, and final visualizations, each of which is highly automated with a user-friendly graphical user interface (GUI). BrainQuake also supports remote communications with a public server, which is facilitated with automated and standardized preprocessing pipelines, high-performance computing power, and data curation management to provide a time-saving and compatible platform for neurosurgeons and researchers.
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Affiliation(s)
- Fang Cai
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Kang Wang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Tong Zhao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Haixiang Wang
- Epilepsy Center, Yuquan Hospital, Tsinghua University, Beijing, China
| | - Wenjing Zhou
- Epilepsy Center, Yuquan Hospital, Tsinghua University, Beijing, China
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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15
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Davis TS, Caston RM, Philip B, Charlebois CM, Anderson DN, Weaver KE, Smith EH, Rolston JD. LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes. Front Neurosci 2021; 15:769872. [PMID: 34955721 PMCID: PMC8695687 DOI: 10.3389/fnins.2021.769872] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/18/2021] [Indexed: 11/24/2022] Open
Abstract
Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.
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Affiliation(s)
- Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States
| | - Rose M Caston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Brian Philip
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States.,Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, United States
| | - Kurt E Weaver
- Department of Radiology, University of Washington, Seattle, WA, United States.,Department of Biological Structure, University of Washington, Seattle, WA, United States
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States.,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
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16
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Liu S, Li G, Jiang S, Wu X, Hu J, Zhang D, Chen L. Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography. Front Neurosci 2021; 15:725384. [PMID: 34690673 PMCID: PMC8528199 DOI: 10.3389/fnins.2021.725384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/01/2021] [Indexed: 11/13/2022] Open
Abstract
Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain–computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray–white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.
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Affiliation(s)
- Shengjie Liu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Guangye Li
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaolong Wu
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
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17
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Li G, Jiang S, Paraskevopoulou SE, Chai G, Wei Z, Liu S, Wang M, Xu Y, Fan Z, Wu Z, Chen L, Zhang D, Zhu X. Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG). J Neural Eng 2021; 18. [PMID: 34284361 DOI: 10.1088/1741-2552/ac160e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 07/20/2021] [Indexed: 11/11/2022]
Abstract
Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.
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Affiliation(s)
- Guangye Li
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Sivylla E Paraskevopoulou
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Guohong Chai
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zixuan Wei
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Shengjie Liu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Meng Wang
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yang Xu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Zehan Wu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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18
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Parker JJ, Jamiolkowski RM, Grant GA, Le S, Halpern CH. Hybrid Fluoroscopic and Neurophysiological Targeting of Responsive Neurostimulation of the Rolandic Cortex. Oper Neurosurg (Hagerstown) 2021; 21:E180-E186. [PMID: 34133746 DOI: 10.1093/ons/opab182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 04/04/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Precise targeting of cortical surface electrodes to epileptogenic regions defined by anatomic and electrophysiological guideposts remains a surgical challenge during implantation of responsive neurostimulation (RNS) devices. OBJECTIVE To describe a hybrid fluoroscopic and neurophysiological technique for targeting of subdural cortical surface electrodes to anatomic regions with limited direct visualization, such as the interhemispheric fissure. METHODS Intraoperative two-dimensional (2D) fluoroscopy was used to colocalize and align an electrode for permanent device implantation with a temporary in Situ electrode placed for extraoperative seizure mapping. Intraoperative phase reversal mapping technique was performed to distinguish primary somatosensory and motor cortex. RESULTS We applied these techniques to optimize placement of an interhemispheric strip electrode connected to a responsive neurostimulator system for detection and treatment of seizures arising from a large perirolandic cortical malformation. Intraoperative neuromonitoring (IONM) phase reversal technique facilitated neuroanatomic mapping and electrode placement. CONCLUSION In challenging-to-access anatomic regions, fluoroscopy and intraoperative neurophysiology can be employed to augment targeting of neuromodulation electrodes to the site of seizure onset zone or specific neurophysiological biomarkers of clinical interest while minimizing brain retraction.
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Affiliation(s)
- Jonathon J Parker
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Ryan M Jamiolkowski
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Gerald A Grant
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Scheherazade Le
- Department of Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
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Ye H, Fan Z, Chai G, Li G, Wei Z, Hu J, Sheng X, Chen L, Zhu X. Self-Related Stimuli Decoding With Auditory and Visual Modalities Using Stereo-Electroencephalography. Front Neurosci 2021; 15:653965. [PMID: 34017235 PMCID: PMC8129191 DOI: 10.3389/fnins.2021.653965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/06/2021] [Indexed: 11/29/2022] Open
Abstract
Name recognition plays important role in self-related cognitive processes and also contributes to a variety of clinical applications, such as autism spectrum disorder diagnosis and consciousness disorder analysis. However, most previous name-related studies usually adopted noninvasive EEG or fMRI recordings, which were limited by low spatial resolution and temporal resolution, respectively, and thus millisecond-level response latencies in precise brain regions could not be measured using these noninvasive recordings. By invasive stereo-electroencephalography (SEEG) recordings that have high resolution in both the spatial and temporal domain, the current study distinguished the neural response to one's own name or a stranger's name, and explored common active brain regions in both auditory and visual modalities. The neural activities were classified using spatiotemporal features of high-gamma, beta, and alpha band. Results showed that different names could be decoded using multi-region SEEG signals, and the best classification performance was achieved at high gamma (60–145 Hz) band. In this case, auditory and visual modality-based name classification accuracies were 84.5 ± 8.3 and 79.9 ± 4.6%, respectively. Additionally, some single regions such as the supramarginal gyrus, middle temporal gyrus, and insula could also achieve remarkable accuracies for both modalities, supporting their roles in the processing of self-related information. The average latency of the difference between the two responses in these precise regions was 354 ± 63 and 285 ± 59 ms in the auditory and visual modality, respectively. This study suggested that name recognition was attributed to a distributed brain network, and the subsets with decoding capabilities might be potential implanted regions for awareness detection and cognition evaluation.
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Affiliation(s)
- Huanpeng Ye
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Guohong Chai
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guangye Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zixuan Wei
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Centracchio J, Sarno A, Esposito D, Andreozzi E, Pavone L, Di Gennaro G, Bartolo M, Esposito V, Morace R, Casciato S, Bifulco P. Efficient automated localization of ECoG electrodes in CT images via shape analysis. Int J Comput Assist Radiol Surg 2021; 16:543-554. [PMID: 33687667 PMCID: PMC8052236 DOI: 10.1007/s11548-021-02325-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 02/15/2021] [Indexed: 11/30/2022]
Abstract
Purpose People with drug-refractory epilepsy are potential candidates for surgery. In many cases, epileptogenic zone localization requires intracranial investigations, e.g., via ElectroCorticoGraphy (ECoG), which uses subdural electrodes to map eloquent areas of large cortical regions. Precise electrodes localization on cortical surface is mandatory to delineate the seizure onset zone. Simple thresholding operations performed on patients’ computed tomography (CT) volumes recognize electrodes but also other metal objects (e.g., wires, stitches), which need to be manually removed. A new automated method based on shape analysis is proposed, which provides substantially improved performances in ECoG electrodes recognition. Methods The proposed method was retrospectively tested on 24 CT volumes of subjects with drug-refractory focal epilepsy, presenting a large number (> 1700) of round platinum electrodes. After CT volume thresholding, six geometric features of voxel clusters (volume, symmetry axes lengths, circularity and cylinder similarity) were used to recognize the actual electrodes among all metal objects via a Gaussian support vector machine (G-SVM). The proposed method was further tested on seven CT volumes from a public repository. Simultaneous recognition of depth and ECoG electrodes was also investigated on three additional CT volumes, containing penetrating depth electrodes. Results The G-SVM provided a 99.74% mean classification accuracy across all 24 single-patient datasets, as well as on the combined dataset. High accuracies were obtained also on the CT volumes from public repository (98.27% across all patients, 99.68% on combined dataset). An overall accuracy of 99.34% was achieved for the recognition of depth and ECoG electrodes. Conclusions The proposed method accomplishes automated ECoG electrodes localization with unprecedented accuracy and can be easily implemented into existing software for preoperative analysis process. The preliminary yet surprisingly good results achieved for the simultaneous depth and ECoG electrodes recognition are encouraging. Ethical approval n°NCT04479410 by “IRCCS Neuromed” (Pozzilli, Italy), 30th July 2020. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02325-0.
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Affiliation(s)
- Jessica Centracchio
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
| | - Antonio Sarno
- National Institute for Nuclear Physics (INFN), Naples, Italy
| | - Daniele Esposito
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
- Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
- Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | | | | | | | - Vincenzo Esposito
- IRCCS Neuromed, Pozzilli, Italy
- Department of Human Neurosciences, Sapienza University, Rome, Italy
| | | | | | - Paolo Bifulco
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
- Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
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Wang M, Li G, Jiang S, Wei Z, Hu J, Chen L, Zhang D. Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study. J Neural Eng 2020; 17:046043. [DOI: 10.1088/1741-2552/ab9987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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