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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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Lucas A, Scheid BH, Pattnaik AR, Gallagher R, Mojena M, Tranquille A, Prager B, Gleichgerrcht E, Gong R, Litt B, Davis KA, Das S, Stein JM, Sinha N. iEEG-recon: A Fast and Scalable Pipeline for Accurate Reconstruction of Intracranial Electrodes and Implantable Devices. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.12.23291286. [PMID: 37398160 PMCID: PMC10312891 DOI: 10.1101/2023.06.12.23291286] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Background Collaboration between epilepsy centers is essential to integrate multimodal data for epilepsy research. Scalable tools for rapid and reproducible data analysis facilitate multicenter data integration and harmonization. Clinicians use intracranial EEG (iEEG) in conjunction with non-invasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. These tasks are still performed manually in many epilepsy centers. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. Methods We created iEEG-recon, a scalable electrode reconstruction pipeline for semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three modules: a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon, and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. Results We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography (ECoG) and stereoelectroencephalography (SEEG) cases with a 10 minute running time per case, and ~20 min for semi-automatic electrode labeling. iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and post-implant T1-MRI visual inspections. Our use of ANTsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely used Freesurfer segmentation. Discussion iEEG-recon is a valuable tool for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting efficient data analysis, and integration into clinical workflows. The tool's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io/en/latest/.
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Affiliation(s)
- Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Brittany H. Scheid
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Akash R. Pattnaik
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Ashley Tranquille
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Brian Prager
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
| | - Ezequiel Gleichgerrcht
- Department of Neurology, Medical University of South Carolina, Charleston, SC
- Emory University, Atlanta, GA
| | | | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Sandhitsu Das
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Joel M. Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
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SAMCOR: A robust and precise co-registration algorithm for brain CT and MR imaging. INTERDISCIPLINARY NEUROSURGERY 2022. [DOI: 10.1016/j.inat.2022.101637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Blenkmann AO, Solbakk AK, Ivanovic J, Larsson PG, Knight RT, Endestad T. Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms. Front Neuroinform 2022; 16:788685. [PMID: 36277477 PMCID: PMC9582989 DOI: 10.3389/fninf.2022.788685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance. Results We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods. Conclusion We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.
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Affiliation(s)
- Alejandro O. Blenkmann
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
| | - Anne-Kristin Solbakk
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | | | | | - Robert T. Knight
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Tor Endestad
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
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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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Liu Q, Wang J, Wang C, Wei F, Zhang C, Wei H, Ye X, Xu J. FreeSurfer and 3D Slicer-Assisted SEEG Implantation for Drug-Resistant Epilepsy. Front Neurorobot 2022; 16:848746. [PMID: 35295674 PMCID: PMC8918516 DOI: 10.3389/fnbot.2022.848746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 02/03/2022] [Indexed: 11/17/2022] Open
Abstract
Objective Our study aimed to develop an approach to improve the speed and resolution of cerebral-hemisphere and lesion modeling and evaluate the advantages and disadvantages of robot-assisted surgical planning software. Methods We applied both conventional robot planning software (method 1) and open-source auxiliary software (FreeSurfer and 3D Slicer; method 2) to model the brain and lesions in 19 patients with drug-resistant epilepsy. The patients' mean age at implantation was 21.4 years (range, 6–52 years). Each patient received an average of 12 electrodes (range, 9–16) between May and November 2021. The electrode-implantation plan was designed based on the models established using the two methods. We statistically analyzed and compared the duration of designing the models and planning the implantation using these two methods and performed the surgeries with the implantation plan designed using the auxiliary software. Results A significantly longer time was needed to reconstruct a cerebral-hemisphere model using method 1 (mean, 206 s) than using method 2 (mean, 20 s) (p < 0.05). Both methods identified a mean of 1.4 lesions (range, 1–5) in each patient. Overall, using method 1 required longer (mean, 130 s; range, 48–436) than using method 2 (mean, 68.1 s; range, 50–104; p < 0.05). In addition, the clarity of the model based on method 1 was lower than that based on method 2. To devise an electrode-implantation plan, it took 9.1–25.5 min (mean, 16) and 6.6–14.8 min (mean, 10.2) based on methods 1 and 2, respectively (p < 0.05). The average target point error of 231 electrodes amounted to 1.90 mm ± 0.37 mm (range, 0.33–3.61 mm). The average entry point error was 0.89 ± 0.26 mm (range, 0.17–1.67 mm). None of the patients presented with intracranial hemorrhage or infection, and no other serious complications were observed. Conclusions FreeSurfer and 3D Slicer-assisted SEEG implantation is an excellent approach to enhance modeling speed and resolution, shorten the electrode-implantation planning time, and boost the efficiency of clinical work. These well-known, trusted open-source programs do not have explicitly restricted licenses. These tools, therefore, seem well suited for clinical-research applications under the premise of approval by an ethics committee, informed consent of the patient, and clinical judgment of the surgeon.
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Affiliation(s)
- Qiangqiang Liu
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junjie Wang
- Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Changquan Wang
- Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Wei
- Wuhan Zhongke Industrial Research Institute of Medical Science Co., Ltd., Wuhan, China
| | - Chencheng Zhang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolai Ye
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiwen Xu
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Jiwen Xu
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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: 5] [Impact Index Per Article: 1.7] [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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Ervin B, Rozhkov L, Buroker J, Leach JL, Mangano FT, Greiner HM, Holland KD, Arya R. Fast Automated Stereo-EEG Electrode Contact Identification and Labeling Ensemble. Stereotact Funct Neurosurg 2021; 99:393-404. [PMID: 33849046 DOI: 10.1159/000515090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/02/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Stereotactic electroencephalography (SEEG) has emerged as the preferred modality for intracranial monitoring in drug-resistant epilepsy (DRE) patients being evaluated for neurosurgery. After implantation of SEEG electrodes, it is important to determine the neuroanatomic locations of electrode contacts (ECs), to localize ictal onset and propagation, and integrate functional information to facilitate surgical decisions. Although there are tools for coregistration of preoperative MRI and postoperative CT scans, identification, sorting, and labeling of SEEG ECs is often performed manually, which is resource intensive. We report development and validation of a software named Fast Automated SEEG Electrode Contact Identification and Labeling Ensemble (FASCILE). METHODS FASCILE is written in Python 3.8.3 and employs a novel automated method for identifying ECs, assigning them to respected SEEG electrodes, and labeling. We compared FASCILE with our clinical process of identifying, sorting, and labeling ECs, by computing localization error in anteroposterior, superoinferior, and lateral dimensions. We also measured mean Euclidean distances between ECs identified by FASCILE and the clinical method. We compared time taken for EC identification, sorting, and labeling for the software developer using FASCILE, a first-time clinical user using FASCILE, and the conventional clinical process. RESULTS Validation in 35 consecutive DRE patients showed a mean overall localization error of 0.73 ± 0.15 mm. FASCILE required 10.7 ± 5.5 min/patient for identifying, sorting, and labeling ECs by a first-time clinical user, compared to 3.3 ± 0.7 h/patient required for the conventional clinical process. CONCLUSION Given the accuracy, speed, and ease of use, we expect FASCILE to be used frequently for SEEG-driven epilepsy surgery. It is freely available for noncommercial use. FASCILE is specifically designed to expedite localization of ECs, assigning them to respective SEEG electrodes (sorting), and labeling them and not for coregistration of CT and MRI data as there are commercial software available for this purpose.
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Affiliation(s)
- Brian Ervin
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, USA
| | - Leonid Rozhkov
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Jason Buroker
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - James L Leach
- Division of Neuro-Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Francesco T Mangano
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.,Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Hansel M Greiner
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Katherine D Holland
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Ravindra Arya
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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Yazdani M, Reagan J, Kocher M, Antonucci M, Taylor J, Edwards J, Vandergrift WA, Spampinato MV. Safety of MRI in the localization of implanted intracranial electrodes for refractory epilepsy. J Neuroimaging 2021; 31:551-559. [PMID: 33783916 DOI: 10.1111/jon.12848] [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: 12/07/2020] [Revised: 01/27/2021] [Accepted: 02/17/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND PURPOSE This is an observational study to evaluate the safety of magnetic resonance imaging (MRI) to localize subdural grids and depth electrodes in patients with refractory epilepsy using a 1.5 Tesla MR scanner. METHODS We implemented an optimized MRI protocol providing adequate image quality for the assessment of subdural grids and depth electrodes, while minimizing the specific absorption rate (SAR). We reviewed all MRI studies performed in patients with subdural grids and depth electrodes between January 2010 and October 2018. Image quality was graded as acceptable or nonacceptable for the assessment of intracranial device positioning. We reviewed the medical record and any imaging obtained after intracranial implant removal for adverse event or complication occurring during and after the procedure. RESULTS Ninety-nine patients with refractory epilepsy underwent MRI scans using a magnetization-prepared rapid acquisition of gradient echo sequence and a transmit-receive head coil with depth electrodes and subdural grids in place. Two patients underwent two separate depth electrode implantations for a total of 101 procedures and MRI scans. No clinical adverse events were reported during or immediately after imaging. Image quality was graded as acceptable for 97 MRI scans. Review of follow-up CT and MRI studies after implant removal, available for 70 patients, did not demonstrate unexpected complications in 69 patients. CONCLUSION In our experience, a low SAR MRI protocol can be used to safely localize intracranial subdural grids and depth electrode in patients with refractory epilepsy.
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Affiliation(s)
- Milad Yazdani
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Justin Reagan
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Madison Kocher
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Michael Antonucci
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - James Taylor
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Jonathan Edwards
- Department of Neurosciences, Medical University of South Carolina, Charleston, SC
| | | | - Maria Vittoria Spampinato
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
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Erhardt JB, Lottner T, Pasluosta CF, Gessner I, Mathur S, Schuettler M, Bock M, Stieglitz T. Fabrication and validation of reference structures for the localization of subdural standard- and micro-electrodes in MRI. J Neural Eng 2020; 17:046044. [PMID: 32764195 DOI: 10.1088/1741-2552/abad7a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Report simple reference structure fabrication and validate the precise localization of subdural micro- and standard electrodes in magnetic resonance imaging (MRI) in phantom experiments. APPROACH Electrode contacts with diameters of 0.3 mm and 4 mm are localized in 1.5 T MRI using reference structures made of silicone and iron oxide nanoparticle doping. The precision of the localization procedure was assessed for several standard MRI sequences and implant orientations in phantom experiments and compared to common clinical localization procedures. MAIN RESULTS A localization precision of 0.41 ± 0.20 mm could be achieved for both electrode diameters compared to 1.46 ± 0.69 mm that was achieved for 4 mm standard electrode contacts localized using a common clinical standard method. The new reference structures are intrinsically bio-compatible, and they can be detected with currently available feature detection software so that a clinical implementation of this technology should be feasible. SIGNIFICANCE Neuropathologies are increasingly diagnosed and treated with subdural electrodes, where the exact localization of the electrode contacts with respect to the patient's cortical anatomy is a prerequisite for the procedure. Post-implantation electrode localization using MRI may be advantageous compared to the common alternative of CT-MRI image co-registration, as it avoids systematic localization errors associated with the co-registration itself, as well as brain shift and implant movement. Additionally, MRI provides superior soft tissue contrast for the identification of brain lesions without exposing the patient to ionizing radiation. Recent studies show that smaller electrodes and high-density electrode grids are ideal for clinical and research purposes, but the localization of these devices in MRI has not been demonstrated.
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Affiliation(s)
- Johannes B Erhardt
- Department of Microsystems Engineering-IMTEK, University of Freiburg, Freiburg, Germany. BrainLinks-BrainTools, Freiburg, Germany
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11
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Dellavale D, Urdapilleta E, Cámpora N, Velarde OM, Kochen S, Mato G. Two types of ictal phase-amplitude couplings in epilepsy patients revealed by spectral harmonicity of intracerebral EEG recordings. Clin Neurophysiol 2020; 131:1866-1885. [PMID: 32580114 DOI: 10.1016/j.clinph.2020.04.160] [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: 01/24/2020] [Revised: 03/27/2020] [Accepted: 04/05/2020] [Indexed: 01/04/2023]
Abstract
OBJECTIVE Spectral harmonicity of the ictal activity was analyzed regarding two clinically relevant aspects, (1) as a confounding factor producing 'spurious' phase-amplitude couplings (PAC) which may lead to wrong conclusions about the underlying ictal mechanisms, and (2) its role in how good PAC is in correspondence to the seizure onset zone (SOZ) classification performed by the epileptologists. METHODS PAC patterns observed in intracerebral electroencephalography (iEEG) recordings were retrospectively studied during seizures of seven patients with pharmacoresistant focal epilepsy. The time locked index (TLI) measure was introduced to quantify the degree of harmonicity between frequency bands associated to the emergence of PAC during epileptic seizures. RESULTS (1) Harmonic and non harmonic PAC patterns coexist during the seizure dynamics in iEEG recordings with macroelectrodes. (2) Harmonic PAC patterns are an emergent property of the periodic non sinusoidal waveform constituting the epileptiform activity. (3) The TLI metric allows to distinguish the non harmonic PAC pattern, which has been previously associated with the ictal core through the paroxysmal depolarizing shifts mechanism of seizure propagation. CONCLUSIONS Our results suggest that the spectral harmonicity of the ictal activity plays a relevant role in the visual analysis of the iEEG recordings performed by the epileptologists to define the SOZ, and that it should be considered for the proper interpretation of ictal mechanisms. SIGNIFICANCE The proposed harmonicity analysis can be used to improve the delineation of the SOZ by reliably identifying non harmonic PAC patterns emerging from fully recruited cortical and subcortical areas.
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Affiliation(s)
- Damián Dellavale
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina.
| | - Eugenio Urdapilleta
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina
| | - Nuria Cámpora
- Neurosciences and Complex Systems Unit (EnyS), El Cruce "N. Kirchner" Hosp., UNAJ, Epilepsy Center, "R. Mejía" Hosp., Faculty of Medicine, UBA, CONICET, Argentina
| | - Osvaldo Matías Velarde
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina
| | - Silvia Kochen
- Neurosciences and Complex Systems Unit (EnyS), El Cruce "N. Kirchner" Hosp., UNAJ, Epilepsy Center, "R. Mejía" Hosp., Faculty of Medicine, UBA, CONICET, Argentina.
| | - Germán Mato
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina.
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12
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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: 17] [Impact Index Per Article: 3.4] [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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13
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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: 9.3] [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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Jiang X, Shamie I, K Doyle W, Friedman D, Dugan P, Devinsky O, Eskandar E, Cash SS, Thesen T, Halgren E. Replay of large-scale spatio-temporal patterns from waking during subsequent NREM sleep in human cortex. Sci Rep 2017; 7:17380. [PMID: 29234075 PMCID: PMC5727134 DOI: 10.1038/s41598-017-17469-w] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 11/27/2017] [Indexed: 01/20/2023] Open
Abstract
Animal studies support the hypothesis that in slow-wave sleep, replay of waking neocortical activity under hippocampal guidance leads to memory consolidation. However, no intracranial electrophysiological evidence for replay exists in humans. We identified consistent sequences of population firing peaks across widespread cortical regions during complete waking periods. The occurrence of these “Motifs” were compared between sleeps preceding the waking period (“Sleep-Pre”) when the Motifs were identified, and those following (“Sleep-Post”). In all subjects, the majority of waking Motifs (most of which were novel) had more matches in Sleep-Post than in Sleep-Pre. In rodents, hippocampal replay occurs during local sharp-wave ripples, and the associated neocortical replay tends to occur during local sleep spindles and down-to-up transitions. These waves may facilitate consolidation by sequencing cell-firing and encouraging plasticity. Similarly, we found that Motifs were coupled to neocortical spindles, down-to-up transitions, theta bursts, and hippocampal sharp-wave ripples. While Motifs occurring during cognitive task performance were more likely to have more matches in subsequent sleep, our studies provide no direct demonstration that the replay of Motifs contributes to consolidation. Nonetheless, these results confirm a core prediction of the dominant neurobiological theory of human memory consolidation.
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Affiliation(s)
- Xi Jiang
- Neurosciences Graduate Program, University of California at San Diego, La Jolla, CA, 92093, USA.
| | - Isaac Shamie
- Department of Radiology, University of California at San Diego, La Jolla, CA, 92093, USA
| | - Werner K Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, St George's, NY, 10016, USA
| | - Daniel Friedman
- Comprehensive Epilepsy Center, New York University School of Medicine, St George's, NY, 10016, USA
| | - Patricia Dugan
- Comprehensive Epilepsy Center, New York University School of Medicine, St George's, NY, 10016, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, St George's, NY, 10016, USA
| | - Emad Eskandar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Thomas Thesen
- Comprehensive Epilepsy Center, New York University School of Medicine, St George's, NY, 10016, USA.,Department of Physiology & Neuroscience, St. George's University, West Indies, Grenada
| | - Eric Halgren
- Department of Radiology, University of California at San Diego, La Jolla, CA, 92093, USA. .,Department of Neurosciences, University of California at San Diego, La Jolla, CA, 92093, USA.
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15
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Blenkmann AO, Phillips HN, Princich JP, Rowe JB, Bekinschtein TA, Muravchik CH, Kochen S. iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization. Front Neuroinform 2017; 11:14. [PMID: 28303098 PMCID: PMC5333374 DOI: 10.3389/fninf.2017.00014] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 02/01/2017] [Indexed: 01/03/2023] Open
Abstract
The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes. Here we present iElectrodes, an open-source toolbox that provides robust and accurate semi-automatic localization of both subdural grids and depth electrodes. Using pre- and post-implantation images, the method takes 2–3 min to localize the coordinates in each electrode array and automatically number the electrodes. The proposed pre-processing pipeline allows one to work in a normalized space and to automatically obtain anatomical labels of the localized electrodes without neuroimaging experts. We validated the method with data from 22 patients implanted with a total of 1,242 electrodes. We show that localization distances were within 0.56 mm of those achieved by experienced manual evaluators. iElectrodes provided additional advantages in terms of robustness (even with severe perioperative cerebral distortions), speed (less than half the operator time compared to expert manual localization), simplicity, utility across multiple electrode types (surface and depth electrodes) and all brain regions.
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Affiliation(s)
- Alejandro O Blenkmann
- FRONT Neurolab, Department of Psychology, University of OsloOslo, Norway; Estudios de Neurociencias y Sistemas Complejos, CONICET- El Cruce Hospital - Universidad Nacional Arturo JauretcheBuenos Aires, Argentina; Institute of Cellular Biology and Neuroscience "Prof E. De Robertis," School of Medicine, University of Buenos Aires - CONICETBuenos Aires, Argentina; Epilepsy Section, Division of Neurology, Ramos Mejía HospitalBuenos Aires, Argentina
| | - Holly N Phillips
- Department of Clinical Neurosciences, University of CambridgeCambridge, UK; MRC Cognition and Brain Sciences UnitCambridge, UK
| | - Juan P Princich
- Estudios de Neurociencias y Sistemas Complejos, CONICET- El Cruce Hospital - Universidad Nacional Arturo Jauretche Buenos Aires, Argentina
| | - James B Rowe
- Department of Clinical Neurosciences, University of CambridgeCambridge, UK; MRC Cognition and Brain Sciences UnitCambridge, UK
| | | | - Carlos H Muravchik
- Facultad de Ingeniería, Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales, Universidad Nacional de La Plata La Plata, Argentina
| | - Silvia Kochen
- Estudios de Neurociencias y Sistemas Complejos, CONICET- El Cruce Hospital - Universidad Nacional Arturo JauretcheBuenos Aires, Argentina; Institute of Cellular Biology and Neuroscience "Prof E. De Robertis," School of Medicine, University of Buenos Aires - CONICETBuenos Aires, Argentina; Epilepsy Section, Division of Neurology, Ramos Mejía HospitalBuenos Aires, Argentina
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Narizzano M, Arnulfo G, Ricci S, Toselli B, Tisdall M, Canessa A, Fato MM, Cardinale F. SEEG assistant: a 3DSlicer extension to support epilepsy surgery. BMC Bioinformatics 2017; 18:124. [PMID: 28231759 PMCID: PMC5324222 DOI: 10.1186/s12859-017-1545-8] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Accepted: 02/13/2017] [Indexed: 11/10/2022] Open
Abstract
Background In the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist’s workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors. Results In this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing. Conclusions The SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools.
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Affiliation(s)
- Massimo Narizzano
- Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.
| | - Serena Ricci
- Departement of Physiology Pharmacology and Neuroscience, CUNY Medical School, New York, New York, USA
| | - Benedetta Toselli
- Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy
| | - Martin Tisdall
- Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond St, WC1N 3JH, London, UK
| | - Andrea Canessa
- Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy
| | - Marco Massimo Fato
- Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy
| | - Francesco Cardinale
- "Claudio Munari" Center for Epilepsy Surgery, Niguarda Hospital, Milan, Italy
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17
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Qin C, Tan Z, Pan Y, Li Y, Wang L, Ren L, Zhou W, Wang L. Automatic and Precise Localization and Cortical Labeling of Subdural and Depth Intracranial Electrodes. Front Neuroinform 2017; 11:10. [PMID: 28261083 PMCID: PMC5314105 DOI: 10.3389/fninf.2017.00010] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 01/24/2017] [Indexed: 11/13/2022] Open
Abstract
Object: Subdural or deep intracerebral electrodes are essential in order to precisely localize epileptic zone in patients with medically intractable epilepsy. Precise localization of the implanted electrodes is critical to clinical diagnosing and treatment as well as for scientific studies. In this study, we sought to automatically and precisely extract intracranial electrodes using pre-operative MRI and post-operative CT images. Method: The subdural and depth intracranial electrodes were readily detected using clustering-based segmentation. Depth electrodes were tracked by fitting a quadratic curve to account for potential bending during the neurosurgery. The identified electrodes can be manipulated using a graphic interface and labeled to cortical areas in individual native space based on anatomical parcellation and displayed in the volume and surface space. Results: The electrodes' localizations were validated with high precision. The electrode coordinates were normalized to a standard space. Moreover, the probabilistic value being to a specific area or a functional network was provided. Conclusions: We developed an integrative toolbox to reconstruct and label the intracranial electrodes implanted in the patients with medically intractable epilepsy. This toolbox provided a convenient way to allow inter-subject comparisons and relation of intracranial EEG findings to the larger body of neuroimaging literature.
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Affiliation(s)
- Chaoyi Qin
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of PsychologyBeijing, China; Department of Psychology, University of Chinese Academy of SciencesBeijing, China
| | - Zheng Tan
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of PsychologyBeijing, China; Department of Psychology, University of Chinese Academy of SciencesBeijing, China
| | - Yali Pan
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of PsychologyBeijing, China; Department of Psychology, University of Chinese Academy of SciencesBeijing, China
| | - Yanyan Li
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology Beijing, China
| | - Lin Wang
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of PsychologyBeijing, China; Department of Psychology, University of Chinese Academy of SciencesBeijing, China
| | - Liankun Ren
- Beijing Key Laboratory of Neuromodulation, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University Beijing, China
| | - Wenjing Zhou
- Epilepsy Center of Yuquan Hospital, Tsinghua University Beijing, China
| | - Liang Wang
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of PsychologyBeijing, China; Department of Psychology, University of Chinese Academy of SciencesBeijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence TechnologyShanghai, China
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18
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LaPlante RA, Tang W, Peled N, Vallejo DI, Borzello M, Dougherty DD, Eskandar EN, Widge AS, Cash SS, Stufflebeam SM. The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes. Int J Comput Assist Radiol Surg 2016; 12:1829-1837. [PMID: 27915398 DOI: 10.1007/s11548-016-1504-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 11/07/2016] [Indexed: 10/20/2022]
Abstract
PURPOSE Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. METHODS We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. RESULTS We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. CONCLUSIONS The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection.
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Affiliation(s)
- Roan A LaPlante
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA, USA.
| | - Wei Tang
- Department of Neurobiology and Anatomy, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Noam Peled
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Deborah I Vallejo
- Cortical Physiology Laboratory, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Mia Borzello
- Cortical Physiology Laboratory, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Emad N Eskandar
- Department of Neurological Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sydney S Cash
- Cortical Physiology Laboratory, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Harvard-MIT Health Sciences and Technology, Cambridge, MA, USA
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Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography. Cortex 2016; 82:192-205. [PMID: 27389803 PMCID: PMC4981429 DOI: 10.1016/j.cortex.2016.05.001] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 03/08/2016] [Accepted: 05/02/2016] [Indexed: 11/20/2022]
Abstract
We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical 'nodes' in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography - ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.
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Localization of Metal Electrodes in the Intact Rat Brain Using Registration of 3D Microcomputed Tomography Images to a Magnetic Resonance Histology Atlas. eNeuro 2015; 2. [PMID: 26322331 PMCID: PMC4550316 DOI: 10.1523/eneuro.0017-15.2015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Simultaneous neural recordings taken from multiple areas of the rodent brain are garnering growing interest due to the insight they can provide about spatially distributed neural circuitry. The promise of such recordings has inspired great progress in methods for surgically implanting large numbers of metal electrodes into intact rodent brains. However, methods for localizing the precise location of these electrodes have remained severely lacking. Traditional histological techniques that require slicing and staining of physical brain tissue are cumbersome, and become increasingly impractical as the number of implanted electrodes increases. Here we solve these problems by describing a method that registers 3-D computerized tomography (CT) images of intact rat brains implanted with metal electrode bundles to a Magnetic Resonance Imaging Histology (MRH) Atlas. Our method allows accurate visualization of each electrode bundle's trajectory and location without removing the electrodes from the brain or surgically implanting external markers. In addition, unlike physical brain slices, once the 3D images of the electrode bundles and the MRH atlas are registered, it is possible to verify electrode placements from many angles by "re-slicing" the images along different planes of view. Further, our method can be fully automated and easily scaled to applications with large numbers of specimens. Our digital imaging approach to efficiently localizing metal electrodes offers a substantial addition to currently available methods, which, in turn, may help accelerate the rate at which insights are gleaned from rodent network neuroscience.
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Arnulfo G, Narizzano M, Cardinale F, Fato MM, Palva JM. Automatic segmentation of deep intracerebral electrodes in computed tomography scans. BMC Bioinformatics 2015; 16:99. [PMID: 25887573 PMCID: PMC4393625 DOI: 10.1186/s12859-015-0511-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 02/24/2015] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Invasive monitoring of brain activity by means of intracerebral electrodes is widely practiced to improve pre-surgical seizure onset zone localization in patients with medically refractory seizures. Stereo-Electroencephalography (SEEG) is mainly used to localize the epileptogenic zone and a precise knowledge of the location of the electrodes is expected to facilitate the recordings interpretation and the planning of resective surgery. However, the localization of intracerebral electrodes on post-implant acquisitions is usually time-consuming (i.e., manual segmentation), it requires advanced 3D visualization tools, and it needs the supervision of trained medical doctors in order to minimize the errors. In this paper we propose an automated segmentation algorithm specifically designed to segment SEEG contacts from a thresholded post-implant Cone-Beam CT volume (0.4 mm, 0.4 mm, 0.8 mm). The algorithm relies on the planned position of target and entry points for each electrode as a first estimation of electrode axis. We implemented the proposed algorithm into DEETO, an open source C++ prototype based on ITK library. RESULTS We tested our implementation on a cohort of 28 subjects in total. The experimental analysis, carried out over a subset of 12 subjects (35 multilead electrodes; 200 contacts) manually segmented by experts, show that the algorithm: (i) is faster than manual segmentation (i.e., less than 1s/subject versus a few hours) (ii) is reliable, with an error of 0.5 mm ± 0.06 mm, and (iii) it accurately maps SEEG implants to their anatomical regions improving the interpretability of electrophysiological traces for both clinical and research studies. Moreover, using the 28-subject cohort we show here that the algorithm is also robust (error < 0.005 mm) against deep-brain displacements (< 12 mm) of the implanted electrode shaft from those planned before surgery. CONCLUSIONS Our method represents, to the best of our knowledge, the first automatic algorithm for the segmentation of SEEG electrodes. The method can be used to accurately identify the neuroanatomical loci of SEEG electrode contacts by a non-expert in a fast and reliable manner.
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Affiliation(s)
- Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics and System Engineering - DIBRIS, University of Genoa, Viale Causa 13, Genoa, Italy. .,Neuroscience Center, University of Helsinki, P.O. Box 56 (Viikinkaari 4) FI-00014, Helsinki, Finland.
| | - Massimo Narizzano
- Department of Informatics, Bioengineering, Robotics and System Engineering - DIBRIS, University of Genoa, Viale Causa 13, Genoa, Italy.
| | - Francesco Cardinale
- C. Munari Centre for Epilepsy Surgery, Niguarda Hospital, Piazza Ospedale Maggiore 3, Milano, Italy.
| | - Marco Massimo Fato
- Department of Informatics, Bioengineering, Robotics and System Engineering - DIBRIS, University of Genoa, Viale Causa 13, Genoa, Italy.
| | - Jaakko Matias Palva
- Neuroscience Center, University of Helsinki, P.O. Box 56 (Viikinkaari 4) FI-00014, Helsinki, Finland.
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