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Bougou V, Vanhoyland M, Cleeren E, Janssen P, Van Paesschen W, Theys T. Mesoscale insights in Epileptic Networks: A Multimodal Intracranial Dataset. Sci Data 2025; 12:774. [PMID: 40348768 PMCID: PMC12065801 DOI: 10.1038/s41597-025-05026-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 04/09/2025] [Indexed: 05/14/2025] Open
Abstract
Understanding the intricate dynamics of epileptic networks at the mesoscale is crucial for advancing our knowledge of epilepsy pathophysiology and developing targeted interventions. In this data descriptor, we present a comprehensive dataset encompassing intracranial electroencephalography (iEEG) recordings, Local Field Potentials (LFP), and Multiunit Activity (MUA) data obtained from Microelectrode arrays (MEA; Utah array; Blackrock). 12 seizures were recorded in 5 epilepsy patients with the MEA. Our dataset offers a unique opportunity to investigate the complex interactions between diverse neural signals across brain areas and to study the mesoscale networks in focal epilepsy. This dataset can be used to explore the modulations of LFP and MUA in conjunction with iEEG, offering potential insights into the spatiotemporal dynamics of epileptic networks. Additionally, the high temporal resolution of the data allows for the computation of High-Frequency Oscillations (HFOs) in both LFP and iEEG signals, facilitating the investigation of their potential relationship with MUA activity.
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Affiliation(s)
- Vasiliki Bougou
- Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium.
- Laboratory for Neuro - and Psychophysiology, Research Group Neurophysiology, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium.
| | - Michaël Vanhoyland
- Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
- Laboratory for Neuro - and Psychophysiology, Research Group Neurophysiology, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
- Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium
| | - Evy Cleeren
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
| | - Peter Janssen
- Laboratory for Neuro - and Psychophysiology, Research Group Neurophysiology, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
| | - Wim Van Paesschen
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
| | - Tom Theys
- Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
- Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium
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Fuhrer J, Glette K, Ivanovic J, Larsson PG, Bekinschtein T, Kochen S, Knight RT, Tørresen J, Solbakk AK, Endestad T, Blenkmann A. Direct brain recordings reveal implicit encoding of structure in random auditory streams. Sci Rep 2025; 15:14725. [PMID: 40289162 PMCID: PMC12034823 DOI: 10.1038/s41598-025-98865-5] [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: 12/20/2024] [Accepted: 04/15/2025] [Indexed: 04/30/2025] Open
Abstract
The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.
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Affiliation(s)
- Julian Fuhrer
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.
- Department of Informatics, University of Oslo, Oslo, Norway.
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Kyrre Glette
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Jugoslav Ivanovic
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Pål Gunnar Larsson
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Tristan Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, UK
| | - Silvia Kochen
- ENyS-CONICET-Univ Jauretche, Buenos Aires, Argentina
| | - Robert T Knight
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, USA
| | - Jim Tørresen
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Anne-Kristin Solbakk
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Tor Endestad
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Alejandro Blenkmann
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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Ramirez JG, Vanhoyland M, Ratan Murty NA, Decramer T, Van Paesschen W, Bracci S, Op de Beeck H, Kanwisher N, Janssen P, Theys T. Intracortical recordings reveal the neuronal selectivity for bodies and body parts in the human visual cortex. Proc Natl Acad Sci U S A 2024; 121:e2408871121. [PMID: 39652751 PMCID: PMC11665852 DOI: 10.1073/pnas.2408871121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 10/22/2024] [Indexed: 02/13/2025] Open
Abstract
Body perception plays a fundamental role in social cognition. Yet, the neural mechanisms underlying this process in humans remain elusive given the spatiotemporal constraints of functional imaging. Here, we present intracortical recordings of single- and multiunit spiking activity in two epilepsy surgery patients in or near the extrastriate body area, a critical region for body perception. Our recordings revealed a strong preference for human bodies over a large range of control stimuli. Notably, body selectivity was driven by a distinct selectivity for body parts. The observed body selectivity generalized to nonphotographic depictions of bodies including silhouettes and stick figures. Overall, our study provides unique neural data that bridge the gap between human neuroimaging and macaque electrophysiology studies, laying a solid foundation for computational models of human body processing.
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Affiliation(s)
- Jesus Garcia Ramirez
- Research group Experimental Neurosurgery and Neuroanatomy, Katholieke Universiteit Leuven, and the Leuven Brain Institute, LeuvenB-3000, Belgium
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, Katholieke Universiteit Leuven and the Leuven Brain Institute, LeuvenB-3000, Belgium
| | - Michael Vanhoyland
- Research group Experimental Neurosurgery and Neuroanatomy, Katholieke Universiteit Leuven, and the Leuven Brain Institute, LeuvenB-3000, Belgium
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, Katholieke Universiteit Leuven and the Leuven Brain Institute, LeuvenB-3000, Belgium
- Department of Neurosurgery, Universitaire Ziekenhuizen Leuven, Katholieke Universiteit Leuven, LeuvenB-3000, Belgium
| | - N. A. Ratan Murty
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA02139
- The Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Thomas Decramer
- Research group Experimental Neurosurgery and Neuroanatomy, Katholieke Universiteit Leuven, and the Leuven Brain Institute, LeuvenB-3000, Belgium
- Department of Neurosurgery, Universitaire Ziekenhuizen Leuven, Katholieke Universiteit Leuven, LeuvenB-3000, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, Katholieke Universiteit Leuven, LeuvenB-3000, Belgium
| | - Stefania Bracci
- Department of Psychology and Cognitive Science, University of Trento, Trento38068, Italy
| | - Hans Op de Beeck
- Laboratory for Biological Psychology, Katholieke Universiteit Leuven, LeuvenB-3000, Belgium
| | - Nancy Kanwisher
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA02139
- The Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Peter Janssen
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, Katholieke Universiteit Leuven and the Leuven Brain Institute, LeuvenB-3000, Belgium
| | - Tom Theys
- Research group Experimental Neurosurgery and Neuroanatomy, Katholieke Universiteit Leuven, and the Leuven Brain Institute, LeuvenB-3000, Belgium
- Department of Neurosurgery, Universitaire Ziekenhuizen Leuven, Katholieke Universiteit Leuven, LeuvenB-3000, Belgium
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Hartigan T, Byrne L, Lavelle S, McDonnell A, Sweeney K, Reilly RB. An Improved Neurosurgical Planning Method for Focal Epileptic Seizure Surgery using Stereo-EEG-Based Source Localization and Multimodal Imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2024; 2024:1-4. [PMID: 40039262 DOI: 10.1109/embc53108.2024.10781654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
Refractory Focal Epilepsy can be treated by the surgical resection of the Seizure Onset Zone (SOZ), the region in the brain from which seizures originate. To remove the SOZ, precise localization must be performed to identify this region, and to minimize the risk of removing eloquent cortex. StereoEEG is a valuable method to localize the SOZ, by recording the propagation of epileptic signals using a series of implanted depth electrodes. This allows the origin of the seizure signals to be determined based on the time at which they are detected at known electrode contact coordinates along the implanted electrodes. The automation of the localization of the SOZ using stereo-EEG, CT and MRI data is becoming increasingly relevant in the neurosurgical literature, as it offers an opportunity for increased accuracy and efficiency. This study proposes a novel method to localize the SOZ by using multimodal image processing. The method allows a statistical representation of the SOZ to be constructed on the cortical surface model, by using a series of spatial transformations. In a clinical case of MRI-positive focal epilepsy, the proposed pipeline was able to correctly identify the SOZ whilst using electrophysiological input from distant electrodes with 80-90% of the pipeline's result being within the resection cavity. In an MRI-negative patient's result, 60-75% of the SOZ determination was also within the resective cavity. In both cases, the pipeline showed greater than 50% reduction in SOZ volume determination. Such precise localization may allow for smaller resection volumes to achieve seizure freedom and reduce neurological complications. This method may therefore offer a more accurate solution to SOZ localization with a reduced clinical workload.
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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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6
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Fujimoto S, Matsuo T, Nakata Y, Shiojima H. Real-time display of intracranial subdural electrodes and the brain surface during an electrode implantation procedure using permeable film. Surg Neurol Int 2024; 15:190. [PMID: 38974543 PMCID: PMC11225510 DOI: 10.25259/sni_74_2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/15/2024] [Indexed: 07/09/2024] Open
Abstract
Background Subdural electrode (SDE) implantation is an important method of diagnosing epileptogenic lesions and mapping brain function, even with the current preference for stereoelectroencephalography. We developed a novel method to assess SDEs and the brain surface during the electrode implantation procedure using brain images printed onto permeable films and intraoperative fluoroscopy. This method can help verify the location of the electrode during surgery and improve the accuracy of SDE implantation. Methods We performed preoperative imaging by magnetic resonance imaging and computed tomography. Subsequently, the images were edited and fused to visualize the gyrus and sulcus better. We printed the images on permeable films and superimposed them on the intraoperative fluoroscopy display. The intraoperative and postoperative coordinates of the electrodes were obtained after the implantation surgery, and the differences in the locations were calculated. Results Permeable films were created for a total of eight patients with intractable epilepsy. The median difference of the electrodes between the intraoperative and postoperative images was 4.6 mm (Interquartile range 2.9-7.1). The locations of electrodes implanted outside the operation field were not significantly different from those implanted inside. Conclusion Our new method may guide the implantation of SDEs into their planned location.
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Affiliation(s)
- So Fujimoto
- Department of Neurosurgery, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan
| | - Takeshi Matsuo
- Department of Neurosurgery, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan
| | - Yasuhiro Nakata
- Department of Neuroradiology, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan
| | - Honoka Shiojima
- Department of Neuroradiology, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan
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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. Epilepsia 2024; 65:817-829. [PMID: 38148517 PMCID: PMC10948311 DOI: 10.1111/epi.17863] [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: 08/14/2023] [Revised: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVE Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive 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. 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 semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes 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 and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). 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 postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations. SIGNIFICANCE iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.
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Affiliation(s)
- Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, 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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Mohanta S, Cleveland DM, Afrasiabi M, Rhone AE, Górska U, Cooper Borkenhagen M, Sanders RD, Boly M, Nourski KV, Saalmann YB. Traveling waves shape neural population dynamics enabling predictions and internal model updating. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574848. [PMID: 38260606 PMCID: PMC10802392 DOI: 10.1101/2024.01.09.574848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The brain generates predictions based on statistical regularities in our environment. However, it is unclear how predictions are optimized through iterative interactions with the environment. Because traveling waves (TWs) propagate across the cortex shaping neural excitability, they can carry information to serve predictive processing. Using human intracranial recordings, we show that anterior-to-posterior alpha TWs correlated with prediction strength. Learning about priors altered neural state space trajectories, and how much it altered correlated with trial-by-trial prediction strength. Learning involved mismatches between predictions and sensory evidence triggering alpha-phase resets in lateral temporal cortex, accompanied by stronger alpha phase-high gamma amplitude coupling and high-gamma power. The mismatch initiated posterior-to-anterior alpha TWs and change in the subsequent trial's state space trajectory, facilitating model updating. Our findings suggest a vital role of alpha TWs carrying both predictions to sensory cortex and mismatch signals to frontal cortex for trial-by-trial fine-tuning of predictive models.
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Affiliation(s)
- S Mohanta
- Department of Psychology, University of Wisconsin-Madison, WI, USA
| | - D M Cleveland
- Department of Psychology, University of Wisconsin-Madison, WI, USA
| | - M Afrasiabi
- Department of Psychology, University of Wisconsin-Madison, WI, USA
| | - A E Rhone
- Department of Neurosurgery, University of Iowa, IA, USA
| | - U Górska
- Department of Psychiatry, University of Wisconsin-Madison, WI, USA
| | | | - R D Sanders
- Specialty of Anaesthesia, University of Sydney, Camperdown, NSW, Australia and Department of Anaesthetics and Institute of Academic Surgery, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - M Boly
- Department of Psychiatry, University of Wisconsin-Madison, WI, USA
- Department of Neurology, University of Wisconsin-Madison, WI, USA
| | - K V Nourski
- Department of Neurosurgery, University of Iowa, IA, USA
- Iowa Neuroscience Institute, University of Iowa, IA, USA
| | - Y B Saalmann
- Department of Psychology, University of Wisconsin-Madison, WI, USA
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9
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Shamas M, Yeh HJ, Fried I, Engel J, Staba RJ. High-rate leading spikes in propagating spike sequences predict seizure outcome in surgical patients with temporal lobe epilepsy. Brain Commun 2023; 5:fcad289. [PMID: 37953846 PMCID: PMC10636565 DOI: 10.1093/braincomms/fcad289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/14/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
Inter-ictal spikes aid in the diagnosis of epilepsy and in planning surgery of medication-resistant epilepsy. However, the localizing information from spikes can be unreliable because spikes can propagate, and the burden of spikes, often assessed as a rate, does not always correlate with the seizure onset zone or seizure outcome. Recent work indicates identifying where spikes regularly emerge and spread could localize the seizure network. Thus, the current study sought to better understand where and how rates of single and coupled spikes, and especially brain regions with high-rate and leading spike of a propagating sequence, informs the extent of the seizure network. In 37 patients with medication-resistant temporal lobe seizures, who had surgery to treat their seizure disorder, an algorithm detected spikes in the pre-surgical depth inter-ictal EEG. A separate algorithm detected spike propagation sequences and identified the location of leading and downstream spikes in each sequence. We analysed the rate and power of single spikes on each electrode and coupled spikes between pairs of electrodes, and the proportion of sites with high-rate, leading spikes in relation to the seizure onset zone of patients seizure free (n = 19) and those with continuing seizures (n = 18). We found increased rates of single spikes in mesial temporal seizure onset zone (ANOVA, P < 0.001, η2 = 0.138), and increased rates of coupled spikes within, but not between, mesial-, lateral- and extra-temporal seizure onset zone of patients with continuing seizures (P < 0.001; η2 = 0.195, 0.113 and 0.102, respectively). In these same patients, there was a higher proportion of brain regions with high-rate leaders, and each sequence contained a greater number of spikes that propagated with a higher efficiency over a longer distance outside the seizure onset zone than patients seizure free (Wilcoxon, P = 0.0172). The proportion of high-rate leaders in and outside the seizure onset zone could predict seizure outcome with area under curve = 0.699, but not rates of single or coupled spikes (0.514 and 0.566). Rates of coupled spikes to a greater extent than single spikes localize the seizure onset zone and provide evidence for inter-ictal functional segregation, which could be an adaptation to avert seizures. Spike rates, however, have little value in predicting seizure outcome. High-rate spike sites leading propagation could represent sources of spikes that are important components of an efficient seizure network beyond the clinical seizure onset zone, and like the seizure onset zone these, too, need to be removed, disconnected or stimulated to increase the likelihood for seizure control.
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Affiliation(s)
- Mohamad Shamas
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Hsiang J Yeh
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Itzhak Fried
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Richard J Staba
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
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10
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Bellier L, Llorens A, Marciano D, Gunduz A, Schalk G, Brunner P, Knight RT. Music can be reconstructed from human auditory cortex activity using nonlinear decoding models. PLoS Biol 2023; 21:e3002176. [PMID: 37582062 PMCID: PMC10427021 DOI: 10.1371/journal.pbio.3002176] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 05/30/2023] [Indexed: 08/17/2023] Open
Abstract
Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superior temporal gyrus (STG), evidenced a new STG subregion tuned to musical rhythm, and defined an anterior-posterior STG organization exhibiting sustained and onset responses to musical elements. Our findings show the feasibility of applying predictive modeling on short datasets acquired in single patients, paving the way for adding musical elements to brain-computer interface (BCI) applications.
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Affiliation(s)
- Ludovic Bellier
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Anaïs Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Déborah Marciano
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Gerwin Schalk
- Department of Neurology, Albany Medical College, Albany, New York, United States of America
| | - Peter Brunner
- Department of Neurology, Albany Medical College, Albany, New York, United States of America
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, Missouri, United States of America
- National Center for Adaptive Neurotechnologies, Albany, New York, United States of America
| | - Robert T. Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America
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11
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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: 1.5] [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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12
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Juan E, Górska U, Kozma C, Papantonatos C, Bugnon T, Denis C, Kremen V, Worrell G, Struck AF, Bateman LM, Merricks EM, Blumenfeld H, Tononi G, Schevon C, Boly M. Distinct signatures of loss of consciousness in focal impaired awareness versus tonic-clonic seizures. Brain 2023; 146:109-123. [PMID: 36383415 PMCID: PMC10582624 DOI: 10.1093/brain/awac291] [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: 10/06/2021] [Revised: 05/17/2022] [Accepted: 06/11/2022] [Indexed: 11/17/2022] Open
Abstract
Loss of consciousness is a hallmark of many epileptic seizures and carries risks of serious injury and sudden death. While cortical sleep-like activities accompany loss of consciousness during focal impaired awareness seizures, the mechanisms of loss of consciousness during focal to bilateral tonic-clonic seizures remain unclear. Quantifying differences in markers of cortical activation and ictal recruitment between focal impaired awareness and focal to bilateral tonic-clonic seizures may also help us to understand their different consequences for clinical outcomes and to optimize neuromodulation therapies. We quantified clinical signs of loss of consciousness and intracranial EEG activity during 129 focal impaired awareness and 50 focal to bilateral tonic-clonic from 41 patients. We characterized intracranial EEG changes both in the seizure onset zone and in areas remote from the seizure onset zone with a total of 3386 electrodes distributed across brain areas. First, we compared the dynamics of intracranial EEG sleep-like activities: slow-wave activity (1-4 Hz) and beta/delta ratio (a validated marker of cortical activation) during focal impaired awareness versus focal to bilateral tonic-clonic. Second, we quantified differences between focal to bilateral tonic-clonic and focal impaired awareness for a marker validated to detect ictal cross-frequency coupling: phase-locked high gamma (high-gamma phased-locked to low frequencies) and a marker of ictal recruitment: the epileptogenicity index. Third, we assessed changes in intracranial EEG activity preceding and accompanying behavioural generalization onset and their correlation with electromyogram channels. In addition, we analysed human cortical multi-unit activity recorded with Utah arrays during three focal to bilateral tonic-clonic seizures. Compared to focal impaired awareness, focal to bilateral tonic-clonic seizures were characterized by deeper loss of consciousness, even before generalization occurred. Unlike during focal impaired awareness, early loss of consciousness before generalization was accompanied by paradoxical decreases in slow-wave activity and by increases in high-gamma activity in parieto-occipital and temporal cortex. After generalization, when all patients displayed loss of consciousness, stronger increases in slow-wave activity were observed in parieto-occipital cortex, while more widespread increases in cortical activation (beta/delta ratio), ictal cross-frequency coupling (phase-locked high gamma) and ictal recruitment (epileptogenicity index). Behavioural generalization coincided with a whole-brain increase in high-gamma activity, which was especially synchronous in deep sources and could not be explained by EMG. Similarly, multi-unit activity analysis of focal to bilateral tonic-clonic revealed sustained increases in cortical firing rates during and after generalization onset in areas remote from the seizure onset zone. Overall, these results indicate that unlike during focal impaired awareness, the neural signatures of loss of consciousness during focal to bilateral tonic-clonic consist of paradoxical increases in cortical activation and neuronal firing found most consistently in posterior brain regions. These findings suggest differences in the mechanisms of ictal loss of consciousness between focal impaired awareness and focal to bilateral tonic-clonic and may account for the more negative prognostic consequences of focal to bilateral tonic-clonic.
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Affiliation(s)
- Elsa Juan
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Department of Psychology, University of Amsterdam, Amsterdam, 1018 WS, The Netherlands
| | - Urszula Górska
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Smoluchowski Institute of Physics, Jagiellonian University, 30-348 Krakow, Poland
| | - Csaba Kozma
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Cynthia Papantonatos
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Tom Bugnon
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Colin Denis
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, 16000, Czech Republic
| | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Neurology, William S. Middleton Veterans Administration Hospital, Madison, WI 53705, USA
| | - Lisa M Bateman
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Edward M Merricks
- Department of Neurology, Columbia University, New York City, NY 10032, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale School of Medicine, New Haven, CT 06519, USA
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Catherine Schevon
- Department of Neurology, Columbia University, New York City, NY 10032, USA
| | - Melanie Boly
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
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13
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Interictal Gamma Event Connectivity Differentiates the Seizure Network and Outcome in Patients after Temporal Lobe Epilepsy Surgery. eNeuro 2022; 9:ENEURO.0141-22.2022. [PMID: 36418173 PMCID: PMC9770020 DOI: 10.1523/eneuro.0141-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Studies of interictal EEG functional connectivity in the epileptic brain seek to identify abnormal interactions between brain regions involved in generating seizures, which clinically often is defined by the seizure onset zone (SOZ). However, there is evidence for abnormal connectivity outside the SOZ (NSOZ), and removal of the SOZ does not always result in seizure control, suggesting, in some cases, that the extent of abnormal connectivity indicates a larger seizure network than the SOZ. To better understand the potential differences in interictal functional connectivity in relation to the seizure network and outcome, we computed event connectivity in the theta (4-8 Hz, ThEC), low-gamma (30-55 Hz, LGEC), and high-gamma (65-95 Hz, HGEC) bands from interictal depth EEG recorded in surgical patients with medication-resistant seizures suspected to begin in the temporal lobe. Analysis finds stronger LGEC and HGEC in SOZ than NSOZ of seizure-free (SF) patients (p = 1.10e-9, 0.0217), but no difference in not seizure-free (NSF) patients. There were stronger LGEC and HGEC between mesial and lateral temporal SOZ of SF than NSF patients (p = 0.00114, 0.00205), and stronger LGEC and ThEC in NSOZ of NSF than SF patients (p = 0.0089, 0.0111). These results show that event connectivity is sensitive to differences in the interactions between regions in SOZ and NSOZ and SF and NSF patients. Patients with differential strengths in event connectivity could represent a well-localized seizure network, whereas an absence of differences could indicate a larger seizure network than the one localized by the SOZ and higher likelihood for seizure recurrence.
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14
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 PMCID: PMC10190110 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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Affiliation(s)
- Manuel R Mercier
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
| | | | - François Tadel
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA United States of America
| | - Pietro Avanzini
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Outer St, Beijing 100875, China
| | - Dillan Cellier
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America
| | - Maria Del Vecchio
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Liberty S Hamilton
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States of America; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, United States of America; Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States of America
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Robert T Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States of America
| | - Anais Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
| | - Pierre Megevand
- Department of Clinical neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lucia Melloni
- Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, Frankfurt am Main 60322, Germany; Department of Neurology, NYU Grossman School of Medicine, 145 East 32nd Street, Room 828, New York, NY 10016, United States of America
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Vitória Piai
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Medical Psychology, Radboudumc, Donders Centre for Medical Neuroscience, Nijmegen, the Netherlands
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN, United States of America
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Göttingen, Germany; Perception and Plasticity Group, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Sydney E Smith
- Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America
| | - Arjen Stolk
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
| | - Nicole C Swann
- University of Oregon in the Department of Human Physiology, United States of America
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Bradley Voytek
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America; Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America; Halıcıoğlu Data Science Institute, University of California, La Jolla, San Diego, United States of America; Kavli Institute for Brain and Mind, University of California, La Jolla, San Diego, United States of America
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - 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
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
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15
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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: 0.3] [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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16
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Dubarry AS, Liégeois-Chauvel C, Trébuchon A, Bénar C, Alario FX. An open-source toolbox for Multi-patient Intracranial EEG Analysis (MIA). Neuroimage 2022; 257:119251. [PMID: 35568349 DOI: 10.1016/j.neuroimage.2022.119251] [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: 12/08/2021] [Revised: 03/31/2022] [Accepted: 04/26/2022] [Indexed: 10/18/2022] Open
Abstract
Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses therein recorded. In this context, we designed, implemented, and tested a user-friendly and efficient open-source toolbox for Multi-Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing event related iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics. The signals can be analyzed in the temporal and time-frequency domains, and the similarity of time courses across patients or contacts can be assessed within anatomical regions. MIA allows visualizing all these results in a variety of formats at every step of the analysis. Here, we present the toolbox architecture and illustrate the different steps and features of the analysis pipeline using a group dataset collected during a language task.
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Affiliation(s)
- A-Sophie Dubarry
- Aix Marseille Univ, CNRS, LPL, Aix-en-Provence, France; Aix Marseille Univ, CNRS, LPC, Aix-en-Provence, France.
| | - Catherine Liégeois-Chauvel
- Cortical Systems Laboratory, University of Pittsburgh, Pennsylvania, USA; Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Agnès Trébuchon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Hôpital la Timone, Service Épileptologie et Rythmologie Cérébrale, Marseille, France
| | - Christian Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - F-Xavier Alario
- Aix Marseille Univ, CNRS, LPC, Aix-en-Provence, France; Cortical Systems Laboratory, University of Pittsburgh, Pennsylvania, USA
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17
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Salas MA, Bell J, Niketeghad S, Oswalt D, Bosking W, Patel U, Dorn JD, Yoshor D, Greenberg R, Bari A, Pouratian N. Sequence of visual cortex stimulation affects phosphene brightness in blind subjects. Brain Stimul 2022; 15:605-614. [DOI: 10.1016/j.brs.2022.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/12/2022] [Accepted: 03/29/2022] [Indexed: 11/02/2022] Open
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18
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Davis TS, Caston RM, Philip B, Charlebois CM, Anderson DN, Weaver KE, Smith EH, Rolston JD. Corrigendum: LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes. Front Neurosci 2022; 16:858978. [PMID: 35250475 PMCID: PMC8889116 DOI: 10.3389/fnins.2022.858978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 11/14/2022] Open
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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19
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Ganti B, Chaitanya G, Balamurugan RS, Nagaraj N, Balasubramanian K, Pati S. Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG. Front Neurol 2022; 13:755094. [PMID: 35250803 PMCID: PMC8889931 DOI: 10.3389/fneur.2022.755094] [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: 08/07/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Abstract
Seizure detection algorithms are often optimized to detect seizures from the epileptogenic cortex. However, in non-localizable epilepsies, the thalamus is frequently targeted for neuromodulation. Developing a reliable seizure detection algorithm from thalamic SEEG may facilitate the translation of closed-loop neuromodulation. Deep learning algorithms promise reliable seizure detectors, but the major impediment is the lack of larger samples of curated ictal thalamic SEEG needed for training classifiers. We aimed to investigate if synthetic data generated by temporal Generative Adversarial Networks (TGAN) can inflate the sample size to improve the performance of a deep learning classifier of ictal and interictal states from limited samples of thalamic SEEG. Thalamic SEEG from 13 patients (84 seizures) was obtained during stereo EEG evaluation for epilepsy surgery. Overall, TGAN generated synthetic data augmented the performance of the bidirectional Long-Short Term Memory (BiLSTM) performance in classifying thalamic ictal and baseline states. Adding synthetic data improved the accuracy of the detection model by 18.5%. Importantly, this approach can be applied to classify electrographic seizure onset patterns or develop patient-specific seizure detectors from implanted neuromodulation devices.
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Affiliation(s)
- Bhargava Ganti
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | - Ganne Chaitanya
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX, United States
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Nithin Nagaraj
- Consciousness Studies Programme, National Institute of Advanced Studies, Bengaluru, India
| | - Karthi Balasubramanian
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | - Sandipan Pati
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX, United States
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
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20
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Irannejad A, Chaitanya G, Toth E, Pizarro D, Pati S. Direct Cortical Stimulation to Probe the Ictogenicity of the Epileptogenic Nodes in Temporal Lobe Epilepsy. Front Neurol 2022; 12:761412. [PMID: 35095721 PMCID: PMC8793936 DOI: 10.3389/fneur.2021.761412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/09/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate mapping of the seizure onset zone (SOZ) is critical to the success of epilepsy surgery outcomes. Epileptogenicity index (EI) is a statistical method that delineates hyperexcitable brain regions involved in the generation and early propagation of seizures. However, EI can overestimate the SOZ for particular electrographic seizure onset patterns. Therefore, using direct cortical stimulation (DCS) as a probing tool to identify seizure generators, we systematically evaluated the causality of the high EI nodes (>0.3) in replicating the patient's habitual seizures. Specifically, we assessed the diagnostic yield of high EI nodes, i.e., the proportion of high EI nodes that evoked habitual seizures. A retrospective single-center study that included post-stereo encephalography (SEEG) confirmed TLE patients (n = 37) that had all high EI nodes stimulated, intending to induce a seizure. We evaluated the nodal responses (true and false responder rate) to stimulation and correlated with electrographic seizure onset patterns (hypersynchronous-HYP and low amplitude fast activity patterns-LAFA) and clinically defined SOZ. The ictogenicity (i.e., the propensity to induce the patient's habitual seizure) of a high EI node was only 44.5%. The LAFA onset pattern had a significantly higher response rate to DCS (i.e., higher evoked seizures). The concordance of an evoked habitual seizure with a clinically defined SOZ with good outcomes was over 50% (p = 0.0025). These results support targeted mapping of SOZ in LAFA onset patterns by performing DCS in high EI nodes to distinguish seizure generators (true responders) from hyperexcitable nodes that may be involved in early propagation.
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Affiliation(s)
- Auriana Irannejad
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Ganne Chaitanya
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Emilia Toth
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Diana Pizarro
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sandipan Pati
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Sandipan Pati
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21
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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: 0.7] [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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22
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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: 34] [Impact Index Per Article: 8.5] [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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23
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Speech-related auditory salience detection in the posterior superior temporal region. Neuroimage 2021; 248:118840. [PMID: 34958951 DOI: 10.1016/j.neuroimage.2021.118840] [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: 06/02/2021] [Revised: 11/13/2021] [Accepted: 12/19/2021] [Indexed: 11/22/2022] Open
Abstract
Processing auditory human speech requires both detection (early and transient) and analysis (sustained). We analyzed high gamma (70-110 Hz) activity of intracranial electroencephalography waveforms acquired during an auditory task that paired forward speech, reverse speech, and signal correlated noise. We identified widespread superior temporal sites with sustained activity responding only to forward and reverse speech regardless of paired order. More localized superior temporal auditory onset sites responded to all stimulus types when presented first in a pair and responded in recurrent fashion to the second paired stimulus in select conditions even in the absence of interstimulus silence; a novel finding. Auditory onset activity to a second paired sound recurred according to relative salience, with evidence of partial suppression during linguistic processing. We propose that temporal lobe auditory onset sites facilitate a salience detector function with hysteresis of 200 ms and are influenced by cortico-cortical feedback loops involving linguistic processing and articulation.
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24
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Tamilia E, Matarrese MAG, Ntolkeras G, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Noninvasive Mapping of Ripple Onset Predicts Outcome in Epilepsy Surgery. Ann Neurol 2021; 89:911-925. [PMID: 33710676 PMCID: PMC8229023 DOI: 10.1002/ana.26066] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Intracranial electroencephalographic (icEEG) studies show that interictal ripples propagate across the brain of children with medically refractory epilepsy (MRE), and the onset of this propagation (ripple onset zone [ROZ]) estimates the epileptogenic zone. It is still unknown whether we can map this propagation noninvasively. The goal of this study is to map ripples (ripple zone [RZ]) and their propagation onset (ROZ) using high-density EEG (HD-EEG) and magnetoencephalography (MEG), and to estimate their prognostic value in pediatric epilepsy surgery. METHODS We retrospectively analyzed simultaneous HD-EEG and MEG data from 28 children with MRE who underwent icEEG and epilepsy surgery. Using electric and magnetic source imaging, we estimated virtual sensors (VSs) at brain locations that matched the icEEG implantation. We detected ripples on VSs, defined the virtual RZ and virtual ROZ, and estimated their distance from icEEG. We assessed the predictive value of resecting virtual RZ and virtual ROZ for postsurgical outcome. Interictal spike localization on HD-EEG and MEG was also performed and compared with ripples. RESULTS We mapped ripple propagation in all patients with HD-EEG and in 27 (96%) patients with MEG. The distance from icEEG did not differ between HD-EEG and MEG when mapping the RZ (26-27mm, p = 0.6) or ROZ (22-24mm, p = 0.4). Resecting the virtual ROZ, but not virtual RZ or the sources of spikes, was associated with good outcome for HD-EEG (p = 0.016) and MEG (p = 0.047). INTERPRETATION HD-EEG and MEG can map interictal ripples and their propagation onset (virtual ROZ). Noninvasively mapping the ripple onset may augment epilepsy surgery planning and improve surgical outcome of children with MRE. ANN NEUROL 2021;89:911-925.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Margherita A. G. Matarrese
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of EngineeringUniversity Bio‐Medico Campus of RomeRomeItaly
| | - Georgios Ntolkeras
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - P. Ellen Grant
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Joseph R. Madsen
- Epilepsy Surgery Program, Department of NeurosurgeryBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Steve M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMA
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTX
- School of Medicine, Texas Christian University and University of North Texas Health Science CenterFort WorthTX
- Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTX
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25
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Tantawi M, Miao J, Matias C, Skidmore CT, Sperling MR, Sharan AD, Wu C. Gray Matter Sampling Differences Between Subdural Electrodes and Stereoelectroencephalography Electrodes. Front Neurol 2021; 12:669406. [PMID: 33986721 PMCID: PMC8110924 DOI: 10.3389/fneur.2021.669406] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/31/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Stereoelectroencephalography (SEEG) has seen a recent increase in popularity in North America; however, concerns regarding the spatial sampling capabilities of SEEG remain. We aimed to quantify and compare the spatial sampling of subdural electrode (SDE) and SEEG implants. Methods: Patients with drug-resistant epilepsy who underwent invasive monitoring were included in this retrospective case-control study. Ten SEEG cases were compared with ten matched SDE cases based on clinical presentation and pre-implantation hypothesis. To quantify gray matter sampling, MR and CT images were coregistered and a 2.5mm radius sphere was superimposed over the center of each electrode contact. The estimated recording volume of gray matter was defined as the cortical voxels within these spherical models. Paired t-tests were performed to compare volumes and locations of SDE and SEEG recording. A Ripley's K-function analysis was performed to quantify differences in spatial distributions. Results: The average recording volume of gray matter by each individual contact was similar between the two modalities. SEEG implants sampled an average of 20% more total gray matter, consisted of an average of 17% more electrode contacts, and had 77% more of their contacts covering gray matter within sulci. Insular coverage was only achieved with SEEG. SEEG implants generally consist of discrete areas of dense local coverage scattered across the brain; while SDE implants cover relatively contiguous areas with lower density recording. Significance: Average recording volumes per electrode contact are similar for SEEG and SDE, but SEEG may allow for greater overall volumes of recording as more electrodes can be routinely implanted. The primary difference lies in the location and distribution of gray matter than can be sampled. The selection between SEEG and SDE implantation depends on sampling needs of the invasive implant.
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Affiliation(s)
- Mohamed Tantawi
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States.,Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jingya Miao
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States.,Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - Caio Matias
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States.,Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | | | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Ashwini D Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chengyuan Wu
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States.,Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
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26
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Thye M, Geller J, Szaflarski JP, Mirman D. Intracranial EEG evidence of functional specialization for taxonomic and thematic relations. Cortex 2021; 140:40-50. [PMID: 33933929 DOI: 10.1016/j.cortex.2021.03.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 02/19/2021] [Accepted: 03/12/2021] [Indexed: 10/21/2022]
Abstract
The dual-hub account posits that the neural organization of semantic knowledge is segregated by the type of semantic relation with anterior temporal lobe (ATL) specializing for taxonomic relations and inferior parietal lobule (IPL) for thematic relations. This study critically examined this account by recording intracranial EEG from an array of depth electrodes within ATL, IPL, and two regions within the semantic control network, inferior frontal gyrus (IFG) and posterior middle temporal gyrus (pMTG), while 17 participants with refractory epilepsy completed a semantic relatedness judgment task. We observed a significant difference between relation types in ATL and IPL approximately 600-800 ms after trial presentation, and no significant differences in IFG or pMTG. Within this time window, alpha and theta suppression indexing cognitive effort and memory retrieval was observed in ATL for taxonomic trials and in IPL for thematic trials. These results suggest taxonomic specialization in ATL and thematic specialization in IPL, consistent with the dual-hub account of semantic cognition.
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Affiliation(s)
- Melissa Thye
- Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | - Jason Geller
- Center for Cognitive Science, Rutgers University, Piscataway, NJ, USA
| | - Jerzy P Szaflarski
- Department of Neurology and the University of Alabama at Birmingham (UAB) Epilepsy Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Daniel Mirman
- Department of Psychology, University of Edinburgh, Edinburgh, UK
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27
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Collavini S, Fernández-Corazza M, Oddo S, Princich JP, Kochen S, Muravchik CH. Improvements on spatial coverage and focality of deep brain stimulation in pre-surgical epilepsy mapping. J Neural Eng 2021; 18. [PMID: 33578398 DOI: 10.1088/1741-2552/abe5b9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 02/12/2021] [Indexed: 12/20/2022]
Abstract
Objective.Electrical stimulation mapping (ESM) of the brain using stereo-electroencephalography (SEEG) intracranial electrodes, also known as depth-ESM (DESM), is being used as part of the pre-surgical planning for brain surgery in drug-resistant epilepsy patients. Typically, DESM consists in applying the electrical stimulation using adjacent contacts of the SEEG electrodes and in recording the EEG responses to those stimuli, giving valuable information of critical brain regions to better delimit the region to resect. However, the spatial extension or coverage of the stimulated area is not well defined even though the precise electrode locations can be determined from computed tomography images.Approach.We first conduct electrical simulations of DESM for different shapes of commercial SEEG electrodes showing the stimulation extensions for different intensities of injected current. We then evaluate the performance of DESM in terms of spatial coverage and focality on two realistic head models of real patients undergoing pre-surgical evaluation. We propose a novel strategy for DESM that consist in applying the current using contacts of different SEEG electrodes (x-DESM), increasing the versatility of DESM without implanting more electrodes. We also present a clinical case where x-DESM replicated the full semiology of an epilepsy seizure using a very low-intensity current injection, when typical adjacent DESM only reproduced partial symptoms with much larger intensities. Finally, we show one example of DESM optimal stimulation to achieve maximum intensity, maximum focality or intermediate solution at a pre-defined target, and one example of temporal interference in DESM capable of increasing focality in brain regions not immediately touching the electrode contacts.Main results.It is possible to define novel current injection patterns using contacts of different electrodes (x-DESM) that might improve coverage and/or focality, depending on the characteristics of the candidate brain. If individual simulations are not possible, we provide the estimated radius of stimulation as a function of the injected current and SEEG electrode brand as a reference for the community.Significance.Our results show that subject-specific electrical stimulations are a valuable tool to use in the pre-surgical planning to visualize the extension of the stimulated regions. The methods we present here are also applicable to pre-surgical planning of tumor resections and deep brain stimulation treatments.
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Affiliation(s)
- Santiago Collavini
- Research Institute of Electronics, Control and Signal Processing (LEICI), National University of La Plata-CONICET, Calle 116 s/n, La Plata B1900, Argentina.,Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina.,Institute of Engineering and Agronomy, National University Arturo Jauretche, Av. Calchaquí 6200, Florencio Varela, Buenos Aires 1888, Argentina
| | - Mariano Fernández-Corazza
- Research Institute of Electronics, Control and Signal Processing (LEICI), National University of La Plata-CONICET, Calle 116 s/n, La Plata B1900, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Silvia Oddo
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Juan Pablo Princich
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Silvia Kochen
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Carlos H Muravchik
- Research Institute of Electronics, Control and Signal Processing (LEICI), National University of La Plata-CONICET, Calle 116 s/n, La Plata B1900, Argentina.,Scientific Research Commission of the Province of Buenos Aires (CIC-PBA), Argentina
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28
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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: 0.8] [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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Chaitanya G, Romeo AK, Ilyas A, Irannejad A, Toth E, Elsayed G, Bentley JN, Riley KO, Pati S. Robot-assisted stereoelectroencephalography exploration of the limbic thalamus in human focal epilepsy: implantation technique and complications in the first 24 patients. Neurosurg Focus 2021; 48:E2. [PMID: 32234983 DOI: 10.3171/2020.1.focus19887] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 01/24/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Despite numerous imaging studies highlighting the importance of the thalamus in a patient's surgical prognosis, human electrophysiological studies involving the limbic thalamic nuclei are limited. The objective of this study was to evaluate the safety and accuracy of robot-assisted stereotactic electrode placement in the limbic thalamic nuclei of patients with suspected temporal lobe epilepsy (TLE). METHODS After providing informed consent, 24 adults with drug-resistant, suspected TLE undergoing evaluation with stereoelectroencephalography (SEEG) were enrolled in the prospective study. The trajectory of one electrode planned for clinical sampling of the operculoinsular cortex was modified to extend it to the thalamus, thereby preventing the need for additional electrode placement for research. The anterior nucleus of the thalamus (ANT) (n = 13) and the medial group of thalamic nuclei (MED) (n = 11), including the mediodorsal and centromedian nuclei, were targeted. The postimplantation CT scan was coregistered to the preoperative MR image, and Morel's thalamic atlas was used to confirm the accuracy of implantation. RESULTS Ten (77%) of 13 patients in the ANT group and 10 (91%) of 11 patients in the MED group had electrodes accurately placed in the thalamic nuclei. None of the patients had a thalamic hemorrhage. However, trace asymptomatic hemorrhages at the cortical-level entry site were noted in 20.8% of patients, who did not require additional surgical intervention. SEEG data from all the patients were interpretable and analyzable. The trajectories for the ANT implant differed slightly from those of the MED group at the entry point-i.e., the precentral gyrus in the former and the postcentral gyrus in the latter. CONCLUSIONS Using judiciously planned robot-assisted SEEG, the authors demonstrate the safety of electrophysiological sampling from various thalamic nuclei for research recordings, presenting a technique that avoids implanting additional depth electrodes or compromising clinical care. With these results, we propose that if patients are fully informed of the risks involved, there are potential benefits of gaining mechanistic insights to seizure genesis, which may help to develop neuromodulation therapies.
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Affiliation(s)
- Ganne Chaitanya
- 1Department of Neurology.,2Epilepsy and Cognitive Neurophysiology Laboratory, and
| | - Andrew K Romeo
- 3Department of Neurosurgery, University of Alabama at Birmingham, Alabama
| | - Adeel Ilyas
- 2Epilepsy and Cognitive Neurophysiology Laboratory, and.,3Department of Neurosurgery, University of Alabama at Birmingham, Alabama
| | - Auriana Irannejad
- 1Department of Neurology.,2Epilepsy and Cognitive Neurophysiology Laboratory, and
| | - Emilia Toth
- 1Department of Neurology.,2Epilepsy and Cognitive Neurophysiology Laboratory, and
| | - Galal Elsayed
- 3Department of Neurosurgery, University of Alabama at Birmingham, Alabama
| | - J Nicole Bentley
- 3Department of Neurosurgery, University of Alabama at Birmingham, Alabama
| | - Kristen O Riley
- 3Department of Neurosurgery, University of Alabama at Birmingham, Alabama
| | - Sandipan Pati
- 1Department of Neurology.,2Epilepsy and Cognitive Neurophysiology Laboratory, and
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Lin FH, Lee HJ, Ahveninen J, Jääskeläinen IP, Yu HY, Lee CC, Chou CC, Kuo WJ. Distributed source modeling of intracranial stereoelectro-encephalographic measurements. Neuroimage 2021; 230:117746. [PMID: 33454414 DOI: 10.1016/j.neuroimage.2021.117746] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/11/2020] [Accepted: 01/06/2021] [Indexed: 11/17/2022] Open
Abstract
Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈-3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.
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Affiliation(s)
- Fa-Hsuan Lin
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Hsin-Ju Lee
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Iiro P Jääskeläinen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation
| | - Hsiang-Yu Yu
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Cheng-Chia Lee
- Institute of Brain Science, Brain Research Center, National Yang-Ming University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chien-Chen Chou
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Wen-Jui Kuo
- Institute of Neuroscience, National Yang Ming University, Taipei, Taiwan; Brain Research Center, National Yang Ming University, Taipei, Taiwan.
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31
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Kam JWY, Helfrich RF, Solbakk AK, Endestad T, Larsson PG, Lin JJ, Knight RT. Top-Down Attentional Modulation in Human Frontal Cortex: Differential Engagement during External and Internal Attention. Cereb Cortex 2021; 31:873-883. [PMID: 33063100 DOI: 10.1093/cercor/bhaa262] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 08/18/2020] [Accepted: 08/18/2020] [Indexed: 12/19/2022] Open
Abstract
Decades of electrophysiological research on top-down control converge on the role of the lateral frontal cortex in facilitating attention to behaviorally relevant external inputs. However, the involvement of frontal cortex in the top-down control of attention directed to the external versus internal environment remains poorly understood. To address this, we recorded intracranial electrocorticography while subjects directed their attention externally to tones and responded to infrequent target tones, or internally to their own thoughts while ignoring the tones. Our analyses focused on frontal and temporal cortices. We first computed the target effect, as indexed by the difference in high frequency activity (70-150 Hz) between target and standard tones. Importantly, we then compared the target effect between external and internal attention, reflecting a top-down attentional effect elicited by task demands, in each region of interest. Both frontal and temporal cortices showed target effects during external and internal attention, suggesting this effect is present irrespective of attention states. However, only the frontal cortex showed an enhanced target effect during external relative to internal attention. These findings provide electrophysiological evidence for top-down attentional modulation in the lateral frontal cortex, revealing preferential engagement with external attention.
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Affiliation(s)
- Julia W Y Kam
- Department of Psychology, University of Calgary, Calgary AB T2N 1N4, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary AB T2N 4N1, Canada.,Helen Wills Neuroscience Institute, University of California - Berkeley, Berkeley, CA 94720, USA
| | - Randolph F Helfrich
- Center for Neurology, University Medical Center Tübingen, Tübingen 2669-72016, Germany.,Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Anne-Kristin Solbakk
- Department of Psychology, Faculty of Social Sciences, University of Oslo, 0317 Oslo, Norway.,Department of Neuropsychology, Helgeland Hospital, Oslo, 0317, Norway.,Department of Neurosurgery, Division of Clinical Neuroscience, Oslo University Hospital - Rikshospitalet, Oslo, 0450, Norway
| | - Tor Endestad
- Department of Psychology, Faculty of Social Sciences, University of Oslo, 0317 Oslo, Norway
| | - Pål G Larsson
- Department of Neurosurgery, Division of Clinical Neuroscience, Oslo University Hospital - Rikshospitalet, Oslo, 0450, Norway.,Department of Neurosurgery, Division of Surgery, Oslo University Hospital - Rikshospitalet, Oslo, 0450, Norway
| | - Jack J Lin
- Comprehensive Epilepsy Program, Department of Neurology, University of California - Irvine, Irvine, CA 92868, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California - Berkeley, Berkeley, CA 94720, USA.,Department of Psychology, University of California - Berkeley, Berkeley, CA 94720, USA
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Electrical stimulation of the medial orbitofrontal cortex in humans elicits pleasant olfactory perceptions. Epilepsy Behav 2021; 114:107559. [PMID: 33243684 DOI: 10.1016/j.yebeh.2020.107559] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/23/2020] [Accepted: 10/07/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Olfactory hallucinations can be part of epileptic seizures of orbitofrontal origin. Olfactory hallucinations, however, are rare and therefore the semiology, localization and lateralization characteristics are underdetermined. In addition, many discrepancies are found in the literature regarding olfactory processing and orbitofrontal (OF) functions and olfactory function. Particularly, the questions of laterality and affective component in coding of odors in the OF cortex remain controversial. AIMS This study explored whether cortical electrical stimulation of the OF and mesiotemporal brain can trigger olfactory hallucinations with special focus on olfactory percepts in terms of laterality and hedonics. MATERIALS AND METHODS Eight patients with temporal lobe epilepsy participated in the study, at the time of invasive exploration of their epilepsy. The most distal contact of the OF and anterior hippocampus depth electrodes were stimulated (50 Hz, 0.2 ms biphasic pulse; maximal stimulation 4 mA). Patients were instructed to report any kind of sensation they might experience. Intracranial depth electrodes were localized (iElectrodes): subject-specific brain mask, subcortical segmentation and cortical parcellation based on the Destrieux atlas (FreeSurfer) were superposed to the coregistered T1-weighted MRI and CT images (SPM). The center of mass of each electrode-artifact cluster determined the electrode localization. The electrode labeling was done in patient space. To obtain the electrode coordinates in Montreal Neurological Institute (MNI) space, the images obtained previously in the patient space were first segmented and normalized (SPM). Then, the localization procedure (iElectrodes) was run again with these new normalized images in MNI space. RESULTS No hallucination was evoked by stimulation, neither of the right nor the left hippocampus (8/8 patients). Pleasant olfactory hallucinations were evoked by OF stimulation in 5/8 patients in either hemisphere. Patients named the percept as the smell of lemon or coffee for example. Among those 5 patients, electrodes were localized in the cortex of the olfactory sulcus, medial orbital sulcus or medial OF gyrus. Increasing stimulation amplitude changed the olfactory percept identification in 3 out of those 5 patients. No affective judgement or change in perceived odor intensity was reported by the patients. No hallucination was evoked by the stimulation of the white matter of the medial OF brain in 3/8 patients independently of the hemisphere stimulated. CONCLUSIONS This study demonstrated that stimulation of the cortex of the medial OF brain and not of its white matter elicits specific pleasant olfactory hallucinations independently of the hemisphere stimulated, supporting one symmetrical olfactory processing in human.
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Toth E, Kumar S, Ganne C, Riley KO, Balasubramanian K, Pati S. Machine learning approach to detect focal-onset seizures in the human anterior nucleus of the thalamus. J Neural Eng 2020; 17. [PMID: 33059336 DOI: 10.1088/1741-2552/abc1b7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 10/15/2020] [Indexed: 01/03/2023]
Abstract
OBJECTIVE There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus- a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based features extracted from LFPs recorded from the anterior nucleus of the thalamus (ANT) can distinguish its state of ictal recruitment from other interictal states (including awake, sleep). APPROACH Two supervised machine learning tools (random forest and the random kitchen sink) were used to evaluate the performance of spectral (discrete wavelet transform-DWT), and time-domain (multiscale entropy-MSE) features in classifying seizures from interictal states in patients undergoing stereo EEG evaluation for epilepsy surgery. Under the supervision of IRB, field potentials were recorded from the ANT in consenting adults with drug-resistant temporal lobe epilepsy. Seizures were confirmed in the ANT using line-length and visual inspection. Wilcoxon rank-sum (WRS) method was used to test the differences in spectral patterns between seizure and interictal (awake and sleep) states. MAIN RESULTS 79 seizures (10 patients) and 158 segments (approx. 4 hours) of interictal stereo EEG data were analyzed. The mean seizure detection latencies with line length in the ANT varied between seizure types (range 5-34 seconds). However, the DWT and MSE in the ANT showed significant changes for all seizure types within the first 20 seconds after seizure onset. The random forest (accuracy 93.9 % and false-positive 4.6%) and the random kitchen sink (accuracy 97.3% and false-positive 1.8%) classified seizures and interictal states. SIGNIFICANCE These results suggest that features extracted from the thalamic LFPs can be trained to detect seizures that can be used for monitoring seizure counts and for closed-loop seizure abortive interventions.
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Affiliation(s)
- Emilia Toth
- University of Alabama School of Medicine, Birmingham, Alabama, UNITED STATES
| | - Sachin Kumar
- Centre for Computational Engineering and Networking , Amrita Vishwa Vidyapeetham Amrita School of Engineering, Coimbatore, Tamil Nadu, INDIA
| | - Chaitanya Ganne
- Neurology, University of Alabama at Birmingham, 1720 7th Ave S, Suite 405F, SPARKS building, Birmingham, UNITED STATES
| | - Kristen O Riley
- Neurosurgery, University of Alabama School of Medicine, Birmingham, Alabama, UNITED STATES
| | - Karthi Balasubramanian
- Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham Amrita School of Engineering, Coimbatore, Tamil Nadu, INDIA
| | - Sandipan Pati
- University of Alabama School of Medicine, Birmingham, Alabama, 35294-3412, UNITED STATES
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Foldes ST, Munter BT, Appavu BL, Kerrigan JF, Adelson PD. Shift in electrocorticography electrode locations after surgical implantation in children. Epilepsy Res 2020; 167:106410. [PMID: 32758670 DOI: 10.1016/j.eplepsyres.2020.106410] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/05/2020] [Accepted: 06/27/2020] [Indexed: 10/24/2022]
Abstract
Interpreting electrocorticography (ECoG) in the context of neuroimaging requires that multimodal information be integrated accurately. However, the implantation of ECoG electrodes can shift the brain impacting the spatial interpretation of electrode locations in the context of pre-implant imaging. We characterized the amount of shift in ECoG electrode locations immediately after implant in a pediatric population. Electrode-shift was quantified as the difference in the electrode locations immediately after surgery (via post-operation CT) compared to the brain surface before the operation (pre-implant T1 MRI). A total of 1140 ECoG contracts were assessed across 18 patients ranging from 3 to 19 (12.1 ± 4.8) years of age who underwent intracranial monitoring in preparation for epilepsy resection surgery. Patients had an average of 63 channels assessed with an average of 5.64 ± 3.27 mm shift from the pre-implant brain surface within 24 h of implant. This shift significantly increased with estimated intracranial volume, but not age. Shift also varied significantly depending of the lobe the contact was over; where contacts on the temporal and frontal lobe had less shift than the parietal. Furthermore, contacts on strips had significantly less shift than those on grids. The shift in the brain surface due to ECoG implantation could lead to a misinterpretation of contact location particularly in patients with larger intracranial volume and for grid contacts over the parietal lobes.
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Affiliation(s)
- Stephen T Foldes
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States.
| | - Bryce T Munter
- Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States
| | - Brian L Appavu
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - John F Kerrigan
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States
| | - P David Adelson
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
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Chaitanya G, Toth E, Pizarro D, Irannejad A, Riley K, Pati S. Precision mapping of the epileptogenic network with low- and high-frequency stimulation of anterior nucleus of thalamus. Clin Neurophysiol 2020; 131:2158-2167. [PMID: 32682244 DOI: 10.1016/j.clinph.2020.05.036] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/18/2020] [Accepted: 05/27/2020] [Indexed: 01/03/2023]
Abstract
OBJECTIVE The goal of thalamic deep brain stimulation in epilepsy is to engage and modulate the epileptogenic network. We demonstrate how the anterior nucleus of thalamus (ANT) stimulation engages the epileptogenic network using electrophysiological measures (gamma response and post-stimulation excitability). METHODS Five patients with suspected temporal lobe epilepsy syndrome, undergoing stereo-electroencephalography (SEEG), were enrolled in the IRB approved study to undergo recording and stimulation of the ANT. We analyzed the extent of gamma-band response (activation or suppression) and post-stimulation change in excitability in various cortical regions during low (10 Hz) and high (50 Hz) frequency stimulations. RESULTS 10 Hz stimulation increased cortical gamma, whereas 50 Hz stimulation suppressed the gamma responses. The maximum response to stimuli was in the hippocampus. High epileptogenicity regions were more susceptible to stimulation. Both 10-and 50 Hz stimulations decreased post-stimulation cortical excitability. The greater the gamma-band activation with 10 Hz stimulation, the greater was the decrease in post-stimulation excitability. CONCLUSIONS We define an EEG marker that delineates stimulation-specific nodal engagement. We proved that nodes that were engaged with the thalamus during stimulation were more likely to show a short term decrease in post-stimulation excitability. SIGNIFICANCE Patient-specific engagement patterns during stimulation can be mapped with SEEG that can be used to optimize stimulation parameters.
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Affiliation(s)
- Ganne Chaitanya
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA
| | - Emilia Toth
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA
| | - Diana Pizarro
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA
| | - Auriana Irannejad
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA
| | - Kristen Riley
- Department of Neurosurgery, University of Alabama at Birmingham, AL, USA
| | - Sandipan Pati
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA.
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Sonkusare S, Nguyen VT, Moran R, van der Meer J, Ren Y, Koussis N, Dionisio S, Breakspear M, Guo C. Intracranial-EEG evidence for medial temporal pole driving amygdala activity induced by multi-modal emotional stimuli. Cortex 2020; 130:32-48. [PMID: 32640373 DOI: 10.1016/j.cortex.2020.05.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 12/13/2022]
Abstract
The temporal pole (TP) is an associative cortical region required for complex cognitive functions such as social and emotional cognition. However, mapping the TP with functional magnetic resonance imaging is technically challenging and thus understanding its interaction with other key emotional circuitry, such as the amygdala, remains elusive. We exploited the unique advantages of stereo-electroencephalography (sEEG) to assess the responses of the TP and the amygdala during the perception of emotionally salient stimuli of pictures, music and movies. These stimuli consistently elicited high gamma responses (70-140 Hz) in both the TP and the amygdala, accompanied by functional connectivity in the low frequency range (2-12 Hz). Computational analyses suggested that the TP drove this effect in the theta frequency range, modulated by the emotional valence of the stimuli. Notably, cross-frequency analysis indicated the phase of theta oscillations in the TP modulated the amplitude of high gamma activity in the amygdala. These results were reproducible across three types of sensory inputs including naturalistic stimuli. Our results suggest that multimodal emotional stimuli induce a hierarchical influence of the TP over the amygdala.
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Affiliation(s)
- Saurabh Sonkusare
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Medicine, The University of Queensland, Brisbane, Australia.
| | - Vinh T Nguyen
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Rosalyn Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Yudan Ren
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Information Science and Technology, Northwest University, Xi'an, China
| | - Nikitas Koussis
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Sasha Dionisio
- Mater Advanced Epilepsy Unit, Mater Hospital, Brisbane, Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; Hunter Medical Research Institute, University of Newcastle, Newcastle, Australia.
| | - Christine Guo
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
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Chaitanya G, Toth E, Pizarro D, Iasemidis L, Murray TA, Riley K, Pati S. Acute modulation of the limbic network with low and high-frequency stimulation of the human fornix. Epilepsy Behav Rep 2020; 14:100363. [PMID: 32435756 PMCID: PMC7232081 DOI: 10.1016/j.ebr.2020.100363] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/25/2020] [Accepted: 03/26/2020] [Indexed: 12/11/2022] Open
Abstract
Targeted stimulation of white matter has opened newer perspectives in the field of neuromodulation, towards an attempt to improve memory or as a therapy for epilepsy. Stimulation of the fornix, being a part of the Papez circuit, is likely to modulate the limbic network excitability. However, the stimulation-frequency dependent variability in network excitability is unknown. In the case study, which involved stereo electroencephalographic (SEEG) recording of field potentials in a 48-year old left-handed woman with suspected temporal lobe epilepsy, we demonstrated the network effects of acute low (1 and 10 Hz) and high (50 Hz) frequency electrical stimulation of fornix. Mapping the short-latency evoked responses to forniceal stimulation confirmed the SEEG target localization within the Papez circuit. Low and high-frequency stimulation of the fornix produced opposite effects in the post-stimuli excitability, with the latter causing increased excitability in the limbic network that culminated in a clinical seizure. A distinct spectral peak around 8 Hz confirmed that sensing field potentials from the forniceal white matter is feasible. This is the first case study that provided an insight into how the temporal patterning of forniceal stimulation altered the downstream limbic network excitability.
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Affiliation(s)
- Ganne Chaitanya
- Epilepsy and Cognitive Neurophysiology Laboratory, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Emilia Toth
- Epilepsy and Cognitive Neurophysiology Laboratory, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Diana Pizarro
- Epilepsy and Cognitive Neurophysiology Laboratory, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Leonidas Iasemidis
- Center for Biomedical Engineering and Rehabilitation Science, Louisiana Tech Institute, Ruston, LA, USA
| | - Teresa A. Murray
- Center for Biomedical Engineering and Rehabilitation Science, Louisiana Tech Institute, Ruston, LA, USA
| | - Kristen Riley
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sandipan Pati
- Epilepsy and Cognitive Neurophysiology Laboratory, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
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Li G, Jiang S, Chen C, Brunner P, Wu Z, Schalk G, Chen L, Zhang D. iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes. J Neural Eng 2019; 17:016016. [PMID: 31658449 DOI: 10.1088/1741-2552/ab51a5] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes. APPROACH iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment. MAIN RESULTS All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes. SIGNIFICANCE iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.
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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. National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America
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Toth E, Chaitanya G, Pati S. Mapping short-latency cortical responses to electrical stimulation of thalamic motor nuclei by increasing sampling rate - A technical report. Clin Neurophysiol 2019; 131:142-144. [PMID: 31765977 DOI: 10.1016/j.clinph.2019.10.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 10/02/2019] [Indexed: 11/27/2022]
Affiliation(s)
- Emilia Toth
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA
| | - Ganne Chaitanya
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA
| | - Sandipan Pati
- Department of Neurology, University of Alabama at Birmingham, AL, USA; Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, AL, USA.
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Behncke J, Kern M, Ruescher J, Schulze-Bonhage A, Ball T. Probabilistic neuroanatomical assignment of intracranial electrodes using the ELAS toolbox. J Neurosci Methods 2019; 327:108396. [DOI: 10.1016/j.jneumeth.2019.108396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 07/18/2019] [Accepted: 08/06/2019] [Indexed: 10/26/2022]
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Default network and frontoparietal control network theta connectivity supports internal attention. Nat Hum Behav 2019; 3:1263-1270. [DOI: 10.1038/s41562-019-0717-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 07/31/2019] [Indexed: 11/08/2022]
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Pizarro D, Ilyas A, Chaitanya G, Toth E, Irannejad A, Romeo A, Riley KO, Iasemidis L, Pati S. Spectral organization of focal seizures within the thalamotemporal network. Ann Clin Transl Neurol 2019; 6:1836-1848. [PMID: 31468745 PMCID: PMC6764631 DOI: 10.1002/acn3.50880] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 01/08/2023] Open
Abstract
Objective To investigate dynamic changes in neural activity between the anterior nucleus of the thalamus (ANT) and the seizure onset zone (SOZ) in patients with drug‐resistant temporal lobe epilepsy (TLE) based on anatomic location, seizure subtype, and state of vigilance (SOV). Methods Eleven patients undergoing stereoelectroencephalography for seizure localization were recruited prospectively for local field potential (LFP) recording directly from the ANT. The SOZ was identified using line length and epileptogenicity index. Changes in power spectral density (PSD) were compared between the two anatomic sites as seizures (N = 53) transitioned from interictal baseline to the posttermination stage. Results At baseline, the thalamic LFPs were significantly lower and distinct from the SOZ with the presence of higher power in the fast ripple band (P < 0.001). Temporal changes in ictal power of neural activity within ANT mimic those of the SOZ, are increased significantly at seizure onset (P < 0.05), and are distinct for seizures that impaired awareness or that secondarily generalized (P < 0.05). The onset of seizure was preceded by a decrease in the mean power spectral density (PSD) in ANT and SOZ (P < 0.05). Neural activity correlated with different states of vigilance at seizure onset within the ANT but not in the SOZ (P = 0.005). Interpretation The ANT can be recruited at the onset of mesial temporal lobe seizures, and the recruitment pattern differs with seizure subtypes. Furthermore, changes in neural dynamics precede seizure onset and are widespread to involve temporo‐thalamic regions, thereby providing an opportunity to intervene early with closed‐loop DBS.
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Affiliation(s)
- Diana Pizarro
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adeel Ilyas
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ganne Chaitanya
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Emilia Toth
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Auriana Irannejad
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Andrew Romeo
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Kristen O Riley
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Leonidas Iasemidis
- Center for Biomedical Engineering and Rehabilitation Science, Louisiana Tech University, Ruston, Louisiana
| | - Sandipan Pati
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
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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: 108] [Impact Index Per Article: 18.0] [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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Mo JJ, Hu WH, Zhang C, Wang X, Liu C, Zhao BT, Zhou JJ, Zhang K. Motor cortex stimulation: a systematic literature-based analysis of effectiveness and case series experience. BMC Neurol 2019; 19:48. [PMID: 30925914 PMCID: PMC6440080 DOI: 10.1186/s12883-019-1273-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Accepted: 03/14/2019] [Indexed: 12/11/2022] Open
Abstract
Background Aim to quantitatively analyze the clinical effectiveness for motor cortex stimulation (MCS) to refractory pain. Methods The literatures were systematically searched in database of Cocharane library, Embase and PubMed, using relevant strategies. Data were extracted from eligible articles and pooled as mean with standard deviation (SD). Comparative analysis was measured by non-parametric t test and linear regression model. Results The pooled effect estimate from 12 trials (n = 198) elucidated that MCS shown the positive effect on refractory pain, and the total percentage improvement was 35.2% in post-stroke pain and 46.5% in trigeminal neuropathic pain. There is no statistical differences between stroke involved thalamus or non-thalamus. The improvement of plexus avulsion (29.8%) and phantom pain (34.1%) was similar. The highest improvement rate was seen in post-radicular plexopathy (65.1%) and MCS may aggravate the pain induced by spinal cord injury, confirmed by small sample size. Concurrently, Both the duration of disease (r = 0.233, p = 0.019*) and the time of follow-up (r = 0.196, p = 0.016*) had small predicative value, while age (p = 0.125) had no correlation to post-operative pain relief. Conclusions MCS is conducive to the patients with refractory pain. The duration of disease and the time of follow-up can be regarded as predictive factor. Meanwhile, further studies are needed to reveal the mechanism of MCS and to reevaluate the cost-benefit aspect with better-designed clinical trials. Electronic supplementary material The online version of this article (10.1186/s12883-019-1273-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jia-Jie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Wen-Han Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Bao-Tian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Jun-Jian Zhou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
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Medina Villalon S, Paz R, Roehri N, Lagarde S, Pizzo F, Colombet B, Bartolomei F, Carron R, Bénar CG. EpiTools, A software suite for presurgical brain mapping in epilepsy: Intracerebral EEG. J Neurosci Methods 2018; 303:7-15. [DOI: 10.1016/j.jneumeth.2018.03.018] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 03/05/2018] [Accepted: 03/28/2018] [Indexed: 11/16/2022]
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Branco MP, Gaglianese A, Glen DR, Hermes D, Saad ZS, Petridou N, Ramsey NF. ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids. J Neurosci Methods 2017; 301:43-51. [PMID: 29100838 DOI: 10.1016/j.jneumeth.2017.10.022] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 10/24/2017] [Indexed: 12/01/2022]
Abstract
BACKGROUND Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject's neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT. NEW METHOD ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual's cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab®, thus providing an easy, automated and standard tool for ECoG electrode localization. RESULTS ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method. COMPARISON WITH EXISTING METHODS A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects. CONCLUSIONS The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart.
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Affiliation(s)
- Mariana P Branco
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands
| | - Anna Gaglianese
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands; Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Daniel R Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
| | - Dora Hermes
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands; Department of Psychology, New York University, New York, NY, United States; Department of Psychology, Stanford University, Stanford, CA, United States
| | - Ziad S Saad
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
| | - Natalia Petridou
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands.
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Fujimoto A, Okanishi T, Kanai S, Sato K, Nishimura M, Enoki H. Neuronavigation-guided Frameless Stereoelectroencephalography (SEEG). Neurol Med Chir (Tokyo) 2017; 57:496-502. [PMID: 28768920 PMCID: PMC5638794 DOI: 10.2176/nmc.tn.2017-0110] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Stereoelectroencephalography (SEEG) is an invasive surgical procedure used to identify epileptogenic zones. The combination of both subdural grids and depth electrodes (DEs) is currently used for invasive intracranial monitoring in many epilepsy centers. To perform DE implantation, some centers use frame-based stereotactic techniques and others use stereotactic robotic techniques. However, not all epilepsy centers have access to these tools. We hypothesized that DE implantation using a neuronavigation system can be utilized for subsequent epilepsy surgery. Between April 2016 and April 2017, we performed invasive monitoring for 26 patients. Among these, 17 patients (8 females, 9 males; mean age, 21.2 years; range, 3–51 years) underwent DE implantation. We divided patients into three groups: Group 1 (7 patients), a free-hand implantation group; Group 2 (7 patients), a frameless stereotactic implantation group; and Group 3 (3 patients), a computed tomography (CT)-guided auto image registration system with the stereotactic implantation group. Group 3 showed the closest distance from planned target to DE tip, followed by Group 2. Fourteen of the 17 patients underwent subsequent epilepsy surgery referring to the results of DE studies. DE placement using a neuronavigation system without stereotactic robotic equipment or frame-based stereotactic techniques can be utilized for subsequent epilepsy surgery.
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Affiliation(s)
- Ayataka Fujimoto
- Seirei Hamamatsu General Hospital, Comprehensive Epilepsy Center
| | - Tohru Okanishi
- Seirei Hamamatsu General Hospital, Comprehensive Epilepsy Center
| | - Sotaro Kanai
- Seirei Hamamatsu General Hospital, Comprehensive Epilepsy Center
| | - Keishiro Sato
- Seirei Hamamatsu General Hospital, Comprehensive Epilepsy Center
| | | | - Hideo Enoki
- Seirei Hamamatsu General Hospital, Comprehensive Epilepsy Center
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