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Fonseca N, Bowerman J, Askari P, Proskovec AL, Feltrin FS, Veltkamp D, Early H, Wagner BC, Davenport EM, Maldjian JA. Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing. J Imaging 2024; 10:80. [PMID: 38667978 PMCID: PMC11051542 DOI: 10.3390/jimaging10040080] [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: 02/16/2024] [Revised: 03/19/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.
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Affiliation(s)
- N.C.d. Fonseca
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jason Bowerman
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Pegah Askari
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Arlington, Arlington, TX 76019, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Amy L. Proskovec
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Fabricio Stewan Feltrin
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Daniel Veltkamp
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Heather Early
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ben C. Wagner
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Elizabeth M. Davenport
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Joseph A. Maldjian
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Kobayashi K, Taylor KN, Shahabi H, Krishnan B, Joshi A, Mackow MJ, Feldman L, Zamzam O, Medani T, Bulacio J, Alexopoulos AV, Najm I, Bingaman W, Leahy RM, Nair DR. Effective connectivity relates seizure outcome to electrode placement in responsive neurostimulation. Brain Commun 2024; 6:fcae035. [PMID: 38390255 PMCID: PMC10882982 DOI: 10.1093/braincomms/fcae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 09/06/2023] [Accepted: 02/19/2024] [Indexed: 02/24/2024] Open
Abstract
Responsive neurostimulation is a closed-loop neuromodulation therapy for drug resistant focal epilepsy. Responsive neurostimulation electrodes are placed near ictal onset zones so as to enable detection of epileptiform activity and deliver electrical stimulation. There is no standard approach for determining the optimal placement of responsive neurostimulation electrodes. Clinicians make this determination based on presurgical tests, such as MRI, EEG, magnetoencephalography, ictal single-photon emission computed tomography and intracranial EEG. Currently functional connectivity measures are not being used in determining the placement of responsive neurostimulation electrodes. Cortico-cortical evoked potentials are a measure of effective functional connectivity. Cortico-cortical evoked potentials are generated by direct single-pulse electrical stimulation and can be used to investigate cortico-cortical connections in vivo. We hypothesized that the presence of high amplitude cortico-cortical evoked potentials, recorded during intracranial EEG monitoring, near the eventual responsive neurostimulation contact sites is predictive of better outcomes from its therapy. We retrospectively reviewed 12 patients in whom cortico-cortical evoked potentials were obtained during stereoelectroencephalography evaluation and subsequently underwent responsive neurostimulation therapy. We studied the relationship between cortico-cortical evoked potentials, the eventual responsive neurostimulation electrode locations and seizure reduction. Directional connectivity indicated by cortico-cortical evoked potentials can categorize stereoelectroencephalography electrodes as either receiver nodes/in-degree (an area of greater inward connectivity) or projection nodes/out-degree (greater outward connectivity). The follow-up period for seizure reduction ranged from 1.3-4.8 years (median 2.7) after responsive neurostimulation therapy started. Stereoelectroencephalography electrodes closest to the eventual responsive neurostimulation contact site tended to show larger in-degree cortico-cortical evoked potentials, especially for the early latency cortico-cortical evoked potentials period (10-60 ms period) in six out of 12 patients. Stereoelectroencephalography electrodes closest to the responsive neurostimulation contacts (≤5 mm) also had greater significant out-degree in the early cortico-cortical evoked potentials latency period than those further away (≥10 mm) (P < 0.05). Additionally, significant correlation was noted between in-degree cortico-cortical evoked potentials and greater seizure reduction with responsive neurostimulation therapy at its most effective period (P < 0.05). These findings suggest that functional connectivity determined by cortico-cortical evoked potentials may provide additional information that could help guide the optimal placement of responsive neurostimulation electrodes.
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Affiliation(s)
- Katsuya Kobayashi
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Kenneth N Taylor
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Hossein Shahabi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Balu Krishnan
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Michael J Mackow
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Lauren Feldman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Omar Zamzam
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Juan Bulacio
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | | | - Imad Najm
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - William Bingaman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Dileep R Nair
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
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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: 72] [Impact Index Per Article: 24.0] [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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Zheng B, Hsieh B, Rex N, Lauro PM, Collins SA, Blum AS, Roth JL, Ayub N, Asaad WF. A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy. Hum Brain Mapp 2022; 43:4852-4863. [PMID: 35851977 PMCID: PMC9582372 DOI: 10.1002/hbm.26017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 11/17/2022] Open
Abstract
Stereotactic electroencephalography (SEEG) is an increasingly utilized method for invasive monitoring in patients with medically intractable epilepsy. Yet, the lack of standardization for labeling electrodes hinders communication among clinicians. A rational clustering of contacts based on anatomy rather than arbitrary physical leads may help clinical neurophysiologists interpret seizure networks. We identified SEEG electrodes on post‐implant CTs and registered them to preoperative MRIs segmented according to an anatomical atlas. Individual contacts were automatically assigned to anatomical areas independent of lead. These contacts were then organized using a hierarchical anatomical schema for display and interpretation. Bipolar‐referenced signal cross‐correlations were used to compare the similarity of grouped signals within a conventional montage versus this anatomical montage. As a result, we developed a hierarchical organization for SEEG contacts using well‐accepted, free software that is based solely on their post‐implant anatomical location. When applied to three example SEEG cases for epilepsy, clusters of contacts that were anatomically related collapsed into standardized groups. Qualitatively, seizure events organized using this framework were better visually clustered compared to conventional schemes. Quantitatively, signals grouped by anatomical region were more similar to each other than electrode‐based groups as measured by Pearson correlation. Further, we uploaded visualizations of SEEG reconstructions into the electronic medical record, rendering them durably useful given the interpretable electrode labels. In conclusion, we demonstrate a standardized, anatomically grounded approach to the organization of SEEG neuroimaging and electrophysiology data that may enable improved communication among and across surgical epilepsy teams and promote a clearer view of individual seizure networks.
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Affiliation(s)
- Bryan Zheng
- Department of Neurosurgery Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Ben Hsieh
- Department of Diagnostic Imaging Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Nathaniel Rex
- Department of Diagnostic Imaging Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Peter M. Lauro
- Department of Neurosurgery Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Scott A. Collins
- Department of Diagnostic Imaging Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Andrew S. Blum
- Department of Neurology Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Julie L. Roth
- Department of Neurology Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Neishay Ayub
- Department of Neurology Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Wael F. Asaad
- Department of Neurosurgery Warren Alpert Medical School, Brown University Providence Rhode Island USA
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Joshi AA, Choi S, Liu Y, Chong M, Sonkar G, Gonzalez-Martinez J, Nair D, Wisnowski JL, Haldar JP, Shattuck DW, Damasio H, Leahy RM. A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI. J Neurosci Methods 2022; 374:109566. [PMID: 35306036 PMCID: PMC9302382 DOI: 10.1016/j.jneumeth.2022.109566] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 12/23/2021] [Accepted: 03/13/2022] [Indexed: 11/17/2022]
Abstract
We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.
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Affiliation(s)
- Anand A. Joshi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA,Correspondence to: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, 3740 McClintock Avenue, EEB 426, Los Angeles, CA 90089-2560. (A.A. Joshi)
| | - Soyoung Choi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA,Neuroscience Graduate Program, University of Southern California, Los Angeles, USA
| | - Yijun Liu
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - Minqi Chong
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - Gaurav Sonkar
- Dept. of Computer Science, National Institute of Technology Warangal, India
| | | | - Dileep Nair
- Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Jessica L. Wisnowski
- Dornsife Cognitive Neuroscience Imaging Center, University of Southern California, Los Angles, USA
| | - Justin P. Haldar
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - David W. Shattuck
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, CA, USA
| | - Hanna Damasio
- Dornsife Cognitive Neuroscience Imaging Center, University of Southern California, Los Angles, USA
| | - Richard M. Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
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Taylor KN, Joshi AA, Hirfanoglu T, Grinenko O, Liu P, Wang X, Gonzalez‐Martinez JA, Leahy RM, Mosher JC, Nair DR. Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy. Epilepsia Open 2021; 6:493-503. [PMID: 34033267 PMCID: PMC8408609 DOI: 10.1002/epi4.12499] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/18/2021] [Accepted: 04/10/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Stereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D-segmented cortex and coregistered postoperative CT images. METHOD Stereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review. RESULTS Anatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas-based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method. SIGNIFICANCE The method for semi-automated atlas-based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.
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Affiliation(s)
| | - Anand A. Joshi
- Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Tugba Hirfanoglu
- Epilepsy CenterNeurological InstituteCleveland ClinicClevelandOHUSA
- Department of Pediatric NeurologyGazi University School of MedicineAnkaraTurkey
| | | | - Ping Liu
- Epilepsy CenterNeurological InstituteCleveland ClinicClevelandOHUSA
| | - Xiaofeng Wang
- Epilepsy CenterNeurological InstituteCleveland ClinicClevelandOHUSA
| | - Jorge A. Gonzalez‐Martinez
- Department of Neurological Surgery and Epilepsy CenterUniversity of Pittsburgh Medical CenterPittsburghPAUSA
| | - Richard M. Leahy
- Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
| | - John C. Mosher
- Department of NeurologyMcGovern Medical SchoolUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Dileep R. Nair
- Epilepsy CenterNeurological InstituteCleveland ClinicClevelandOHUSA
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