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Manero A, Rivera V, Fu Q, Schwartzman JD, Prock-Gibbs H, Shah N, Gandhi D, White E, Crawford KE, Coathup MJ. Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation. Bioengineering (Basel) 2024; 11:695. [PMID: 39061777 PMCID: PMC11274085 DOI: 10.3390/bioengineering11070695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 07/04/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
As both the proportion of older people and the length of life increases globally, a rise in age-related degenerative diseases, disability, and prolonged dependency is projected. However, more sophisticated biomedical materials, as well as an improved understanding of human disease, is forecast to revolutionize the diagnosis and treatment of conditions ranging from osteoarthritis to Alzheimer's disease as well as impact disease prevention. Another, albeit quieter, revolution is also taking place within society: human augmentation. In this context, humans seek to improve themselves, metamorphosing through self-discipline or more recently, through use of emerging medical technologies, with the goal of transcending aging and mortality. In this review, and in the pursuit of improved medical care following aging, disease, disability, or injury, we first highlight cutting-edge and emerging materials-based neuroprosthetic technologies designed to restore limb or organ function. We highlight the potential for these technologies to be utilized to augment human performance beyond the range of natural performance. We discuss and explore the growing social movement of human augmentation and the idea that it is possible and desirable to use emerging technologies to push the boundaries of what it means to be a healthy human into the realm of superhuman performance and intelligence. This potential future capability is contrasted with limitations in the right-to-repair legislation, which may create challenges for patients. Now is the time for continued discussion of the ethical strategies for research, implementation, and long-term device sustainability or repair.
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Affiliation(s)
- Albert Manero
- Limbitless Solutions, University of Central Florida, 12703 Research Parkway, Suite 100, Orlando, FL 32826, USA (V.R.)
- Biionix Cluster, University of Central Florida, Orlando, FL 32827, USA; (Q.F.); (K.E.C.)
| | - Viviana Rivera
- Limbitless Solutions, University of Central Florida, 12703 Research Parkway, Suite 100, Orlando, FL 32826, USA (V.R.)
| | - Qiushi Fu
- Biionix Cluster, University of Central Florida, Orlando, FL 32827, USA; (Q.F.); (K.E.C.)
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Jonathan D. Schwartzman
- College of Medicine, University of Central Florida, Orlando, FL 32827, USA; (J.D.S.); (H.P.-G.); (N.S.); (D.G.); (E.W.)
| | - Hannah Prock-Gibbs
- College of Medicine, University of Central Florida, Orlando, FL 32827, USA; (J.D.S.); (H.P.-G.); (N.S.); (D.G.); (E.W.)
| | - Neel Shah
- College of Medicine, University of Central Florida, Orlando, FL 32827, USA; (J.D.S.); (H.P.-G.); (N.S.); (D.G.); (E.W.)
| | - Deep Gandhi
- College of Medicine, University of Central Florida, Orlando, FL 32827, USA; (J.D.S.); (H.P.-G.); (N.S.); (D.G.); (E.W.)
| | - Evan White
- College of Medicine, University of Central Florida, Orlando, FL 32827, USA; (J.D.S.); (H.P.-G.); (N.S.); (D.G.); (E.W.)
| | - Kaitlyn E. Crawford
- Biionix Cluster, University of Central Florida, Orlando, FL 32827, USA; (Q.F.); (K.E.C.)
- Department of Materials Science and Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Melanie J. Coathup
- Biionix Cluster, University of Central Florida, Orlando, FL 32827, USA; (Q.F.); (K.E.C.)
- College of Medicine, University of Central Florida, Orlando, FL 32827, USA; (J.D.S.); (H.P.-G.); (N.S.); (D.G.); (E.W.)
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Balaji SS, Parhi KK. Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks. J Neural Eng 2024; 21:036053. [PMID: 38885675 DOI: 10.1088/1741-2552/ad5938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/17/2024] [Indexed: 06/20/2024]
Abstract
Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy.Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier's features.Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis.Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.
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Affiliation(s)
- Sai Sanjay Balaji
- University of Minnesota, Department of Electrical & Computer Engineering, Minneapolis, MN, United States of America
| | - Keshab K Parhi
- University of Minnesota, Department of Electrical & Computer Engineering, Minneapolis, MN, United States of America
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Gong R, Roth RW, Chang AJ, Sinha N, Parashos A, Davis KA, Kuzniecky R, Bonilha L, Gleichgerrcht E. EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes. Neurology 2024; 102:e209451. [PMID: 38820468 DOI: 10.1212/wnl.0000000000209451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties. METHODS We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time. RESULTS Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes. DISCUSSION Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.
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Affiliation(s)
- Ruxue Gong
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Rebecca W Roth
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Allen J Chang
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Nishant Sinha
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Alexandra Parashos
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Kathryn A Davis
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Ruben Kuzniecky
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Leonardo Bonilha
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Ezequiel Gleichgerrcht
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
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Shi H, Pattnaik AR, Aguila C, Lucas A, Sinha N, Prager B, Mojena M, Gallagher R, Parashos A, Bonilha L, Gleichgerrcht E, Davis KA, Litt B, Conrad EC. Utility of intracranial EEG networks depends on re-referencing and connectivity choice. Brain Commun 2024; 6:fcae165. [PMID: 38799618 PMCID: PMC11126314 DOI: 10.1093/braincomms/fcae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/02/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.
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Affiliation(s)
- Haoer Shi
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Akash Ranjan Pattnaik
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carlos Aguila
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alfredo Lucas
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Prager
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra Parashos
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30325, USA
| | | | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Tchoe Y, Wu T, U HS, Roth DM, Kim D, Lee J, Cleary DR, Pizarro P, Tonsfeldt KJ, Lee K, Chen PC, Bourhis AM, Galton I, Coughlin B, Yang JC, Paulk AC, Halgren E, Cash SS, Dayeh SA. An electroencephalogram microdisplay to visualize neuronal activity on the brain surface. Sci Transl Med 2024; 16:eadj7257. [PMID: 38657026 PMCID: PMC11093107 DOI: 10.1126/scitranslmed.adj7257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 04/03/2024] [Indexed: 04/26/2024]
Abstract
Functional mapping during brain surgery is applied to define brain areas that control critical functions and cannot be removed. Currently, these procedures rely on verbal interactions between the neurosurgeon and electrophysiologist, which can be time-consuming. In addition, the electrode grids that are used to measure brain activity and to identify the boundaries of pathological versus functional brain regions have low resolution and limited conformity to the brain surface. Here, we present the development of an intracranial electroencephalogram (iEEG)-microdisplay that consists of freestanding arrays of 2048 GaN light-emitting diodes laminated on the back of micro-electrocorticography electrode grids. With a series of proof-of-concept experiments in rats and pigs, we demonstrate that these iEEG-microdisplays allowed us to perform real-time iEEG recordings and display cortical activities by spatially corresponding light patterns on the surface of the brain in the surgical field. Furthermore, iEEG-microdisplays allowed us to identify and display cortical landmarks and pathological activities from rat and pig models. Using a dual-color iEEG-microdisplay, we demonstrated coregistration of the functional cortical boundaries with one color and displayed the evolution of electrical potentials associated with epileptiform activity with another color. The iEEG-microdisplay holds promise to facilitate monitoring of pathological brain activity in clinical settings.
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Affiliation(s)
- Youngbin Tchoe
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Tianhai Wu
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hoi Sang U
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - David M Roth
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Anesthesiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Dongwoo Kim
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jihwan Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel R Cleary
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
- Center for the Future of Surgery, Department of Surgery, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Neurological Surgery, University of California, San Diego, La Jolla, CA 92093, USA
| | - Patricia Pizarro
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Neurological Surgery, Oregon Health & Science University, Mail code CH8N, 3303 SW Bond Avenue, Portland, OR 97239, USA
| | - Karen J Tonsfeldt
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Science and Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Keundong Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Po Chun Chen
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Andrew M Bourhis
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ian Galton
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Brian Coughlin
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Jimmy C Yang
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurological Surgery, Ohio State University, Columbus, OH 43210, USA
| | - Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Eric Halgren
- Department of Neurological Surgery, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Shadi A Dayeh
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
- Departments of Radiology and Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA
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Ntolkeras G, Makaram N, Bernabei M, De La Vega AC, Bolton J, Madsen JR, Stone SSD, Pearl PL, Papadelis C, Grant EP, Tamilia E. Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach. Epilepsia 2024; 65:944-960. [PMID: 38318986 PMCID: PMC11018464 DOI: 10.1111/epi.17898] [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: 08/29/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/07/2024]
Abstract
OBJECTIVE To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data. METHODS We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization. RESULTS FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. SIGNIFICANCE We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.
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Affiliation(s)
- Georgios Ntolkeras
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Navaneethakrishna Makaram
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Matteo Bernabei
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Aime Cristina De La Vega
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, Texas, USA
| | - Ellen P Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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7
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Stergiadis C, Kazis D, Klados MA. Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography. Seizure 2024; 117:28-35. [PMID: 38308906 DOI: 10.1016/j.seizure.2024.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 02/05/2024] Open
Abstract
PURPOSE High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients. METHODS Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures. RESULTS The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples. CONCLUSION Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.
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Affiliation(s)
- Christos Stergiadis
- Department of Electronic Engineering, University of York, York, YO10 5DD, UK
| | - Dimitrios Kazis
- 3rd Neurological Department, Aristotle University of Thessaloniki Faculty of Health Sciences, Exohi, 57010 Thessaloniki, Greece
| | - Manousos A Klados
- Department of Psychology, University of York Europe Campus, CITY College 24, Proxenou Koromila Street, 546 22 Thessaloniki, Greece; Neuroscience Research Center (NEUREC), University of York Europe Campus, City College, Thessaloniki, Greece.
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8
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Karimi-Rouzbahani H, McGonigal A. Generalisability of epileptiform patterns across time and patients. Sci Rep 2024; 14:6293. [PMID: 38491096 PMCID: PMC10942983 DOI: 10.1038/s41598-024-56990-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/13/2024] [Indexed: 03/18/2024] Open
Abstract
The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.
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Affiliation(s)
- Hamid Karimi-Rouzbahani
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia.
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia.
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia.
| | - Aileen McGonigal
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia
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9
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Chang AJ, Roth RW, Gong R, Gross RE, Harmsen I, Parashos A, Revell A, Davis KA, Bonilha L, Gleichgerrcht E. Network coupling and surgical treatment response in temporal lobe epilepsy: A proof-of-concept study. Epilepsy Behav 2023; 149:109503. [PMID: 37931391 PMCID: PMC10842155 DOI: 10.1016/j.yebeh.2023.109503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/29/2023] [Accepted: 10/20/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE This proof-of-concept study aimed to examine the overlap between structural and functional activity (coupling) related to surgical response. METHODS We studied intracranial rest and ictal stereoelectroencephalography (sEEG) recordings from 77 seizures in thirteen participants with temporal lobe epilepsy (TLE) who subsequently underwent resective/laser ablation surgery. We used the stereotactic coordinates of electrodes to construct functional (sEEG electrodes) and structural connectomes (diffusion tensor imaging). A Jaccard index was used to assess the similarity (coupling) between structural and functional connectivity at rest and at various intraictal timepoints. RESULTS We observed that patients who did not become seizure free after surgery had higher connectome coupling recruitment than responders at rest and during early and mid seizure (and visa versa). SIGNIFICANCE Structural networks provide a backbone for functional activity in TLE. The association between lack of seizure control after surgery and the strength of synchrony between these networks suggests that surgical intervention aimed to disrupt these networks may be ineffective in those that display strong synchrony. Our results, combined with findings of other groups, suggest a potential mechanism that explains why certain patients benefit from epilepsy surgery and why others do not. This insight has the potential to guide surgical planning (e.g., removal of high coupling nodes) following future research.
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Affiliation(s)
- Allen J Chang
- College of Graduate Studies, Neuroscience Institute, Medical University of South Carolina, Charleston, SC, USA
| | - Rebecca W Roth
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Ruxue Gong
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - Irene Harmsen
- College of Graduate Studies, Neuroscience Institute, Medical University of South Carolina, Charleston, SC, USA
| | - Alexandra Parashos
- Department of Neurology, College of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew Revell
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Leonardo Bonilha
- Department of Neurology, University of South Carolina, Columbia, SC, USA
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10
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Matarrese MAG, Loppini A, Fabbri L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Pearl PL, Filippi S, Papadelis C. Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy. Brain 2023; 146:3898-3912. [PMID: 37018068 PMCID: PMC10473571 DOI: 10.1093/brain/awad118] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/06/2023] Open
Abstract
Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.
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Affiliation(s)
- Margherita A G Matarrese
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Alessandro Loppini
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Lorenzo Fabbri
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simonetta Filippi
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
- School of Medicine, Texas Christian University, Fort Worth, TX, USA
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11
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Xu K, Yang X, Zhou J, Guan Y, Zhao M, Wang M, Wang J, Li T, Wang X, Luan G. SEEG-based reevaluation of epileptogenic networks and the predictive role for reoperation in MTLE patients with surgical failure. Epilepsia Open 2023; 8:846-857. [PMID: 37043173 PMCID: PMC10472362 DOI: 10.1002/epi4.12743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/08/2023] [Indexed: 04/13/2023] Open
Abstract
OBJECTIVE Approximately 20%-30% of mesial temporal lobe epilepsy (MTLE) patients got unfavorable seizure control after surgery, and there was a discrepancy about the reasons for the surgical failure. The functional connectivity (FC) patterns obtained from stereo-electroencephalography (SEEG) reveal information about the dynamics of the epileptic brain and the added value of extracting information that was not identifiable in the SEEG data using FC analysis. This study aims to find out the patterns of the potential epileptogenic network of failure patients and the electrophysiological predictors of reoperation. METHODS From January 2012 to December 2019, the MTLE patients with surgical failure were reviewed, and all patients underwent SEEG-guided reoperation. The epileptogenic network was quantified by calculating FC indicators, including phase slope index (PSI), mutual information (MI) strength, imaginary coherence (icoh), and Granger causality. RESULTS Ten patients with 13 seizures were included in the analysis, and 7 of them achieved a favorable outcome after the SEEG-guided reoperation. The surgical zone (SZ) with a favorable prognosis showed greater outward information flow than the non-SZ, whereas the SZ with an unfavorable prognosis showed greater inward information flow. The recurrent patients with favorable prognosis had strong connectivity between the posterior hippocampus, temporal neocortex, and insula, whereas the patients with unfavorable prognosis showed strong functional connectivity between the insula and temporal-parietal-occipital junction. The power spectrum of patients with favorable prognosis was significantly lower than that of patients with unfavorable prognosis, especially showing a more oscillation power of low frequency. SIGNIFICANCE The SEEG-guided reoperation could achieve favorable seizure control outcomes for recurrent patients. The FCs were a potential indicator to help construct the temporal epileptic network and predictor for the reoperative prognosis in the recurrent patients.
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Affiliation(s)
- Ke Xu
- Department of Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Xue Yang
- Department of Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Jian Zhou
- Department of Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Yuguang Guan
- Department of Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Meng Zhao
- Department of Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Mengyang Wang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Jing Wang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Tianfu Li
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Department of Brain Institute, Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Epilepsy Research, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Department of Brain Institute, Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Epilepsy Research, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
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12
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Tchoe Y, Wu T, U HS, Roth DM, Kim D, Lee J, Cleary DR, Pizarro P, Tonsfeldt KJ, Lee K, Chen PC, Bourhis AM, Galton I, Coughlin B, Yang JC, Paulk AC, Halgren E, Cash SS, Dayeh SA. The Brain Electroencephalogram Microdisplay for Precision Neurosurgery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.19.549735. [PMID: 37503216 PMCID: PMC10370209 DOI: 10.1101/2023.07.19.549735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Brain surgeries are among the most delicate clinical procedures and must be performed with the most technologically robust and advanced tools. When such surgical procedures are performed in functionally critical regions of the brain, functional mapping is applied as a standard practice that involves direct coordinated interactions between the neurosurgeon and the clinical neurology electrophysiology team. However, information flow during these interactions is commonly verbal as well as time consuming which in turn increases the duration and cost of the surgery, possibly compromising the patient outcomes. Additionally, the grids that measure brain activity and identify the boundaries of pathological versus functional brain regions suffer from low resolution (3-10 mm contact to contact spacing) with limited conformity to the brain surface. Here, we introduce a brain intracranial electroencephalogram microdisplay (Brain-iEEG-microdisplay) which conforms to the brain to measure the brain activity and display changes in near real-time (40 Hz refresh rate) on the surface of the brain in the surgical field. We used scalable engineered gallium nitride (GaN) substrates with 6" diameter to fabricate, encapsulate, and release free-standing arrays of up to 2048 GaN light emitting diodes (μLEDs) in polyimide substrates. We then laminated the μLED arrays on the back of micro-electrocorticography (μECoG) platinum nanorod grids (PtNRGrids) and developed hardware and software to perform near real-time intracranial EEG analysis and activation of light patterns that correspond to specific cortical activities. Using the Brain-iEEG-microdisplay, we precisely ideFSntified and displayed important cortical landmarks and pharmacologically induced pathological activities. In the rat model, we identified and displayed individual cortical columns corresponding to individual whiskers and the near real-time evolution of epileptic discharges. In the pig animal model, we demonstrated near real-time mapping and display of cortical functional boundaries using somatosensory evoked potentials (SSEP) and display of responses to direct electrical stimulation (DES) from the surface or within the brain tissue. Using a dual-color Brain-iEEG-microdisplay, we demonstrated co-registration of the functional cortical boundaries with one color and displayed the evolution of electrical potentials associated with epileptiform activity with another color. The Brain-iEEG-microdisplay holds the promise of increasing the efficiency of diagnosis and possibly surgical treatment, thereby reducing the cost and improving patient outcomes which would mark a major advancement in neurosurgery. These advances can also be translated to broader applications in neuro-oncology and neurophysiology.
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Affiliation(s)
- Youngbin Tchoe
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Tianhai Wu
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Hoi Sang U
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - David M Roth
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
- Center for the Future of Surgery, Department of Surgery, University of California San Diego, La Jolla, California 92093, United States
- Department of Anesthesiology, University of California San Diego, La Jolla, California 92093, United States
| | - Dongwoo Kim
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Jihwan Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Daniel R Cleary
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
- Department of Neurological Surgery, Oregon Health & Science University, Mail code CH8N, 3303 SW Bond Avenue, Portland, Oregon 97239- 3098, United States
| | - Patricia Pizarro
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
- Center for the Future of Surgery, Department of Surgery, University of California San Diego, La Jolla, California 92093, United States
| | - Karen J Tonsfeldt
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Science and Medicine, University of California San Diego, La Jolla, California 92093, United States
| | - Keundong Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Po Chun Chen
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Andrew M Bourhis
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Ian Galton
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Brian Coughlin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Jimmy C Yang
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
- Department of Neurological Surgery, Ohio State University, Columbus, Ohio 43210, United States
| | - Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Eric Halgren
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
- Department of Radiology, University of California San Diego, La Jolla, California 92093, United States
| | - Sydney S Cash
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Science and Medicine, University of California San Diego, La Jolla, California 92093, United States
| | - Shadi A Dayeh
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
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13
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Rijal S, Corona L, Perry MS, Tamilia E, Madsen JR, Stone SSD, Bolton J, Pearl PL, Papadelis C. Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy. Sci Rep 2023; 13:9622. [PMID: 37316544 PMCID: PMC10267141 DOI: 10.1038/s41598-023-36551-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.
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Affiliation(s)
- Sakar Rijal
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Ludovica Corona
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA.
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA.
- School of Medicine, Texas Christian University, Fort Worth, TX, 76129, USA.
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14
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Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023; 146:2248-2258. [PMID: 36623936 PMCID: PMC10232272 DOI: 10.1093/brain/awad007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Li
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Andrew Y Revell
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kristin M Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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15
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Corona L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Stufflebeam SM, Pearl PL, Papadelis C. Non-invasive mapping of epileptogenic networks predicts surgical outcome. Brain 2023; 146:1916-1931. [PMID: 36789500 PMCID: PMC10151194 DOI: 10.1093/brain/awac477] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/03/2022] [Accepted: 11/30/2022] [Indexed: 02/16/2023] Open
Abstract
Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.
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Affiliation(s)
- Ludovica Corona
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Steve M Stufflebeam
- Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
- School of Medicine, Texas Christian University, Fort Worth, TX 76129, USA
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16
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Yang S, Xu K, Guan S, Zou L, Gao L, Wang J, Tian H, Li H, Fang Y, Li H. Polymer nanofiber network reinforced gold electrode array for neural activity recording. Biomed Eng Lett 2023; 13:111-118. [PMID: 37124105 PMCID: PMC10130319 DOI: 10.1007/s13534-022-00257-5] [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: 06/03/2022] [Revised: 11/19/2022] [Accepted: 12/20/2022] [Indexed: 01/04/2023] Open
Abstract
Flexible and stretchable neural electrodes are promising tools for high-fidelity interfacing with soft and curvilinear brain surface. Here, we describe a flexible and stretchable neural electrode array that consists of polyacrylonitrile (PAN) nanofiber network reinforced gold (Au) film electrodes. Under stretching, the interweaving PAN nanofibers effectively terminate the formation of propagating cracks in the Au films and thus enable the formation of a dynamically stable electrode-tissue interface. Moreover, the PAN nanofibers increase the surface roughness and active surface areas of the Au electrodes, leading to reduced electrochemical impedance and improved signal-to-noise ratio. As a result, PAN nanofiber network reinforced Au electrode arrays can allow for reliable in vivo multichannel recording of epileptiform activities in rats. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-022-00257-5.
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Affiliation(s)
- Siting Yang
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Ke Xu
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Shouliang Guan
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
| | - Liang Zou
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Lei Gao
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Jinfen Wang
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
| | - Huihui Tian
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
| | - Hui Li
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, 450052 China
| | - Ying Fang
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Hongbian Li
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190 China
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17
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Shen M, Zhang L, Gong Y, Li L, Liu X. Epileptic Tissue Localization through Skewness-Based Functional Connectivity in the High-Frequency Band of Intracranial EEG. Bioengineering (Basel) 2023; 10:bioengineering10040461. [PMID: 37106648 PMCID: PMC10136084 DOI: 10.3390/bioengineering10040461] [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: 03/17/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Functional connectivity analysis of intracranial electroencephalography (iEEG) plays an important role in understanding the mechanism of epilepsy and seizure dynamics. However, existing connectivity analysis is only suitable for low-frequency bands below 80 Hz. High-frequency oscillations (HFOs) and high-frequency activity (HFA) in the high-frequency band (80-500 Hz) are thought to be specific biomarkers in epileptic tissue localization. However, the transience in duration and variability of occurrence time and amplitudes of these events pose a challenge for conducting effective connectivity analysis. To deal with this problem, we proposed skewness-based functional connectivity (SFC) in the high-frequency band and explored its utility in epileptic tissue localization and surgical outcome evaluation. SFC comprises three main steps. The first step is the quantitative measurement of amplitude distribution asymmetry between HFOs/HFA and baseline activity. The second step is functional network construction on the basis of rank correlation of asymmetry across time. The third step is connectivity strength extraction from the functional network. Experiments were conducted in two separate datasets which consist of iEEG recordings from 59 patients with drug-resistant epilepsy. Significant difference (p<0.001) in connectivity strength was found between epileptic and non-epileptic tissue. Results were quantified via the receiver operating characteristic curve and the area under the curve (AUC). Compared with low-frequency bands, SFC demonstrated superior performance. With respect to pooled and individual epileptic tissue localization for seizure-free patients, AUCs were 0.66 (95% confidence interval (CI): 0.63-0.69) and (0.63 95% CI 0.56-0.71), respectively. For surgical outcome classification, the AUC was 0.75 (95% CI 0.59-0.85). Therefore, SFC can act as a promising assessment tool in characterizing the epileptic network and potentially provide better treatment options for patients with drug-resistant epilepsy.
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Affiliation(s)
- Mu Shen
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lin Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yi Gong
- School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100096, China
| | - Lei Li
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xianzeng Liu
- Department of Neurology, Peking University International Hospital, and Peking University Clinical Research Institute, Beijing 102206, China
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18
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Mohanty D, Quach M. The Noninvasive Evaluation for Minimally Invasive Pediatric Epilepsy Surgery (MIPES): A Multimodal Exploration of the Localization-Based Hypothesis. JOURNAL OF PEDIATRIC EPILEPSY 2022. [DOI: 10.1055/s-0042-1760104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractMinimally invasive pediatric epilepsy surgery (MIPES) is a rising technique in the management of focal-onset drug-refractory epilepsy. Minimally invasive surgical techniques are based on small, focal interventions (such as parenchymal ablation or localized neuromodulation) leading to elimination of the seizure onset zone or interruption of the larger epileptic network. Precise localization of the seizure onset zone, demarcation of eloquent cortex, and mapping of the network leading to seizure propagation are required to achieve optimal outcomes. The toolbox for presurgical, noninvasive evaluation of focal epilepsy continues to expand rapidly, with a variety of options based on advanced imaging and electrophysiology. In this article, we will examine several of these diagnostic modalities from the standpoint of MIPES and discuss how each can contribute to the development of a localization-based hypothesis for potential surgical targets.
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Affiliation(s)
- Deepankar Mohanty
- Section of Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas
| | - Michael Quach
- Section of Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas
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19
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Lagarde S, Bénar CG, Wendling F, Bartolomei F. Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG. Brain Connect 2022; 12:850-869. [PMID: 35972755 PMCID: PMC9807250 DOI: 10.1089/brain.2021.0190] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.
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Affiliation(s)
- Stanislas Lagarde
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France.,Address correspondence to: Stanislas Lagarde, Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, 264 Rue Saint-Pierre, 13005 Marseille, France
| | | | | | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France
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20
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Conrad EC, Bernabei JM, Sinha N, Ghosn NJ, Stein JM, Shinohara RT, Litt B. Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning. J Neural Eng 2022; 19:056019. [PMID: 36084621 PMCID: PMC9590099 DOI: 10.1088/1741-2552/ac90ed] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/26/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023]
Abstract
Objective.To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases.Approach.We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a 'spatial null model' to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates.Main results.About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63-0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78,p= 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72,p= 0.38).Significance.Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.
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Affiliation(s)
- Erin C Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - John M Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nina J Ghosn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Joel M Stein
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States of America
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
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21
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Wang ZJ, Noh BH, Kim ES, Yang D, Yang S, Kim NY, Hur YJ, Kim HD. Brain network analysis of interictal epileptiform discharges from ECoG to identify epileptogenic zone in pediatric patients with epilepsy and focal cortical dysplasia type II: A retrospective study. Front Neurol 2022; 13:901633. [PMID: 35989902 PMCID: PMC9388828 DOI: 10.3389/fneur.2022.901633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveFor patients with drug–resistant focal epilepsy, intracranial monitoring remains the gold standard for surgical intervention. Focal cortical dysplasia (FCD) is the most common cause of pharmacoresistant focal epilepsy in pediatric patients who usually develop seizures in early childhood. Timely removal of the epileptogenic zone (EZ) is necessary to achieve lasting seizure freedom and favorable developmental and cognitive outcomes to improve the quality of life. We applied brain network analysis to investigate potential biomarkers for the diagnosis of EZ that will aid in the resection for pediatric focal epilepsy patients with FCD type II.MethodsTen pediatric patients with focal epilepsy diagnosed as FCD type II and that had a follow–up after resection surgery (Engel class I [n = 9] and Engel class II [n = 1]) were retrospectively included. Time–frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation were combined to calculate brain network parameters based on interictal epileptiform discharges from ECoG.ResultsClustering coefficient, local efficiency, node out–degree, and node out–strength with higher values are the most reliable biomarkers for the delineation of EZ, and the differences between EZ and margin zone (MZ), and EZ and normal zone (NZ) were significant (p < 0.05; Mann–Whitney U-test, two–tailed). In particular, the difference between MZ and NZ was significant for patients with frontal FCD (MZ > NZ; p < 0.05) but was not significant for patients with extra–frontal FCD.ConclusionsBrain network analysis, based on the combination of time–frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation, can aid in the diagnosis of EZ for pediatric focal epilepsy patients with FCD type II.
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Affiliation(s)
- Zhi Ji Wang
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Byoung Ho Noh
- Department of Pediatrics, Kangwon National University Hospital, Chuncheon-si, South Korea
| | - Eun Seong Kim
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Donghwa Yang
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Division of Pediatric Neurology, Department of Pediatrics, National Health Insurance Service Ilsan Hospital, Goyang-si, South Korea
| | - Shan Yang
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Nam Young Kim
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Yun Jung Hur
- Department of Pediatrics, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
- *Correspondence: Yun Jung Hur
| | - Heung Dong Kim
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Heung Dong Kim
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22
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Bernabei JM, Sinha N, Arnold TC, Conrad E, Ong I, Pattnaik AR, Stein JM, Shinohara RT, Lucas TH, Bassett DS, Davis KA, Litt B. Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain 2022; 145:1949-1961. [PMID: 35640886 PMCID: PMC9630716 DOI: 10.1093/brain/awab480] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 11/14/2021] [Accepted: 11/26/2021] [Indexed: 07/25/2023] Open
Abstract
Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Nishant Sinha
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - T Campbell Arnold
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Erin Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ian Ong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Akash R Pattnaik
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
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23
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Taylor PN, Papasavvas CA, Owen TW, Schroeder GM, Hutchings FE, Chowdhury FA, Diehl B, Duncan JS, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Wang Y. Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue. Brain 2022; 145:939-949. [PMID: 35075485 PMCID: PMC9050535 DOI: 10.1093/brain/awab380] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/19/2021] [Accepted: 09/03/2021] [Indexed: 11/14/2022] Open
Abstract
The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.
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Affiliation(s)
- Peter N Taylor
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Christoforos A Papasavvas
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Thomas W Owen
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Gabrielle M Schroeder
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Frances E Hutchings
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Matthew C Walker
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
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Varatharajah Y, Joseph B, Brinkmann B, Morita-Sherman M, Fitzgerald Z, Vegh D, Nair D, Burgess R, Cendes F, Jehi L, Worrell G. Quantitative Analysis of Visually Reviewed Normal Scalp EEG Predicts Seizure Freedom Following Anterior Temporal Lobectomy. Epilepsia 2022; 63:1630-1642. [PMID: 35416285 PMCID: PMC9283304 DOI: 10.1111/epi.17257] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 04/10/2022] [Accepted: 04/11/2022] [Indexed: 11/28/2022]
Abstract
Objective Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL. Methods We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities. Results We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score). Significance These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.
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Affiliation(s)
- Yogatheesan Varatharajah
- Department of Bioengineering, University of Illinois, Urbana, IL, 61801, USA.,Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Boney Joseph
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Benjamin Brinkmann
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Deborah Vegh
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Dileep Nair
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Richard Burgess
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Fernando Cendes
- Department of Neurology, University of Campinas UNICAMP, Campinas, Brazil
| | - Lara Jehi
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Gregory Worrell
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
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25
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Corona L, Tamilia E, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:408-411. [PMID: 34891320 PMCID: PMC8893022 DOI: 10.1109/embc46164.2021.9629686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Children with medically refractory epilepsy (MRE) require resective neurosurgery to achieve seizure freedom, whose success depends on accurate delineation of the epileptogenic zone (EZ). Functional connectivity (FC) can assess the extent of epileptic brain networks since intracranial EEG (icEEG) studies have shown its link to the EZ and predictive value for surgical outcome in these patients. Here, we propose a new noninvasive method based on magnetoencephalography (MEG) and high-density (HD-EEG) data that estimates FC metrics at the source level through an "implantation" of virtual sensors (VSs). We analyzed MEG, HD-EEG, and icEEG data from eight children with MRE who underwent surgery having good outcome and performed source localization (beamformer) on noninvasive data to build VSs at the icEEG electrode locations. We analyzed data with and without Interictal Epileptiform Discharges (IEDs) in different frequency bands, and computed the following FC matrices: Amplitude Envelope Correlation (AEC), Correlation (CORR), and Phase Locking Value (PLV). Each matrix was used to generate a graph using Minimum Spanning Tree (MST), and for each node (i.e., each sensor) we computed four centrality measures: betweenness, closeness, degree, and eigenvector. We tested the reliability of VSs measures with respect to icEEG (regarded as benchmark) via linear correlation, and compared FC values inside vs. outside resection. We observed higher FC inside than outside resection (p<0.05) for AEC [alpha (8-12 Hz), beta (12-30 Hz), and broadband (1-50 Hz)] on data with IEDs and AEC theta (4-8 Hz) on data without IEDs for icEEG, AEC broadband (1-50 Hz) on data without IEDs for MEG-VSs, as well as for all centrality measures of icEEG and MEG/HD-EEG-VSs. Additionally, icEEG and VSs metrics presented high correlation (0.6-0.9, p<0.05). Our data support the notion that the proposed method can potentially replicate the icEEG ability to map the epileptogenic network in children with MRE.Clinical Relevance - The estimation of FC with noninvasive techniques, such as MEG and HD-EEG, via VSs is a promising tool that would help the presurgical evaluation by delineating the EZ without waiting for a seizure to occur, and potentially improve the surgical outcome of patients with MRE undergoing surgery.
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Papadelis C, Perry MS. Localizing the Epileptogenic Zone with Novel Biomarkers. Semin Pediatr Neurol 2021; 39:100919. [PMID: 34620466 PMCID: PMC8501232 DOI: 10.1016/j.spen.2021.100919] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 01/01/2023]
Abstract
Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.
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Affiliation(s)
- Christos Papadelis
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX; School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, TX; Department of Bioengineering, University of Texas at Arlington, Arlington, TX; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.
| | - M Scott Perry
- Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
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27
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Bernabei JM, Arnold TC, Shah P, Revell A, Ong IZ, Kini LG, Stein JM, Shinohara RT, Lucas TH, Davis KA, Bassett DS, Litt B. Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models. Brain Commun 2021; 3:fcab156. [PMID: 34396112 PMCID: PMC8361393 DOI: 10.1093/braincomms/fcab156] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/19/2021] [Accepted: 05/31/2021] [Indexed: 01/01/2023] Open
Abstract
Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - T Campbell Arnold
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew Revell
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ian Z Ong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lohith G Kini
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
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28
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Fonti D, Lagarde S, Pizzo F, Aboubakr W, Benar C, Giusiano B, Bartolomei F. Parieto-premotor functional connectivity changes during parietal lobe seizures are associated with motor semiology. Clin Neurophysiol 2021; 132:2046-2053. [PMID: 34284239 DOI: 10.1016/j.clinph.2021.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/06/2021] [Accepted: 06/09/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES Parietal lobe seizures (PLS) are characterized by multiple clinical manifestations including motor signs. The mechanisms underlying the occurrence of motor signs are poorly understood. The main objective of this work was to estimate the functional coupling of brain regions associated with this clinical presentation. METHODS We retrospectively selected patients affected by drug-resistant epilepsy who underwent Stereoelectroencephalography (SEEG) for pre-surgical evaluation and in whom the seizure onset zone (SOZ) was located in the parietal cortex. The SOZ was defined visually and quantitatively by the epileptogenicity index (EI) method. Two groups of seizures were defined according to the presence ("motor seizures") or the absence ("non-motor seizures") of motor signs. Functional connectivity (FC) estimation was based on pairwise nonlinear regression analysis (h2 coefficient). To study FC changes between parietal, frontal and temporal regions, for each patient, z-score values of 16 cortico-cortical interactions were obtained comparing h2 coefficients of pre-ictal, seizure onset and seizure propagation periods. RESULTS We included 22 patients, 13 with "motor seizures" and 9 with "non-motor seizures". Resective surgery was performed in 14 patients, 8 patients had a positive surgical outcome (Engel's class I and II). During seizure onset period, a decrease of FC was observed and was significantly more important (in comparison with background period) in "motor" seizures. This was particularly observed between parietal operculum/post-central gyrus (OP/PoCg) and mesial temporal areas. During seizure propagation, a FC increase was significantly more important (in comparison with seizure onset) in "motor seizures", in particular between lateral pre-motor (pmL) area and precuneus, pmL and superior parietal lobule (SPL) and between inferior parietal lobule (IPL) and supplementary motor area (SMA). CONCLUSIONS Our study shows that motor semiology in PLS is accompanied by an increase of FC between parietal and premotor cortices, significantly different than what is observed in PLS without motor semiology. SIGNIFICANCE Our results indicate that preferential routes of coupling between parietal and premotor cortices are responsible for the prominent motor presentation during PLS.
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Affiliation(s)
- Davide Fonti
- APHM, Timone Hospital, Epileptology Department, Marseille, France; Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Stanislas Lagarde
- Aix Marseille Univ, APHM, INSERM, INS, Inst Neurosci Syst, Timone Hospital, Epileptology Department, Marseille, France
| | - Francesca Pizzo
- Aix Marseille Univ, APHM, INSERM, INS, Inst Neurosci Syst, Timone Hospital, Epileptology Department, Marseille, France
| | - Wala Aboubakr
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian Benar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Bernard Giusiano
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, APHM, INSERM, INS, Inst Neurosci Syst, Timone Hospital, Epileptology Department, Marseille, France.
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29
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Xiao L, Li C, Wang Y, Chen J, Si W, Yao C, Li X, Duan C, Heng PA. Automatic Localization of Seizure Onset Zone From High-Frequency SEEG Signals: A Preliminary Study. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021. [DOI: 10.1109/jtehm.2021.3090214] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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30
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Demuru M, Zweiphenning W, van Blooijs D, Van Eijsden P, Leijten F, Zijlmans M, Kalitzin S. Validation of virtual resection on intraoperative interictal data acquired during epilepsy surgery. J Neural Eng 2020; 17. [PMID: 33086212 DOI: 10.1088/1741-2552/abc3a8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/21/2020] [Indexed: 11/11/2022]
Abstract
OBJECTIVE A 'Virtual resection' consists of computationally simulating the effect of an actual resection on the brain. We validated two functional connectivity based virtual resection methods with the actual connectivity measured using post-resection intraoperative recordings. METHODS A non-linear association index was applied to pre-resection recordings from 11 extra-temporal focal epilepsy patients. We computed two virtual resection strategies: first, a 'naive' one obtained by simply removing from the connectivity matrix the electrodes that were resected; second, a virtual resection with partialization accounting for the influence of resected electrodes on not-resected electrodes. We validated the virtual resections with two analysis: 1) We tested with a Kolmogorov-Smirnov test if the distributions of connectivity values after the virtual resections differed from the actual post-resection connectivity distribution; 2) we tested if the overall effect of the resection measured by contrasting pre-resection and post-resection connectivity values is detectable with the virtual resection approach using a Kolmogorv-Smirnov test. RESULTS The estimation of post-resection connectivity values did not succeed for both methods. In the second analysis, the naive method failed completely to detect the effect found between pre-resection and post-resection connectivity distributions, while the partialization method agreed with post-resection measurements in detecting a drop connectivity compared to pre-resection recordings. CONCLUSION Our findings suggest that the partialization technique is superior to the naive method in detecting the overall effect after the resection. SIGNIFICANCE We pointed out how a realistic validation based on actual post-resection recordings reveals that virtual resection methods are not yet mature to inform the clinical decision-making.
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Affiliation(s)
- Matteo Demuru
- Research, SEIN, Hoofddorp, Noord-Holland, NETHERLANDS
| | - Willemiek Zweiphenning
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Dorien van Blooijs
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Pieter Van Eijsden
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Frans Leijten
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Maeike Zijlmans
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
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31
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Ramaraju S, Wang Y, Sinha N, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Taylor PN. Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom. Front Neurol 2020; 11:563847. [PMID: 33071948 PMCID: PMC7543719 DOI: 10.3389/fneur.2020.563847] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 08/20/2020] [Indexed: 01/21/2023] Open
Abstract
Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.
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Affiliation(s)
- Sriharsha Ramaraju
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Fergus Rugg-Gunn
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
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32
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Narasimhan S, Kundassery KB, Gupta K, Johnson GW, Wills KE, Goodale SE, Haas K, Rolston JD, Naftel RP, Morgan VL, Dawant BM, González HFJ, Englot DJ. Seizure-onset regions demonstrate high inward directed connectivity during resting-state: An SEEG study in focal epilepsy. Epilepsia 2020; 61:2534-2544. [PMID: 32944945 DOI: 10.1111/epi.16686] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVE In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity. METHODS In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity. RESULTS Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes. SIGNIFICANCE Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.
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Affiliation(s)
- Saramati Narasimhan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Keshav B Kundassery
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kanupriya Gupta
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Graham W Johnson
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Kristin E Wills
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sarah E Goodale
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Kevin Haas
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Victoria L Morgan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Hernán F J González
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Dario J Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
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33
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Wang Y, Sinha N, Schroeder GM, Ramaraju S, McEvoy AW, Miserocchi A, de Tisi J, Chowdhury FA, Diehl B, Duncan JS, Taylor PN. Interictal intracranial electroencephalography for predicting surgical success: The importance of space and time. Epilepsia 2020; 61:1417-1426. [PMID: 32589284 PMCID: PMC7611164 DOI: 10.1111/epi.16580] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 05/21/2020] [Accepted: 05/21/2020] [Indexed: 12/14/2022]
Abstract
Objective Predicting postoperative seizure freedom using functional correlation networks derived from interictal intracranial electroencephalography (EEG) has shown some success. However, there are important challenges to consider: (1) electrodes physically closer to each other naturally tend to be more correlated, causing a spatial bias; (2) implantation location and number of electrodes differ between patients, making cross-subject comparisons difficult; and (3) functional correlation networks can vary over time but are currently assumed to be static. Methods In this study, we address these three challenges using intracranial EEG data from 55 patients with intractable focal epilepsy. Patients additionally underwent preoperative magnetic resonance imaging (MRI), intraoperative computed tomography, and postoperative MRI, allowing accurate localization of electrodes and delineation of the removed tissue. Results We show that normalizing for spatial proximity between nearby electrodes improves prediction of postsurgery seizure outcomes. Moreover, patients with more extensive electrode coverage were more likely to have their outcome predicted correctly (area under the receiver operating characteristic curve > 0.9, P « 0.05) but not necessarily more likely to have a better outcome. Finally, our predictions are robust regardless of the time segment analyzed. Significance Future studies should account for the spatial proximity of electrodes in functional network construction to improve prediction of postsurgical seizure outcomes. Greater coverage of both removed and spared tissue allows for predictions with higher accuracy.
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Affiliation(s)
- Yujiang Wang
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.,Institute of Neurology, University College London, London, UK
| | - Nishant Sinha
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Gabrielle M Schroeder
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Sriharsha Ramaraju
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Andrew W McEvoy
- Institute of Neurology, University College London, London, UK
| | - Anna Miserocchi
- Institute of Neurology, University College London, London, UK
| | - Jane de Tisi
- Institute of Neurology, University College London, London, UK
| | | | - Beate Diehl
- Institute of Neurology, University College London, London, UK
| | - John S Duncan
- Institute of Neurology, University College London, London, UK
| | - Peter N Taylor
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.,Institute of Neurology, University College London, London, UK
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34
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Kini LG, Bernabei JM, Mikhail F, Hadar P, Shah P, Khambhati AN, Oechsel K, Archer R, Boccanfuso J, Conrad E, Shinohara RT, Stein JM, Das S, Kheder A, Lucas TH, Davis KA, Bassett DS, Litt B. Virtual resection predicts surgical outcome for drug-resistant epilepsy. Brain 2020; 142:3892-3905. [PMID: 31599323 DOI: 10.1093/brain/awz303] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 07/11/2019] [Accepted: 08/08/2019] [Indexed: 12/13/2022] Open
Abstract
Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.
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Affiliation(s)
- Lohith G Kini
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - John M Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Fadi Mikhail
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Peter Hadar
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California San Francisco, San Francisco CA 94143, USA
| | - Kelly Oechsel
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Ryan Archer
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Jacqueline Boccanfuso
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Erin Conrad
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia PA 19104, USA.,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Sandhitsu Das
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Ammar Kheder
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Physics and Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Psychiatry, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA.,Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia PA 19104, USA
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35
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Conrad EC, Bernabei JM, Kini LG, Shah P, Mikhail F, Kheder A, Shinohara RT, Davis KA, Bassett DS, Litt B. The sensitivity of network statistics to incomplete electrode sampling on intracranial EEG. Netw Neurosci 2020; 4:484-506. [PMID: 32537538 PMCID: PMC7286312 DOI: 10.1162/netn_a_00131] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 02/10/2020] [Indexed: 12/12/2022] Open
Abstract
Network neuroscience applied to epilepsy holds promise to map pathological networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of the epileptic brain because of sparse placement of intracranial electrodes may affect model results. In this study, we evaluate the sensitivity of several published network measures to incomplete spatial sampling and propose an algorithm using network subsampling to determine confidence in model results. We retrospectively evaluated intracranial EEG data from 28 patients implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after randomly and systematically removing subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics. This sensitivity was largely independent of whether seizure onset zone contacts were targeted or spared from removal. We present an algorithm using random subsampling to compute patient-specific confidence intervals for network localizations. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.
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Affiliation(s)
- Erin C. Conrad
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - John M. Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Lohith G. Kini
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Preya Shah
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Fadi Mikhail
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ammar Kheder
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A. Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S. Bassett
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Litt
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
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36
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Abstract
During the presurgical evaluation of patients with focal refractory epilepsies, the spatial mapping of the seizure onset zone (SOZ) and seizure propagation networks critically depends on the use of different features extracted from the intracranial electroencephalogram (IEEG). The identification of the SOZ is usually based on visual inspection by highly qualified neurophysiologists. However, quantitative IEEG analyses have recently been developed by exploiting signal and image characteristics in order to improve and expedite the SOZ detection. Here, the authors briefly review some of the latest methods proposed by different research groups and then present the recent implementation in Brainstorm software.
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37
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Müller M, Caporro M, Gast H, Pollo C, Wiest R, Schindler K, Rummel C. Linear and nonlinear interrelations show fundamentally distinct network structure in preictal intracranial EEG of epilepsy patients. Hum Brain Mapp 2019; 41:467-483. [PMID: 31625670 PMCID: PMC7268049 DOI: 10.1002/hbm.24816] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 09/18/2019] [Accepted: 09/20/2019] [Indexed: 12/24/2022] Open
Abstract
Resection of the seizure generating tissue can be highly beneficial in patients with drug-resistant epilepsy. However, only about half of all patients undergoing surgery get permanently and completely seizure free. Investigating the dependences between intracranial EEG signals adds a multivariate perspective largely unavailable to visual EEG analysis, which is the current clinical practice. We examined linear and nonlinear interrelations between intracranial EEG signals regarding their spatial distribution and network characteristics. The analyzed signals were recorded immediately before clinical seizure onset in epilepsy patients who received a standardized electrode implantation targeting the mesiotemporal structures. The linear interrelation networks were predominantly locally connected and highly reproducible between patients. In contrast, the nonlinear networks had a clearly centralized structure, which was specific for the individual pathology. The nonlinear interrelations were overrepresented in the focal hemisphere and in patients with no or only rare seizures after surgery specifically in the resected tissue. Connections to the outside were predominantly nonlinear. In all patients without worthwhile improvement after resective treatment, tissue producing strong nonlinear interrelations was left untouched by surgery. Our findings indicate that linear and nonlinear interrelations play fundamentally different roles in preictal intracranial EEG. Moreover, they suggest nonlinear signal interrelations to be a marker of epileptogenic tissue and not a characteristic of the mesiotemporal structures. Our results corroborate the network-based nature of epilepsy and suggest the application of network analysis to support the planning of resective epilepsy surgery.
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Affiliation(s)
- Michael Müller
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland.,Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Matteo Caporro
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Heidemarie Gast
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
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