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Raghavan M, Pilet J, Carlson C, Anderson CT, Mueller W, Lew S, Ustine C, Shah-Basak P, Youssofzadeh V, Beardsley SA. Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy. Sci Rep 2024; 14:17736. [PMID: 39085280 PMCID: PMC11291981 DOI: 10.1038/s41598-024-67120-8] [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: 04/23/2024] [Accepted: 07/08/2024] [Indexed: 08/02/2024] Open
Abstract
Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40-200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.
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Affiliation(s)
- Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
| | - Jared Pilet
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA
| | - Chad Carlson
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | | | - Wade Mueller
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Sean Lew
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Priyanka Shah-Basak
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Vahab Youssofzadeh
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Scott A Beardsley
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA
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Patient-specific solution of the electrocorticography forward problem in deforming brain. Neuroimage 2022; 263:119649. [PMID: 36167268 DOI: 10.1016/j.neuroimage.2022.119649] [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: 09/30/2021] [Revised: 08/25/2022] [Accepted: 09/23/2022] [Indexed: 11/22/2022] Open
Abstract
Invasive intracranial electroencephalography (iEEG), or electrocorticography (ECoG), measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling it can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG forward problem, which is a crucial prerequisite for solving the iEEG inverse problemin epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that rapid and accurate solution of the forward problem in a deformed brain for a given patient is achievable.
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Stolk A, Griffin S, van der Meij R, Dewar C, Saez I, Lin JJ, Piantoni G, Schoffelen JM, Knight RT, Oostenveld R. Integrated analysis of anatomical and electrophysiological human intracranial data. Nat Protoc 2019; 13:1699-1723. [PMID: 29988107 PMCID: PMC6548463 DOI: 10.1038/s41596-018-0009-6] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.
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Affiliation(s)
- Arjen Stolk
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA. .,Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.
| | - Sandon Griffin
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Roemer van der Meij
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Callum Dewar
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.,College of Medicine, University of Illinois, Chicago, IL, USA
| | - Ignacio Saez
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Jack J Lin
- Department of Neurology, University of California, Irvine, Irvine, CA, USA
| | - Giovanni Piantoni
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.,Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.,NatMEG, Karolinska Institutet, Stockholm, Sweden
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Fan X, Roberts DW, Kamal Y, Olson JD, Paulsen KD. Quantification of Subdural Electrode Shift Between Initial Implantation, Postimplantation Computed Tomography, and Subsequent Resection Surgery. Oper Neurosurg (Hagerstown) 2019; 16:9-19. [PMID: 29617890 DOI: 10.1093/ons/opy050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 02/25/2018] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Subdural electrodes are often implanted for localization of epileptic regions. Postoperative computed tomography (CT) is typically acquired to locate electrode positions for planning any subsequent surgical resection. Electrodes are assumed to remain stationary between CT acquisition and resection surgery. OBJECTIVE To quantify subdural electrode shift that occurred between the times of implantation (Crani 1), postoperative CT acquisition, and resection surgery (Crani 2). METHODS Twenty-three patients in this case series undergoing subdural electrode implantation were evaluated. Preoperative magnetic resonance and postoperative CT were acquired and coregistered, and electrode positions were extracted from CT. Intraoperative positions of electrodes and the cortical surface were digitized with a coregistered stereovision system. Movement of the exposed cortical surface was also tracked, and change in electrode positions was calculated relative to both the skull and the cortical surface. RESULTS In the 23 cases, average shift of electrode positions was 8.0 ± 3.3 mm between Crani 1 and CT, 9.2 ± 3.7 mm between CT and Crani 2, and 6.2 ± 3.0 mm between Crani 1 and Crani 2. The average cortical shift was 4.7 ± 1.4 mm with 2.9 ± 1.0 mm in the lateral direction. The average shift of electrode positions relative to the cortical surface between Crani 1 and Crani 2 was 5.5 ± 3.7 mm. CONCLUSION The results show that electrodes shifted laterally not only relative to the skull, but also relative to the cortical surface, thereby displacing the electrodes from their initial placement on the cortex. This has significant clinical implications for resection based upon seizure activity and functional mapping derived from intracranial electrodes.
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Affiliation(s)
- Xiaoyao Fan
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire
| | - David W Roberts
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire.,Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire.,Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.,Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Yasmin Kamal
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire.,Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire
| | - Jonathan D Olson
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire
| | - Keith D Paulsen
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire.,Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire.,Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
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Blenkmann AO, Phillips HN, Princich JP, Rowe JB, Bekinschtein TA, Muravchik CH, Kochen S. iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization. Front Neuroinform 2017; 11:14. [PMID: 28303098 PMCID: PMC5333374 DOI: 10.3389/fninf.2017.00014] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 02/01/2017] [Indexed: 01/03/2023] Open
Abstract
The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes. Here we present iElectrodes, an open-source toolbox that provides robust and accurate semi-automatic localization of both subdural grids and depth electrodes. Using pre- and post-implantation images, the method takes 2–3 min to localize the coordinates in each electrode array and automatically number the electrodes. The proposed pre-processing pipeline allows one to work in a normalized space and to automatically obtain anatomical labels of the localized electrodes without neuroimaging experts. We validated the method with data from 22 patients implanted with a total of 1,242 electrodes. We show that localization distances were within 0.56 mm of those achieved by experienced manual evaluators. iElectrodes provided additional advantages in terms of robustness (even with severe perioperative cerebral distortions), speed (less than half the operator time compared to expert manual localization), simplicity, utility across multiple electrode types (surface and depth electrodes) and all brain regions.
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Affiliation(s)
- Alejandro O Blenkmann
- FRONT Neurolab, Department of Psychology, University of OsloOslo, Norway; Estudios de Neurociencias y Sistemas Complejos, CONICET- El Cruce Hospital - Universidad Nacional Arturo JauretcheBuenos Aires, Argentina; Institute of Cellular Biology and Neuroscience "Prof E. De Robertis," School of Medicine, University of Buenos Aires - CONICETBuenos Aires, Argentina; Epilepsy Section, Division of Neurology, Ramos Mejía HospitalBuenos Aires, Argentina
| | - Holly N Phillips
- Department of Clinical Neurosciences, University of CambridgeCambridge, UK; MRC Cognition and Brain Sciences UnitCambridge, UK
| | - Juan P Princich
- Estudios de Neurociencias y Sistemas Complejos, CONICET- El Cruce Hospital - Universidad Nacional Arturo Jauretche Buenos Aires, Argentina
| | - James B Rowe
- Department of Clinical Neurosciences, University of CambridgeCambridge, UK; MRC Cognition and Brain Sciences UnitCambridge, UK
| | | | - Carlos H Muravchik
- Facultad de Ingeniería, Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales, Universidad Nacional de La Plata La Plata, Argentina
| | - Silvia Kochen
- Estudios de Neurociencias y Sistemas Complejos, CONICET- El Cruce Hospital - Universidad Nacional Arturo JauretcheBuenos Aires, Argentina; Institute of Cellular Biology and Neuroscience "Prof E. De Robertis," School of Medicine, University of Buenos Aires - CONICETBuenos Aires, Argentina; Epilepsy Section, Division of Neurology, Ramos Mejía HospitalBuenos Aires, Argentina
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Rowland NC, Miller KJ, Starr PA. Three-Dimensional Accuracy of ECOG Strip Electrode Localization Using Coregistration of Preoperative MRI and Intraoperative Fluoroscopy. Stereotact Funct Neurosurg 2014; 92:8-16. [DOI: 10.1159/000350027] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2012] [Accepted: 02/19/2013] [Indexed: 11/19/2022]
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Yang AI, Wang X, Doyle WK, Halgren E, Carlson C, Belcher TL, Cash SS, Devinsky O, Thesen T. Localization of dense intracranial electrode arrays using magnetic resonance imaging. Neuroimage 2012; 63:157-165. [PMID: 22759995 PMCID: PMC4408869 DOI: 10.1016/j.neuroimage.2012.06.039] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2012] [Revised: 06/17/2012] [Accepted: 06/20/2012] [Indexed: 10/28/2022] Open
Abstract
Intracranial electrode arrays are routinely used in the pre-surgical evaluation of patients with medically refractory epilepsy, and recordings from these electrodes have been increasingly employed in human cognitive neurophysiology due to their high spatial and temporal resolution. For both researchers and clinicians, it is critical to localize electrode positions relative to the subject-specific neuroanatomy. In many centers, a post-implantation MRI is utilized for electrode detection because of its higher sensitivity for surgical complications and the absence of radiation. However, magnetic susceptibility artifacts surrounding each electrode prohibit unambiguous detection of individual electrodes, especially those that are embedded within dense grid arrays. Here, we present an efficient method to accurately localize intracranial electrode arrays based on pre- and post-implantation MR images that incorporates array geometry and the individual's cortical surface. Electrodes are directly visualized relative to the underlying gyral anatomy of the reconstructed cortical surface of individual patients. Validation of this approach shows high spatial accuracy of the localized electrode positions (mean of 0.96 mm ± 0.81 mm for 271 electrodes across 8 patients). Minimal user input, short processing time, and utilization of radiation-free imaging are strong incentives to incorporate quantitatively accurate localization of intracranial electrode arrays with MRI for research and clinical purposes. Co-registration to a standard brain atlas further allows inter-subject comparisons and relation of intracranial EEG findings to the larger body of neuroimaging literature.
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Affiliation(s)
- Andrew I. Yang
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY 10016, USA
| | - Xiuyuan Wang
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY 10016, USA
| | - Werner K. Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY 10016, USA
- Department of Neurosurgery, New York University School of Medicine, New York, NY 10016, USA
| | - Eric Halgren
- Department of Radiology, University of California at San Diego, San Diego, CA 92093, USA
- Department of Neurosciences, University of California at San Diego, San Diego, CA 92093, USA
- Department of Psychiatry, University of California at San Diego, San Diego, CA 92093, USA
| | - Chad Carlson
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY 10016, USA
| | - Thomas L. Belcher
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY 10016, USA
| | - Sydney S. Cash
- Department of Neurology, Epilepsy Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY 10016, USA
- Department of Neurosurgery, New York University School of Medicine, New York, NY 10016, USA
| | - Thomas Thesen
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY 10016, USA
- Department of Radiology, University of California at San Diego, San Diego, CA 92093, USA
- Department of Neurosciences, University of California at San Diego, San Diego, CA 92093, USA
- Department of Psychiatry, University of California at San Diego, San Diego, CA 92093, USA
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