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Chybowski B, Klimes P, Cimbalnik J, Travnicek V, Nejedly P, Pail M, Peter-Derex L, Hall J, Dubeau F, Jurak P, Brazdil M, Frauscher B. Timing matters for accurate identification of the epileptogenic zone. Clin Neurophysiol 2024; 161:1-9. [PMID: 38430856 DOI: 10.1016/j.clinph.2024.01.007] [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: 07/18/2023] [Revised: 12/12/2023] [Accepted: 01/01/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVE Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). METHODS We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). RESULTS On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. CONCLUSIONS The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SIGNIFICANCE Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.
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Affiliation(s)
- Bartlomiej Chybowski
- University of Edinburgh, School of Medicine, Deanery of Clinical Sciences, 47 Little France Crescent, EH164TJ Edinburgh, Scotland
| | - Petr Klimes
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Pekařská 53, 602 00 Brno, Czech Republic
| | - Vojtech Travnicek
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital, Pekařská 53, 602 00 Brno, Czech Republic
| | - Petr Nejedly
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Martin Pail
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic; Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Žerotínovo nám 617/9, 601 77 Brno, Czech Republic
| | - Laure Peter-Derex
- Center for Sleep Medicine and Respiratory Diseases, Lyon University Hospital, Lyon 1 University, 103 Grande Rue de la Croix-Rousse, 69004 Lyon, France; Lyon Neuroscience Research Center, CH Le Vinatier - Bâtiment 462 - Neurocampus, 95 Bd Pinel, 69500 Lyon, France
| | - Jeff Hall
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada
| | - Pavel Jurak
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Milan Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Žerotínovo nám 617/9, 601 77 Brno, Czech Republic
| | - Birgit Frauscher
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada; Department of Neurology, Duke University Medical School and Department of Biomedical Engineering, Pratt School of Engineering, 2424 Erwin Road, Durham, NC, 27705, USA.
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2
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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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Cai J, Hadjinicolaou AE, Paulk AC, Soper DJ, Xia T, Williams ZM, Cash SS. Natural language processing models reveal neural dynamics of human conversation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.10.531095. [PMID: 36945468 PMCID: PMC10028965 DOI: 10.1101/2023.03.10.531095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
Through conversation, humans relay complex information through the alternation of speech production and comprehension. The neural mechanisms that underlie these complementary processes or through which information is precisely conveyed by language, however, remain poorly understood. Here, we used pretrained deep learning natural language processing models in combination with intracranial neuronal recordings to discover neural signals that reliably reflect speech production, comprehension, and their transitions during natural conversation between individuals. Our findings indicate that neural activities that encoded linguistic information were broadly distributed throughout frontotemporal areas across multiple frequency bands. We also find that these activities were specific to the words and sentences being conveyed and that they were dependent on the word's specific context and order. Finally, we demonstrate that these neural patterns partially overlapped during language production and comprehension and that listener-speaker transitions were associated with specific, time-aligned changes in neural activity. Collectively, our findings reveal a dynamical organization of neural activities that subserve language production and comprehension during natural conversation and harness the use of deep learning models in understanding the neural mechanisms underlying human language.
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Affiliation(s)
- Jing Cai
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Alex E. Hadjinicolaou
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Angelique C. Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Daniel J. Soper
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Tian Xia
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Ziv M. Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA
- Harvard Medical School, Program in Neuroscience, Boston, MA
- These authors contributed equally
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA
- These authors contributed equally
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Kremen V, Sladky V, Mivalt F, Gregg NM, Balzekas I, Marks V, Brinkmann BH, Lundstrom BN, Cui J, St Louis EK, Croarkin P, Alden EC, Fields J, Crockett K, Adolf J, Bilderbeek J, Hermes D, Messina S, Miller KJ, Van Gompel J, Denison T, Worrell GA. A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.09.24302358. [PMID: 38370724 PMCID: PMC10871449 DOI: 10.1101/2024.02.09.24302358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy, but the optimal stimulation parameters remain unclear. We developed a neurotechnology platform for tracking seizures and MMS to enable data streaming between an investigational brain sensing-stimulation implant, mobile devices, and a cloud environment. Artificial Intelligence algorithms provided accurate catalogs of seizures, interictal epileptiform spikes, and wake-sleep brain states. Remotely administered memory and mood assessments were used to densely sample cognitive and behavioral response during ANT-DBS. We evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. They both reduced seizures, but low-frequency ANT-DBS showed greater reductions and better sleep and memory. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapy.
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5
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Balzekas I, Trzasko J, Yu G, Richner TJ, Mivalt F, Sladky V, Gregg NM, Van Gompel J, Miller K, Croarkin PE, Kremen V, Worrell GA. Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity. PLoS Comput Biol 2024; 20:e1011152. [PMID: 38662736 PMCID: PMC11045138 DOI: 10.1371/journal.pcbi.1011152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 03/04/2024] [Indexed: 04/28/2024] Open
Abstract
Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.
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Affiliation(s)
- Irena Balzekas
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, United States of America
- Mayo Clinic Alix School of Medicine, Rochester, Minnesota, United States of America
- Mayo Clinic Medical Scientist Training Program, Rochester, Minnesota, United States of America
| | - Joshua Trzasko
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Grace Yu
- Mayo Clinic Alix School of Medicine, Rochester, Minnesota, United States of America
- Mayo Clinic Medical Scientist Training Program, Rochester, Minnesota, United States of America
| | - Thomas J. Richner
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Filip Mivalt
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
- International Clinic Research Center, St. Anne’s University Research Hospital, Brno, Czech Republic
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Vladimir Sladky
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
- International Clinic Research Center, St. Anne’s University Research Hospital, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Czechia
| | - Nicholas M. Gregg
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Jamie Van Gompel
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Kai Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Vaclav Kremen
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
| | - Gregory A. Worrell
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, United States of America
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6
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Ayyoubi AH, Fazli Besheli B, Quach MM, Gavvala JR, Goldman AM, Swamy CP, Bartoli E, Curry DJ, Sheth SA, Francis DJ, Ince NF. Benchmarking signal quality and spatiotemporal distribution of interictal spikes in prolonged human iEEG recordings using CorTec wireless brain interchange. Sci Rep 2024; 14:2652. [PMID: 38332136 PMCID: PMC10853182 DOI: 10.1038/s41598-024-52487-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
Neuromodulation through implantable pulse generators (IPGs) represents an important treatment approach for neurological disorders. While the field has observed the success of state-of-the-art interventions, such as deep brain stimulation (DBS) or responsive neurostimulation (RNS), implantable systems face various technical challenges, including the restriction of recording from a limited number of brain sites, power management, and limited external access to the assessed neural data in a continuous fashion. To the best of our knowledge, for the first time in this study, we investigated the feasibility of recording human intracranial EEG (iEEG) using a benchtop version of the Brain Interchange (BIC) unit of CorTec, which is a portable, wireless, and externally powered implant with sensing and stimulation capabilities. We developed a MATLAB/SIMULINK-based rapid prototyping environment and a graphical user interface (GUI) to acquire and visualize the iEEG captured from all 32 channels of the BIC unit. We recorded prolonged iEEG (~ 24 h) from three human subjects with externalized depth leads using the BIC and commercially available clinical amplifiers simultaneously in the epilepsy monitoring unit (EMU). The iEEG signal quality of both streams was compared, and the results demonstrated a comparable power spectral density (PSD) in all the systems in the low-frequency band (< 80 Hz). However, notable differences were primarily observed above 100 Hz, where the clinical amplifiers were associated with lower noise floor (BIC-17 dB vs. clinical amplifiers < - 25 dB). We employed an established spike detector to assess and compare the spike rates in each iEEG stream. We observed over 90% conformity between the spikes rates and their spatial distribution captured with BIC and clinical systems. Additionally, we quantified the packet loss characteristic in the iEEG signal during the wireless data transfer and conducted a series of simulations to compare the performance of different interpolation methods for recovering the missing packets in signals at different frequency bands. We noted that simple linear interpolation has the potential to recover the signal and reduce the noise floor with modest packet loss levels reaching up to 10%. Overall, our results indicate that while tethered clinical amplifiers exhibited noticeably better noise floor above 80 Hz, epileptic spikes can still be detected successfully in the iEEG recorded with the externally powered wireless BIC unit opening the road for future closed-loop neuromodulation applications with continuous access to brain activity.
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Affiliation(s)
- Amir Hossein Ayyoubi
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Behrang Fazli Besheli
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Michael M Quach
- Department of Neurology, Texas Children's Hospital, Houston, TX, USA
| | | | - Alica M Goldman
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Daniel J Curry
- Department of Neurosurgery, Texas Children's Hospital, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - David J Francis
- Department of Psychology, University of Houston, Houston, TX, USA
| | - Nuri F Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA.
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7
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Latreille V, Avigdor T, Thomas J, Crane J, Sziklas V, Jones-Gotman M, Frauscher B. Scalp and hippocampal sleep correlates of memory function in drug-resistant temporal lobe epilepsy. Sleep 2024; 47:zsad228. [PMID: 37658793 PMCID: PMC10851866 DOI: 10.1093/sleep/zsad228] [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: 04/24/2023] [Revised: 07/22/2023] [Indexed: 09/05/2023] Open
Abstract
Seminal animal studies demonstrated the role of sleep oscillations such as cortical slow waves, thalamocortical spindles, and hippocampal ripples in memory consolidation. In humans, whether ripples are involved in sleep-related memory processes is less clear. Here, we explored the interactions between sleep oscillations (measured as traits) and general episodic memory abilities in 26 adults with drug-resistant temporal lobe epilepsy who performed scalp-intracranial electroencephalographic recordings and neuropsychological testing, including two analogous hippocampal-dependent verbal and nonverbal memory tasks. We explored the relationships between hemispheric scalp (spindles, slow waves) and hippocampal physiological and pathological oscillations (spindles, slow waves, ripples, and epileptic spikes) and material-specific memory function. To differentiate physiological from pathological ripples, we used multiple unbiased data-driven clustering approaches. At the individual level, we found material-specific cerebral lateralization effects (left-verbal memory, right-nonverbal memory) for all scalp spindles (rs > 0.51, ps < 0.01) and fast spindles (rs > 0.61, ps < 0.002). Hippocampal epileptic spikes and short pathological ripples, but not physiological oscillations, were negatively (rs > -0.59, ps < 0.01) associated with verbal learning and retention scores, with left lateralizing and antero-posterior effects. However, data-driven clustering failed to separate the ripple events into defined clusters. Correlation analyses with the resulting clusters revealed no meaningful or significant associations with the memory scores. Our results corroborate the role of scalp spindles in memory processes in patients with drug-resistant temporal lobe epilepsy. Yet, physiological and pathological ripples were not separable when using data-driven clustering, and thus our findings do not provide support for a role of sleep ripples as trait-like characteristics of general memory abilities in epilepsy.
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Affiliation(s)
- Véronique Latreille
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Tamir Avigdor
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - John Thomas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Marilyn Jones-Gotman
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Birgit Frauscher
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Analytical Neurophysiology (ANPHY) Lab, Duke University Medical Center, Durham, NC, USA
- Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Department of Biomedical Engineering. Duke Pratt School of Engineering, Durham NC, USA
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8
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Su M, Hu K, Liu W, Wu Y, Wang T, Cao C, Sun B, Zhan S, Ye Z. Theta Oscillations Support Prefrontal-hippocampal Interactions in Sequential Working Memory. Neurosci Bull 2024; 40:147-156. [PMID: 37847448 PMCID: PMC10838883 DOI: 10.1007/s12264-023-01134-6] [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: 03/28/2023] [Accepted: 06/28/2023] [Indexed: 10/18/2023] Open
Abstract
The prefrontal cortex and hippocampus may support sequential working memory beyond episodic memory and spatial navigation. This stereoelectroencephalography (SEEG) study investigated how the dorsolateral prefrontal cortex (DLPFC) interacts with the hippocampus in the online processing of sequential information. Twenty patients with epilepsy (eight women, age 27.6 ± 8.2 years) completed a line ordering task with SEEG recordings over the DLPFC and the hippocampus. Participants showed longer thinking times and more recall errors when asked to arrange random lines clockwise (random trials) than to maintain ordered lines (ordered trials) before recalling the orientation of a particular line. First, the ordering-related increase in thinking time and recall error was associated with a transient theta power increase in the hippocampus and a sustained theta power increase in the DLPFC (3-10 Hz). In particular, the hippocampal theta power increase correlated with the memory precision of line orientation. Second, theta phase coherences between the DLPFC and hippocampus were enhanced for ordering, especially for more precisely memorized lines. Third, the theta band DLPFC → hippocampus influence was selectively enhanced for ordering, especially for more precisely memorized lines. This study suggests that theta oscillations may support DLPFC-hippocampal interactions in the online processing of sequential information.
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Affiliation(s)
- Minghong Su
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kejia Hu
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wei Liu
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yunhao Wu
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tao Wang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chunyan Cao
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Bomin Sun
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shikun Zhan
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Zheng Ye
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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Chvojka J, Prochazkova N, Rehorova M, Kudlacek J, Kylarova S, Kralikova M, Buran P, Weissova R, Balastik M, Jefferys JGR, Novak O, Jiruska P. Mouse model of focal cortical dysplasia type II generates a wide spectrum of high-frequency activities. Neurobiol Dis 2024; 190:106383. [PMID: 38114051 DOI: 10.1016/j.nbd.2023.106383] [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: 09/27/2023] [Revised: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
High-frequency oscillations (HFOs) represent an electrographic biomarker of endogenous epileptogenicity and seizure-generating tissue that proved clinically useful in presurgical planning and delineating the resection area. In the neocortex, the clinical observations on HFOs are not sufficiently supported by experimental studies stemming from a lack of realistic neocortical epilepsy models that could provide an explanation of the pathophysiological substrates of neocortical HFOs. In this study, we explored pathological epileptiform network phenomena, particularly HFOs, in a highly realistic murine model of neocortical epilepsy due to focal cortical dysplasia (FCD) type II. FCD was induced in mice by the expression of the human pathogenic mTOR gene mutation during embryonic stages of brain development. Electrographic recordings from multiple cortical regions in freely moving animals with FCD and epilepsy demonstrated that the FCD lesion generates HFOs from all frequency ranges, i.e., gamma, ripples, and fast ripples up to 800 Hz. Gamma-ripples were recorded almost exclusively in FCD animals, while fast ripples occurred in controls as well, although at a lower rate. Gamma-ripple activity is particularly valuable for localizing the FCD lesion, surpassing the utility of fast ripples that were also observed in control animals, although at significantly lower rates. Propagating HFOs occurred outside the FCD, and the contralateral cortex also generated HFOs independently of the FCD, pointing to a wider FCD network dysfunction. Optogenetic activation of neurons carrying mTOR mutation and expressing Channelrhodopsin-2 evoked fast ripple oscillations that displayed spectral and morphological profiles analogous to spontaneous oscillations. This study brings experimental evidence that FCD type II generates pathological HFOs across all frequency bands and provides information about the spatiotemporal properties of each HFO subtype in FCD. The study shows that mutated neurons represent a functionally interconnected and active component of the FCD network, as they can induce interictal epileptiform phenomena and HFOs.
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Affiliation(s)
- Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Natalie Prochazkova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Monika Rehorova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jan Kudlacek
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Salome Kylarova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Michaela Kralikova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Peter Buran
- Laboratory of Molecular Neurobiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Romana Weissova
- Laboratory of Molecular Neurobiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Martin Balastik
- Laboratory of Molecular Neurobiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - John G R Jefferys
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Ondrej Novak
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.
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10
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Gunia A, Moraresku S, Janča R, Ježdík P, Kalina A, Hammer J, Marusič P, Vlček K. The brain dynamics of visuospatial perspective-taking captured by intracranial EEG. Neuroimage 2024; 285:120487. [PMID: 38072339 DOI: 10.1016/j.neuroimage.2023.120487] [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: 02/05/2023] [Revised: 09/18/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Visuospatial perspective-taking (VPT) is the ability to imagine a scene from a position different from the one used in self-perspective judgments (SPJ). We typically use VPT to understand how others see the environment. VPT requires overcoming the self-perspective, and impairments in this process are implicated in various brain disorders, such as schizophrenia and autism. However, the underlying brain areas of VPT are not well distinguished from SPJ-related ones and from domain-general responses to both perspectives. In addition, hierarchical processing theory suggests that domain-specific processes emerge over time from domain-general ones. It mainly focuses on the sensory system, but outside of it, support for this hypothesis is lacking. Therefore, we aimed to spatiotemporally distinguish brain responses domain-specific to VPT from the specific ones to self-perspective, and domain-general responses to both perspectives. In particular, we intended to test whether VPT- and SPJ specific responses begin later than the general ones. We recorded intracranial EEG data from 30 patients with epilepsy who performed a task requiring laterality judgments during VPT and SPJ, and analyzed the spatiotemporal features of responses in the broad gamma band (50-150 Hz). We found VPT-specific processing in a more extensive brain network than SPJ-specific processing. Their dynamics were similar, but both differed from the general responses, which began earlier and lasted longer. Our results anatomically distinguish VPT-specific from SPJ-specific processing. Furthermore, we temporally differentiate between domain-specific and domain-general processes both inside and outside the sensory system, which serves as a novel example of hierarchical processing.
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Affiliation(s)
- Anna Gunia
- Laboratory of Neurophysiology of Memory, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic; Charles University, Third Faculty of Medicine, Prague, Czech Republic.
| | - Sofiia Moraresku
- Laboratory of Neurophysiology of Memory, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic; Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Radek Janča
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Petr Ježdík
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Adam Kalina
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Jiří Hammer
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Petr Marusič
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Kamil Vlček
- Laboratory of Neurophysiology of Memory, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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11
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Abdi-Sargezeh B, Shirani S, Sanei S, Took CC, Geman O, Alarcon G, Valentin A. A review of signal processing and machine learning techniques for interictal epileptiform discharge detection. Comput Biol Med 2024; 168:107782. [PMID: 38070202 DOI: 10.1016/j.compbiomed.2023.107782] [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: 06/24/2023] [Revised: 11/15/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024]
Abstract
Brain interictal epileptiform discharges (IEDs), as one of the hallmarks of epileptic brain, are transient events captured by electroencephalogram (EEG). IEDs are generated by seizure networks, and they occur between seizures (interictal periods). The development of a robust method for IED detection could be highly informative for clinical treatment procedures and epileptic patient management. Since 1972, different machine learning techniques, from template matching to deep learning, have been developed to automatically detect IEDs from scalp EEG (scEEG) and intracranial EEG (iEEG). While the scEEG signals suffer from low information details and high attenuation of IEDs due to the high skull electrical impedance, the iEEG signals recorded using implanted electrodes enjoy higher details and are more suitable for identifying the IEDs. In this review paper, we group IED detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the iEEG from the concurrent scEEG followed by detection and classification. The methods are compared quantitatively (e.g., in terms of accuracy, sensitivity, and specificity), and their general advantages and limitations are described. Finally, current limitations and possible future research paths related to this field are mentioned.
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Affiliation(s)
- Bahman Abdi-Sargezeh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; School of Science and Technology, Nottingham Trent University, Nottingham, UK.
| | - Sepehr Shirani
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Clive Cheong Took
- Department of Electronic Engineering, Royal Holloway, University of London, London, UK
| | - Oana Geman
- Computer, Electronics and Automation Department, University Stefan cel Mare, Suceava, Romania
| | - Gonzalo Alarcon
- Department of Clinical Neurophysiology, Royal Manchester Children's Hospital, Manchester, UK
| | - Antonio Valentin
- Department of Clinical Neuroscience, King's College London, London, UK
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12
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Moraresku S, Hammer J, Janca R, Jezdik P, Kalina A, Marusic P, Vlcek K. Timing of Allocentric and Egocentric Spatial Processing in Human Intracranial EEG. Brain Topogr 2023; 36:870-889. [PMID: 37474691 PMCID: PMC10522529 DOI: 10.1007/s10548-023-00989-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
Spatial reference frames (RFs) play a key role in spatial cognition, especially in perception, spatial memory, and navigation. There are two main types of RFs: egocentric (self-centered) and allocentric (object-centered). Although many fMRI studies examined the neural correlates of egocentric and allocentric RFs, they could not sample the fast temporal dynamics of the underlying cognitive processes. Therefore, the interaction and timing between these two RFs remain unclear. Taking advantage of the high temporal resolution of intracranial EEG (iEEG), we aimed to determine the timing of egocentric and allocentric information processing and describe the brain areas involved. We recorded iEEG and analyzed broad gamma activity (50-150 Hz) in 37 epilepsy patients performing a spatial judgment task in a three-dimensional circular virtual arena. We found overlapping activation for egocentric and allocentric RFs in many brain regions, with several additional egocentric- and allocentric-selective areas. In contrast to the egocentric responses, the allocentric responses peaked later than the control ones in frontal regions with overlapping selectivity. Also, across several egocentric or allocentric selective areas, the egocentric selectivity appeared earlier than the allocentric one. We identified the maximum number of egocentric-selective channels in the medial occipito-temporal region and allocentric-selective channels around the intraparietal sulcus in the parietal cortex. Our findings favor the hypothesis that egocentric spatial coding is a more primary process, and allocentric representations may be derived from egocentric ones. They also broaden the dominant view of the dorsal and ventral streams supporting egocentric and allocentric space coding, respectively.
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Affiliation(s)
- Sofiia Moraresku
- Laboratory of Neurophysiology of Memory, Institute of Physiology, Czech Academy of Sciences, Videnska 1083, 142 20, Prague, Czechia.
- Third Faculty of Medicine, Charles University, Prague, Czechia.
| | - Jiri Hammer
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czechia
| | - Radek Janca
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia
| | - Petr Jezdik
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia
| | - Adam Kalina
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czechia
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czechia
| | - Kamil Vlcek
- Laboratory of Neurophysiology of Memory, Institute of Physiology, Czech Academy of Sciences, Videnska 1083, 142 20, Prague, Czechia.
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13
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Mivalt F, Kremen V, Sladky V, Cui J, Gregg NM, Balzekas I, Marks V, St Louis EK, Croarkin P, Lundstrom BN, Nelson N, Kim J, Hermes D, Messina S, Worrell S, Richner T, Brinkmann BH, Denison T, Miller KJ, Van Gompel J, Stead M, Worrell GA. Impedance Rhythms in Human Limbic System. J Neurosci 2023; 43:6653-6666. [PMID: 37620157 PMCID: PMC10538585 DOI: 10.1523/jneurosci.0241-23.2023] [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: 02/07/2023] [Revised: 08/09/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The impedance is a fundamental electrical property of brain tissue, playing a crucial role in shaping the characteristics of local field potentials, the extent of ephaptic coupling, and the volume of tissue activated by externally applied electrical brain stimulation. We tracked brain impedance, sleep-wake behavioral state, and epileptiform activity in five people with epilepsy living in their natural environment using an investigational device. The study identified impedance oscillations that span hours to weeks in the amygdala, hippocampus, and anterior nucleus thalamus. The impedance in these limbic brain regions exhibit multiscale cycles with ultradian (∼1.5-1.7 h), circadian (∼21.6-26.4 h), and infradian (∼20-33 d) periods. The ultradian and circadian period cycles are driven by sleep-wake state transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Limbic brain tissue impedance reaches a minimum value in NREM sleep, intermediate values in REM sleep, and rises through the day during wakefulness, reaching a maximum in the early evening before sleep onset. Infradian (∼20-33 d) impedance cycles were not associated with a distinct behavioral correlate. Brain tissue impedance is known to strongly depend on the extracellular space (ECS) volume, and the findings reported here are consistent with sleep-wake-dependent ECS volume changes recently observed in the rodent cortex related to the brain glymphatic system. We hypothesize that human limbic brain ECS changes during sleep-wake state transitions underlie the observed multiscale impedance cycles. Impedance is a simple electrophysiological biomarker that could prove useful for tracking ECS dynamics in human health, disease, and therapy.SIGNIFICANCE STATEMENT The electrical impedance in limbic brain structures (amygdala, hippocampus, anterior nucleus thalamus) is shown to exhibit oscillations over multiple timescales. We observe that impedance oscillations with ultradian and circadian periodicities are associated with transitions between wakefulness, NREM, and REM sleep states. There are also impedance oscillations spanning multiple weeks that do not have a clear behavioral correlate and whose origin remains unclear. These multiscale impedance oscillations will have an impact on extracellular ionic currents that give rise to local field potentials, ephaptic coupling, and the tissue activated by electrical brain stimulation. The approach for measuring tissue impedance using perturbational electrical currents is an established engineering technique that may be useful for tracking ECS volume.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital, 60200 Brno, Czech Republic
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, 16000 Prague, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- International Clinical Research Center, St. Anne's University Hospital, 60200 Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University, 16000 Prague, Czech Republic
| | - Jie Cui
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Victoria Marks
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Erik K St Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology and Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota 55905
| | | | - Brian Nils Lundstrom
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Noelle Nelson
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Jiwon Kim
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Steven Messina
- Department of Radiology, Mayo Clinic Rochester, Minnesota 55905
| | - Samuel Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Thomas Richner
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Timothy Denison
- Department of Engineering Science, Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Kai J Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905
| | - Jamie Van Gompel
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905
| | - Matthew Stead
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Gregory A Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
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14
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Hannan S, Thomas J, Jaber K, El Kosseifi C, Ho A, Abdallah C, Avigdor T, Gotman J, Frauscher B. The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy. Ann Neurol 2023. [PMID: 37712215 DOI: 10.1002/ana.26796] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
OBJECTIVE Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics. METHODS We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included. RESULTS The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51). INTERPRETATION In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.
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Affiliation(s)
- Sana Hannan
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
- Department of Biomedical and Life Sciences, Lancaster University, Lancaster, United Kingdom
| | - John Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Kassem Jaber
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Charbel El Kosseifi
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Alyssa Ho
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Tamir Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
- Analytical Neurophysiology Lab, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
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15
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Lepage KQ, Jain S, Kvavilashvili A, Witcher M, Vijayan S. Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data. Bioengineering (Basel) 2023; 10:1009. [PMID: 37760111 PMCID: PMC10525760 DOI: 10.3390/bioengineering10091009] [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: 06/28/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 09/29/2023] Open
Abstract
A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night's worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.
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Affiliation(s)
- Kyle Q. Lepage
- School of Neuroscience, Sandy Hall, Virginia Tech, 210 Drillfield Drive, Blacksburg, VA 24060, USA; (A.K.); (S.V.)
| | - Sparsh Jain
- Department of Biomedical Engineering and Mechanics, Virginia Tech, 325 Stanger St., Blacksburg, VA 24061, USA;
| | - Andrew Kvavilashvili
- School of Neuroscience, Sandy Hall, Virginia Tech, 210 Drillfield Drive, Blacksburg, VA 24060, USA; (A.K.); (S.V.)
| | - Mark Witcher
- Section of Neurosurgery, Carilion Clinic, Carilion Roanoke Memorial Hospital, 1906 Belleview Ave SE, Roanoke, VA 24014, USA;
| | - Sujith Vijayan
- School of Neuroscience, Sandy Hall, Virginia Tech, 210 Drillfield Drive, Blacksburg, VA 24060, USA; (A.K.); (S.V.)
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16
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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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17
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Janca R, Jezdik P, Ebel M, Kalina A, Kudr M, Jahodova A, Krysl D, Mackova K, Straka B, Marusic P, Krsek P. Distinct patterns of interictal intracranial EEG in focal cortical dysplasia type I and II. Clin Neurophysiol 2023; 151:10-17. [PMID: 37121217 DOI: 10.1016/j.clinph.2023.03.360] [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: 12/28/2022] [Revised: 03/13/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling. METHODS This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types. RESULTS Patients with FCD type II had a significantly higher IED rate (p < 0.005), a shorter inter-discharge interval within RD episodes (p < 0.003), sleep influence on decreased RD periodicity (p < 0.036), and longer RD episode duration (p < 0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy. CONCLUSION Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II. SIGNIFICANCE Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.
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Affiliation(s)
- Radek Janca
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic.
| | - Petr Jezdik
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Matyas Ebel
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Adam Kalina
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Martin Kudr
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Alena Jahodova
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - David Krysl
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Katerina Mackova
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Barbora Straka
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Petr Marusic
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Pavel Krsek
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
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18
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Huang Y, Wang JB, Parker JJ, Shivacharan R, Lal RA, Halpern CH. Spectro-spatial features in distributed human intracranial activity proactively encode peripheral metabolic activity. Nat Commun 2023; 14:2729. [PMID: 37169738 PMCID: PMC10174612 DOI: 10.1038/s41467-023-38253-7] [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: 08/23/2022] [Accepted: 04/17/2023] [Indexed: 05/13/2023] Open
Abstract
Mounting evidence demonstrates that the central nervous system (CNS) orchestrates glucose homeostasis by sensing glucose and modulating peripheral metabolism. Glucose responsive neuronal populations have been identified in the hypothalamus and several corticolimbic regions. However, how these CNS gluco-regulatory regions modulate peripheral glucose levels is not well understood. To better understand this process, we simultaneously measured interstitial glucose concentrations and local field potentials in 3 human subjects from cortical and subcortical regions, including the hypothalamus in one subject. Correlations between high frequency activity (HFA, 70-170 Hz) and peripheral glucose levels are found across multiple brain regions, notably in the hypothalamus, with correlation magnitude modulated by sleep-wake cycles, circadian coupling, and hypothalamic connectivity. Correlations are further present between non-circadian (ultradian) HFA and glucose levels which are higher during awake periods. Spectro-spatial features of neural activity enable decoding of peripheral glucose levels both in the present and up to hours in the future. Our findings demonstrate proactive encoding of homeostatic glucose dynamics by the CNS.
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Affiliation(s)
- Yuhao Huang
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Jeffrey B Wang
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
- Medical Scientist Training Program, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Jonathon J Parker
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Rajat Shivacharan
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Rayhan A Lal
- Department of Medicine (Endocrinology), Stanford University Medical Center, Stanford, CA, 94305, USA.
- Department of Pediatrics (Endocrinology), Stanford University Medical Center, Stanford, CA, 94305, USA.
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA.
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19
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Long S, Bruzzone M, Mitropanopoulos S, Kalamangalam G, Gunduz A. Identification and classification of pathology and artifacts for human intracranial cognitive research. Neuroimage 2023; 270:119961. [PMID: 36848970 PMCID: PMC10461234 DOI: 10.1016/j.neuroimage.2023.119961] [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: 08/02/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023] Open
Abstract
Intracranial electroencephalography (iEEG) presents a unique opportunity to extend human neuroscientific understanding. However, typically iEEG is collected from patients diagnosed with focal drug-resistant epilepsy (DRE) and contains transient bursts of pathological activity. This activity disrupts performances on cognitive tasks and can distort findings from human neurophysiology studies. In addition to manual marking by a trained expert, numerous IED detectors have been developed to identify these pathological events. Even so, the versatility and usefulness of these detectors is limited by training on small datasets, incomplete performance metrics, and lack of generalizability to iEEG. Here, we employed a large annotated public iEEG dataset from two institutions to train a random forest classifier (RFC) to distinguish data segments as either 'non-cerebral artifact' (n = 73,902), 'pathological activity' (n = 67,797), or 'physiological activity' (n = 151,290). We found our model performed with an accuracy of 0.941, specificity of 0.950, sensitivity of 0.908, precision of 0.911, and F1 score of 0.910, averaged across all three event types. We extended the generalizability of our model to continuous bipolar data collected in a task-state at a different institution with a lower sampling rate and found our model performed with an accuracy of 0.789, specificity of 0.806, and sensitivity of 0.742, averaged across all three event types. Additionally, we created a custom graphical user interface to implement our classifier and enhance usability.
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Affiliation(s)
- Sarah Long
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, United States
| | - Maria Bruzzone
- Wilder Center for Epilepsy, Department of Neurology, University of Florida, United States
| | | | - Giridhar Kalamangalam
- Wilder Center for Epilepsy, Department of Neurology, University of Florida, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, United States; Wilder Center for Epilepsy, Department of Neurology, University of Florida, United States.
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20
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Basu I, Yousefi A, Crocker B, Zelmann R, Paulk AC, Peled N, Ellard KK, Weisholtz DS, Cosgrove GR, Deckersbach T, Eden UT, Eskandar EN, Dougherty DD, Cash SS, Widge AS. Closed-loop enhancement and neural decoding of cognitive control in humans. Nat Biomed Eng 2023; 7:576-588. [PMID: 34725508 PMCID: PMC9056584 DOI: 10.1038/s41551-021-00804-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/02/2021] [Indexed: 12/20/2022]
Abstract
Deficits in cognitive control-that is, in the ability to withhold a default pre-potent response in favour of a more adaptive choice-are common in depression, anxiety, addiction and other mental disorders. Here we report proof-of-concept evidence that, in participants undergoing intracranial epilepsy monitoring, closed-loop direct stimulation of the internal capsule or striatum, especially the dorsal sites, enhances the participants' cognitive control during a conflict task. We also show that closed-loop stimulation upon the detection of lapses in cognitive control produced larger behavioural changes than open-loop stimulation, and that task performance for single trials can be directly decoded from the activity of a small number of electrodes via neural features that are compatible with existing closed-loop brain implants. Closed-loop enhancement of cognitive control might remediate underlying cognitive deficits and aid the treatment of severe mental disorders.
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Affiliation(s)
- Ishita Basu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ali Yousefi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Departments of Computer Science and Neuroscience, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Noam Peled
- Department of Radiology, MGH/HST Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Kristen K Ellard
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - G Rees Cosgrove
- Department of Neurological Surgery, Brigham & Womens Hospital, Boston, MA, USA
| | - Thilo Deckersbach
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Emad N Eskandar
- Department of Neurological Surgery, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurological Surgery, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.
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21
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Cui J, Balzekas I, Nurse E, Viana P, Gregg N, Karoly P, Worrell G, Richardson MP, Freestone DR, Brinkmann BH. Perceived seizure risk in epilepsy â€" Chronic electronic surveys with and without concurrent EEG. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.23.23287561. [PMID: 37034596 PMCID: PMC10081426 DOI: 10.1101/2023.03.23.23287561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Objective Previous studies suggested that patients with epilepsy might be able to fore-cast their own seizures. We sought to assess the relationships of premonitory symptoms and perceived seizure risk with future and recent self-reported and EEG-confirmed seizures in the subjects living with epilepsy in their natural home environments. Methods We collected long-term e-surveys from ambulatory patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication compliance, sleep quality, mood, stress, perceived seizure risk and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with device seizure forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results Sixty-nine subjects returned 12,590 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed increased stress (OR = 2.52, 95% CI = [1.52, 4.14], p < 0.001) and decreased mood (0.32, [0.13, 0.82], 0.02) were associated with increased relative odds of future self-reported seizures. On multivariate analysis, previous self-reported seizures (4.24, [2.69, 6.68], < 0.001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (3.30, [1.97, 5.52], < 0.001) remained significant when prior self-reported seizures were added to the model. No significant association was found between e-survey responses and subsequent EEG seizures. Significance It appears that patients may tend to self-forecast seizures that occur in sequential groupings. Our results suggest that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting. Key points Long-term e-surveys data and concurrent EEG signals were collected across three study sites to assess the ability of the patients to self-forecast their seizures.Patients may tend to self-forecast self-reported seizures that occur in sequential groupings.Factors, such as mood and stress, may not be independent premonitory symptoms but may be the consequence of recent seizures.No ability to self-forecast EEG confirmed seizures was observed in a small cohort with concurrent EEG validation.A mathematic relation between OR and AUC provides a means to compare forecasting performance between survey and device studies.
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Affiliation(s)
- Jie Cui
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Irena Balzekas
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan Nurse
- Seer Medical, Melbourne, Australia
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Pedro Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Faculty of Medicine, University of Lisbon, Portugal
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Philippa Karoly
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | | | - Benjamin H. Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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22
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Thomas J, Kahane P, Abdallah C, Avigdor T, Zweiphenning WJEM, Chabardes S, Jaber K, Latreille V, Minotti L, Hall J, Dubeau F, Gotman J, Frauscher B. A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome. Ann Neurol 2023; 93:522-535. [PMID: 36373178 DOI: 10.1002/ana.26548] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. METHODS We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. RESULTS The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. INTERPRETATION Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.
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Affiliation(s)
- John Thomas
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Chifaou Abdallah
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Tamir Avigdor
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Willemiek J E M Zweiphenning
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Stephan Chabardes
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Kassem Jaber
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Véronique Latreille
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Lorella Minotti
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Jeff Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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23
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Azeem A, von Ellenrieder N, Royer J, Frauscher B, Bernhardt B, Gotman J. Integration of white matter architecture to stereo-EEG better describes epileptic spike propagation. Clin Neurophysiol 2023; 146:135-146. [PMID: 36379837 DOI: 10.1016/j.clinph.2022.10.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/12/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Stereo-electroencephalography (SEEG)-derived epilepsy networks are used to better understand a patient's epilepsy; however, a unimodal approach provides an incomplete picture. We combine tractography and SEEG to determine the relationship between spike propagation and the white matter architecture and to improve our understanding of spike propagation mechanisms. METHODS Probablistic tractography from diffusion imaging (dMRI) of matched subjects from the Human Connectome Project (HCP) was combined with patient-specific SEEG-derived spike propagation networks. Two regions-of-interest (ROIs) with a significant spike propagation relationship constituted a Propagation Pair. RESULTS In 56 of 59 patients, Propagation Pairs were more often tract-connected as compared to all ROI pairs (p < 0.01; d = -1.91). The degree of spike propagation between tract-connected ROIs was greater (39 ± 21%) compared to tract-unconnected ROIs (31 ± 18%; p < 0.0001). Within the same network, ROIs receiving propagation earlier were more often tract-connected to the source (59.7%) as compared to late receivers (25.4%; p < 0.0001). CONCLUSIONS Brain regions involved in spike propagation are more likely to be connected by white matter tracts. Between nodes, presence of tracts suggests a direct course of propagation, whereas the absence of tracts suggests an indirect course of propagation. SIGNIFICANCE We demonstrate a logical and consistent relationship between spike propagation and the white matter architecture.
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Affiliation(s)
- Abdullah Azeem
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Nicolás von Ellenrieder
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jessica Royer
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Birgit Frauscher
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; Department of Neurology & Neurosurgery, Montreal Neurological Hospital, Montréal, QC, Canada
| | - Boris Bernhardt
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jean Gotman
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
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24
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Cousyn L, Messaoud RB, Lehongre K, Frazzini V, Lambrecq V, Adam C, Mathon B, Navarro V, Chavez M. Daily resting-state intracranial EEG connectivity for seizure risk forecasts. Epilepsia 2023; 64:e23-e29. [PMID: 36481871 DOI: 10.1111/epi.17480] [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: 05/10/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.
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Affiliation(s)
- Louis Cousyn
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Rémy Ben Messaoud
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- INRIA, ARAMIS Project-Team, Paris, France
| | - Katia Lehongre
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
| | - Valerio Frazzini
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Virginie Lambrecq
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Claude Adam
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
| | - Bertrand Mathon
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Sorbonne University, Paris, France
- Department of Neurosurgery, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Vincent Navarro
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Mario Chavez
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
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25
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Nejedly P, Kremen V, Lepkova K, Mivalt F, Sladky V, Pridalova T, Plesinger F, Jurak P, Pail M, Brazdil M, Klimes P, Worrell G. Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification. Sci Rep 2023; 13:744. [PMID: 36639549 PMCID: PMC9839708 DOI: 10.1038/s41598-023-27978-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/11/2023] [Indexed: 01/14/2023] Open
Abstract
Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.
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Affiliation(s)
- Petr Nejedly
- 1St Department of Neurology, Faculty of Medicine, Masaryk University, Brno, Czech Republic. .,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic. .,Department of Neurology, Mayo Clinic, Mayo Systems Electrophysiology Laboratory, Rochester, MN, USA.
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Mayo Systems Electrophysiology Laboratory, Rochester, MN, USA. .,Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.
| | - Kamila Lepkova
- Department of Neurology, Mayo Clinic, Mayo Systems Electrophysiology Laboratory, Rochester, MN, USA.,Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Filip Mivalt
- Department of Neurology, Mayo Clinic, Mayo Systems Electrophysiology Laboratory, Rochester, MN, USA.,Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
| | - Vladimir Sladky
- Department of Neurology, Mayo Clinic, Mayo Systems Electrophysiology Laboratory, Rochester, MN, USA
| | - Tereza Pridalova
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.,Department of Neurology, Mayo Clinic, Mayo Systems Electrophysiology Laboratory, Rochester, MN, USA
| | - Filip Plesinger
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Pavel Jurak
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Martin Pail
- 1St Department of Neurology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Milan Brazdil
- 1St Department of Neurology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Petr Klimes
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Mayo Systems Electrophysiology Laboratory, Rochester, MN, USA.
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26
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Liu AA, Henin S, Abbaspoor S, Bragin A, Buffalo EA, Farrell JS, Foster DJ, Frank LM, Gedankien T, Gotman J, Guidera JA, Hoffman KL, Jacobs J, Kahana MJ, Li L, Liao Z, Lin JJ, Losonczy A, Malach R, van der Meer MA, McClain K, McNaughton BL, Norman Y, Navas-Olive A, de la Prida LM, Rueckemann JW, Sakon JJ, Skelin I, Soltesz I, Staresina BP, Weiss SA, Wilson MA, Zaghloul KA, Zugaro M, Buzsáki G. A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations. Nat Commun 2022; 13:6000. [PMID: 36224194 PMCID: PMC9556539 DOI: 10.1038/s41467-022-33536-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 09/21/2022] [Indexed: 02/05/2023] Open
Abstract
Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.
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Affiliation(s)
- Anli A Liu
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA
| | - Simon Henin
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Saman Abbaspoor
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Anatol Bragin
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Elizabeth A Buffalo
- Department of Physiology and Biophysics, Washington National Primate Center, University of Washington, Seattle, WA, USA
| | - Jordan S Farrell
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - David J Foster
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Tamara Gedankien
- Department of Biomedical Engineering, Department of Neurological Surgery, Columbia University, New York, NY, USA
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jennifer A Guidera
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program, Department of Bioengineering, University of California, San Francisco, San Francisco, CA, USA
| | - Kari L Hoffman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Joshua Jacobs
- Department of Biomedical Engineering, Department of Neurological Surgery, Columbia University, New York, NY, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lin Li
- Department of Biomedical Engineering, University of North Texas, Denton, TX, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - Jack J Lin
- Department of Neurology, Center for Mind and Brain, University of California Davis, Oakland, CA, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - Rafael Malach
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | | | - Kathryn McClain
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA
| | - Bruce L McNaughton
- The Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada
| | - Yitzhak Norman
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | | | | | - Jon W Rueckemann
- Department of Physiology and Biophysics, Washington National Primate Center, University of Washington, Seattle, WA, USA
| | - John J Sakon
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ivan Skelin
- Department of Neurology, Center for Mind and Brain, University of California Davis, Oakland, CA, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Bernhard P Staresina
- Department of Experimental Psychology, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Shennan A Weiss
- Brookdale Hospital Medical Center, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences and Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, USA
| | - Michaël Zugaro
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
| | - György Buzsáki
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA.
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA.
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27
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Gong Y, Xu C, Wang S, Wang Y, Chen Z. Computerized application for epilepsy in China: Does the era of artificial intelligence comes? Acta Neurol Scand 2022; 146:732-742. [PMID: 36156212 DOI: 10.1111/ane.13711] [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: 07/31/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 12/01/2022]
Abstract
Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.
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Affiliation(s)
- Yiwei Gong
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuang Wang
- Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
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28
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Fukumori K, Yoshida N, Sugano H, Nakajima M, Tanaka T. Satelight: Self-attention-based model for epileptic spike detection from multi-electrode EEG. J Neural Eng 2022; 19. [PMID: 36073896 DOI: 10.1088/1741-2552/ac9050] [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: 04/30/2022] [Accepted: 09/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Because of the lack of highly skilled experts, automated technologies that support electroencephalogram (EEG)-based in epilepsy diagnosis are advancing. Deep convolutional neural network-based models have been used successfully for detecting epileptic spikes, one of the biomarkers, from EEG. However, a sizeable number of supervised EEG records are required for training. APPROACH This study introduces the Satelight model, which uses the self-attention (SA) mechanism. The model was trained using a clinical EEG dataset labeled by ve specialists, including 16,008 epileptic spikes and 15,478 artifacts from 50 children. The SA mechanism is expected to reduce the number of parameters and efficiently extract features from a small amount of EEG data. To validate the effectiveness, we compared various spike detection approaches with the clinical EEG data. MAIN RESULTS The experimental results showed that the proposed method detected epileptic spikes more effectively than other models (accuracy = 0:876 and false positive rate = 0:133). SIGNIFICANCE The proposed model had only one-tenth the number of parameters as the other effective model, despite having such a high detection performance. Further exploration of the hidden parameters revealed that the model automatically attended to the EEG's characteristic waveform locations of interest.
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Affiliation(s)
- Kosuke Fukumori
- Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei-shi, Tokyo, 184-8588, JAPAN
| | - Noboru Yoshida
- Juntendo University Nerima Hospital, 3-1-10 Takanodai, Nerima-ku, Tokyo, 177-8521, JAPAN
| | - Hidenori Sugano
- Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, JAPAN
| | - Madoka Nakajima
- Juntendo University School of Medicine, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-0033, JAPAN
| | - Toshihisa Tanaka
- Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei-shi, Tokyo, 184-8588, JAPAN
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29
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Feng Y, Quon RJ, Jobst BC, Casey MA. Evoked responses to note onsets and phrase boundaries in Mozart's K448. Sci Rep 2022; 12:9632. [PMID: 35688855 PMCID: PMC9187696 DOI: 10.1038/s41598-022-13710-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 04/25/2022] [Indexed: 11/29/2022] Open
Abstract
Understanding the neural correlates of perception of hierarchical structure in music presents a direct window into auditory organization. To examine the hypothesis that high-level and low-level structures—i.e. phrases and notes—elicit different neural responses, we collected intracranial electroencephalography (iEEG) data from eight subjects during exposure to Mozart’s K448 and directly compared Event-related potentials (ERPs) due to note onsets and those elicited by phrase boundaries. Cluster-level permutation tests revealed that note-onset-related ERPs and phrase-boundary-related ERPs were significantly different at \documentclass[12pt]{minimal}
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\begin{document}$$-150$$\end{document}-150, 200, and 450 ms relative to note onset and phrase markers. We also observed increased activity in frontal brain regions when processing phrase boundaries. We relate these observations to (1) a process which syntactically binds notes together hierarchically to form larger phrases; (2) positive emotions induced by successful prediction of forthcoming phrase boundaries and violations of melodic expectations at phrase boundaries.
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Affiliation(s)
- Yijing Feng
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Robert J Quon
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA.,Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Barbara C Jobst
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA.,Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Michael A Casey
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA. .,Department of Music, Dartmouth College, Hanover, NH, 03755, USA.
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30
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Sladky V, Nejedly P, Mivalt F, Brinkmann BH, Kim I, St. Louis EK, Gregg NM, Lundstrom BN, Crowe CM, Attia TP, Crepeau D, Balzekas I, Marks VS, Wheeler LP, Cimbalnik J, Cook M, Janca R, Sturges BK, Leyde K, Miller KJ, Van Gompel JJ, Denison T, Worrell GA, Kremen V. Distributed brain co-processor for tracking spikes, seizures and behavior during electrical brain stimulation. Brain Commun 2022; 4:fcac115. [PMID: 35755635 PMCID: PMC9217965 DOI: 10.1093/braincomms/fcac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/12/2022] [Accepted: 05/05/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behavior and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a hand-held device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes, and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from 9 humans and 8 canines with epilepsy, and then implemented prospectively in out-of-sample testing in 2 pet canines and 4 humans with drug resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures, and correlation with patient behavioral reports. In the future correlation of spikes and seizures with behavior will allow more detailed investigation of the clinical impact of spikes and seizures on patients.
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Affiliation(s)
- Vladimir Sladky
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Petr Nejedly
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | - Filip Mivalt
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
| | - Benjamin H. Brinkmann
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Inyong Kim
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Erik K. St. Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pul-monary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
| | - Nicholas M. Gregg
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Brian N. Lundstrom
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Chelsea M. Crowe
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, USA
| | - Tal Pal Attia
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Daniel Crepeau
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Irena Balzekas
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, MN, USA
- Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, MN, USA
| | - Victoria S. Marks
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, MN, USA
| | - Lydia P. Wheeler
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, MN, USA
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Mark Cook
- Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia
| | - Radek Janca
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Second Faculty of Medicine, Motol University Hospital, Charles University, Prague, Czech Republic
| | - Beverly K. Sturges
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, USA
| | | | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | | | | | - Gregory A. Worrell
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Vaclav Kremen
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
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31
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von Ellenrieder N, Peter-Derex L, Gotman J, Frauscher B. SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only. J Neural Eng 2022; 19. [PMID: 35439736 DOI: 10.1088/1741-2552/ac6829] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/18/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction. APPROACH Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch. MAIN RESULTS An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%. SIGNIFICANCE The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.
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Affiliation(s)
- Nicolás von Ellenrieder
- Montreal Neurological Institute and Hospital, McGill University, 3801 University streeet, Montreal, Quebec, H3A 2B4, CANADA
| | - Laure Peter-Derex
- PAM Team, Centre de Recherche en Neurosciences de Lyon, 95 Boulevard Pinel, Lyon, Rhône-Alpes , 69675 BRON, FRANCE
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, 3801 University St, Montreal, Quebec, H3A 2B4, CANADA
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CANADA
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32
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Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning. eNeuro 2022; 9:ENEURO.0361-21.2022. [PMID: 35105662 PMCID: PMC8896554 DOI: 10.1523/eneuro.0361-21.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/03/2021] [Accepted: 01/03/2022] [Indexed: 12/29/2022] Open
Abstract
Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual's estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low-dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.
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33
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Paulk AC, Zelmann R, Crocker B, Widge AS, Dougherty DD, Eskandar EN, Weisholtz DS, Richardson RM, Cosgrove GR, Williams ZM, Cash SS. Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters. Brain Stimul 2022; 15:491-508. [PMID: 35247646 PMCID: PMC8985164 DOI: 10.1016/j.brs.2022.02.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses. OBJECTIVE To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated. METHODS We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites). RESULTS Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation. CONCLUSION These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Darin D Dougherty
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Daniel S Weisholtz
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - R Mark Richardson
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - Ziv M Williams
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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Paulk AC, Kfir Y, Khanna AR, Mustroph ML, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson RM, Williams ZM, Cash SS. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat Neurosci 2022; 25:252-263. [PMID: 35102333 DOI: 10.1038/s41593-021-00997-0] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/07/2021] [Indexed: 12/20/2022]
Abstract
Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.
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Affiliation(s)
- 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.
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Arjun R Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York City, NY, USA
- Zuckerman Institute, Columbia University, New York City, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York City, NY, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Columbia University, New York City, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sergey D Stavisky
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurological Surgery, University of California at Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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35
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Tchoe Y, Bourhis AM, Cleary DR, Stedelin B, Lee J, Tonsfeldt KJ, Brown EC, Siler DA, Paulk AC, Yang JC, Oh H, Ro YG, Lee K, Russman SM, Ganji M, Galton I, Ben-Haim S, Raslan AM, Dayeh SA. Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics. Sci Transl Med 2022; 14:eabj1441. [PMID: 35044788 DOI: 10.1126/scitranslmed.abj1441] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively used for research in brain-machine interfaces. However, the existing clinical and experimental devices are often limited in either spatial resolution or cortical coverage. Here, we developed scalable manufacturing processes with a dense electrical connection scheme to achieve reconfigurable thin-film, multithousand-channel neurophysiological recording grids using platinum nanorods (PtNRGrids). With PtNRGrids, we have achieved a multithousand-channel array of small (30 μm) contacts with low impedance, providing high spatial and temporal resolution over a large cortical area. We demonstrated that PtNRGrids can resolve submillimeter functional organization of the barrel cortex in anesthetized rats that captured the tissue structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. In addition, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1-mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrated the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces.
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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
| | - Andrew M Bourhis
- 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.,Department of Neurological Surgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Brittany Stedelin
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Jihwan Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, 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
| | - Erik C Brown
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Dominic A Siler
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jimmy C Yang
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hongseok Oh
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Yun Goo Ro
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, 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
| | - Samantha M Russman
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Mehran Ganji
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Ian Galton
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Sharona Ben-Haim
- 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, University of California San Diego, La Jolla, CA 92093, USA
| | - Ahmed M Raslan
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, 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.,Department of Neurological Surgery, University of California San Diego, La Jolla, CA 92093, USA.,Graduate Program of Materials Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
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36
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Gregg NM, Sladky V, Nejedly P, Mivalt F, Kim I, Balzekas I, Sturges BK, Crowe C, Patterson EE, Van Gompel JJ, Lundstrom BN, Leyde K, Denison TJ, Brinkmann BH, Kremen V, Worrell GA. Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy. Sci Rep 2021; 11:24250. [PMID: 34930926 PMCID: PMC8688461 DOI: 10.1038/s41598-021-03555-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/03/2021] [Indexed: 11/30/2022] Open
Abstract
Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.
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Affiliation(s)
- Nicholas M Gregg
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Vladimir Sladky
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- International Clinical Research Center, St. Anne's University Hospital, 656 91, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, 272 01, Kladno, Czech Republic
| | - Petr Nejedly
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- International Clinical Research Center, St. Anne's University Hospital, 656 91, Brno, Czech Republic
| | - Filip Mivalt
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- International Clinical Research Center, St. Anne's University Hospital, 656 91, Brno, Czech Republic
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, 616 00, Brno, Czech Republic
| | - Inyong Kim
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
| | - Irena Balzekas
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- Mayo Clinic School of Medicine and the Medical Scientist Training Program, Mayo Clinic, Rochester, MN, 55905, USA
| | - Beverly K Sturges
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, 95616, USA
| | - Chelsea Crowe
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, 95616, USA
| | - Edward E Patterson
- Department of Veterinary Clinical Sciences, University of Minnesota College of Veterinary Medicine, St. Paul, MN, 55108, USA
| | | | - Brian N Lundstrom
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kent Leyde
- Cadence Neuroscience, Seattle, WA, 98052, USA
| | - Timothy J Denison
- Institute for Biomedical Engineering, Oxford University, Oxford, OX3 7DQ, UK
| | - Benjamin H Brinkmann
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
| | - Vaclav Kremen
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, 160 00, Prague, Czech Republic
| | - Gregory A Worrell
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
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Klimes P, Peter-Derex L, Hall J, Dubeau F, Frauscher B. Spatio-temporal spike dynamics predict surgical outcome in adult focal epilepsy. Clin Neurophysiol 2021; 134:88-99. [PMID: 34991017 DOI: 10.1016/j.clinph.2021.10.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/27/2021] [Accepted: 10/29/2021] [Indexed: 01/05/2023]
Abstract
OBJECTIVE We hypothesized that spatio-temporal dynamics of interictal spikes reflect the extent and stability of epileptic sources and determine surgical outcome. METHODS We studied 30 consecutive patients (14 good outcome). Spikes were detected in prolonged stereo-electroencephalography recordings. We quantified the spatio-temporal dynamics of spikes using the variance of the spike rate, line length and skewness of the spike distribution, and related these features to outcome. We built a logistic regression model, and compared its performance to traditional markers. RESULTS Good outcome patients had more dominant and stable sources than poor outcome patients as expressed by a higher variance of spike rates, a lower variance of line length, and a lower variance of positive skewness (ps < 0.05). The outcome was correctly predicted in 80% of patients. This was better or non-inferior to predictions based on a focal lesion (p = 0.016), focal seizure-onset zone, or complete resection (ps > 0.05). In the five patients where traditional markers failed, spike distribution predicted the outcome correctly. The best results were achieved by 18-h periods or longer. CONCLUSIONS Analysis of spike dynamics shows that surgery outcome depends on strong, single and stable sources. SIGNIFICANCE Our quantitative method has the potential to be a reliable predictor of surgical outcome.
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Affiliation(s)
- Petr Klimes
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.
| | - Laure Peter-Derex
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Center for Sleep Medicine and Respiratory Diseases, Lyon University Hospital, Lyon 1 University, Lyon, France; Lyon Neuroscience Research Center, Lyon, France
| | - Jeff Hall
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
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Sultana S, Hitomi T, Kobayashi MD, Shimotake A, Matsuhashi M, Takahashi R, Ikeda A. Long Time Constant May Endorses Sharp Waves and Spikes Than Sharp Transients in Scalp Electroencephalography: A Comparison of Both After-Slow Among Different Time Constant and High-Frequency Activity Analysis. Front Hum Neurosci 2021; 15:748893. [PMID: 34744663 PMCID: PMC8569184 DOI: 10.3389/fnhum.2021.748893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/28/2021] [Indexed: 11/29/2022] Open
Abstract
Objective: To clarify whether long time constant (TC) is useful for detecting the after-slow activity of epileptiform discharges (EDs): sharp waves and spikes and for differentiating EDs from sharp transients (Sts). Methods: We employed 68 after-slow activities preceded by 32 EDs (26 sharp waves and six spikes) and 36 Sts from 52 patients with partial and generalized epilepsy (22 men, 30 women; mean age 39.08 ± 13.13 years) defined by visual inspection. High-frequency activity (HFA) associated with the apical component of EDs and Sts was also investigated to endorse two groups. After separating nine Sts that were labeled by visual inspection but did not fulfill the amplitude criteria for after-slow of Sts, 59 activities (32 EDs and 27 Sts) were analyzed about the total area of after-slow under three TCs (long: 2 s; conventional: 0.3 s; and short: 0.1 s). Results: Compared to Sts, HFA was found significantly more with the apical component of EDs (p < 0.05). The total area of after-slow in all 32 EDs under TC 2 s was significantly larger than those under TC 0.3 s and 0.1 s (p < 0.001). Conversely, no significant differences were observed in the same parameter of 27 Sts among the three different TCs. Regarding separated nine Sts, the total area of after-slow showed a similar tendency to that of 27 Sts under three different TCs. Significance: These results suggest that long TC could be useful for selectively endorsing after-slow of EDs and differentiating EDs from Sts. These findings are concordant with the results of the HFA analysis. Visual inspection is also equally good as the total area of after-slow analysis.
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Affiliation(s)
- Shamima Sultana
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takefumi Hitomi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Clinical Laboratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Akihiro Shimotake
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Quon RJ, Meisenhelter S, Camp EJ, Testorf ME, Song Y, Song Q, Culler GW, Moein P, Jobst BC. AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges. Clin Neurophysiol 2021; 133:1-8. [PMID: 34773796 DOI: 10.1016/j.clinph.2021.09.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/02/2021] [Accepted: 09/21/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algorithm and convolutional neural network (CNN) for the detection of intracranial IEDs ("AiED"). METHODS 1000 intracranial electroencephalogram (EEG) epochs extracted randomly from 307 subjects with refractory epilepsy were annotated independently by two expert neurophysiologists. These annotated epochs were divided into 1062 two-second epochs with IEDs and 1428 two-second epochs without IEDs, which were transformed into spectrograms prior to training the neural network. The highest performing network was validated on an annotated external test set. RESULTS The final network had an F1-score of 0.95 (95% CI: 0.91-0.98) and an average Area Under the Receiver Operating Characteristic of 0.98 (95% CI: 0.96-1.00). For the external test set, it showed an overall F1-score of 0.71, correctly identifying 100% of all high-amplitude IED complexes, 96.23% of all high-amplitude isolated IEDs, and 66.15% of all IEDs of atypical morphology. CONCLUSIONS Template-matching combined with a CNN offers a fast, robust method for detecting intracranial IEDs. SIGNIFICANCE "AiED" is generalizable and achieves comparable performance to human reviewers; it may support clinical and research EEG analyses.
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Affiliation(s)
- Robert J Quon
- Department of Neurology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA; Department of Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
| | | | - Edward J Camp
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
| | - Markus E Testorf
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA; Thayer School of Engineering at Dartmouth College, Hanover, NH, USA.
| | - Yinchen Song
- Department of Neurology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA; Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
| | - Qingyuan Song
- Department of Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
| | - George W Culler
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
| | - Payam Moein
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
| | - Barbara C Jobst
- Department of Neurology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA; Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
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40
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Khuvis S, Hwang ST, Mehta AD. Intracranial EEG Biomarkers for Seizure Lateralization in Rapidly-Bisynchronous Epilepsy After Laser Corpus Callosotomy. Front Neurol 2021; 12:696492. [PMID: 34690909 PMCID: PMC8531267 DOI: 10.3389/fneur.2021.696492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: It has been asserted that high-frequency analysis of intracranial EEG (iEEG) data may yield information useful in localizing epileptogenic foci. Methods: We tested whether proposed biomarkers could predict lateralization based on iEEG data collected prior to corpus callosotomy (CC) in three patients with bisynchronous epilepsy, whose seizures lateralized definitively post-CC. Lateralization data derived from algorithmically-computed ictal phase-locked high gamma (PLHG), high gamma amplitude (HGA), and low-frequency (filtered) line length (LFLL), as well as interictal high-frequency oscillation (HFO) and interictal epileptiform discharge (IED) rate metrics were compared against ground-truth lateralization from post-CC ictal iEEG. Results: Pre-CC unilateral IEDs were more frequent on the more-pathologic side in all subjects. HFO rate predicted lateralization in one subject, but was sensitive to detection threshold. On pre-CC data, no ictal metric showed better predictive power than any other. All post-corpus callosotomy seizures lateralized to the pathological hemisphere using PLHG, HGA, and LFLL metrics. Conclusions: While quantitative metrics of IED rate and ictal HGA, PHLG, and LFLL all accurately lateralize based on post-CC iEEG, only IED rate consistently did so based on pre-CC data. Significance: Quantitative analysis of IEDs may be useful in lateralizing seizure pathology. More work is needed to develop reliable techniques for high-frequency iEEG analysis.
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Affiliation(s)
- Simon Khuvis
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States.,Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Sean T Hwang
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States
| | - Ashesh D Mehta
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States.,Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
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Leeman-Markowski B, Hardstone R, Lohnas L, Cowen B, Davachi L, Doyle W, Dugan P, Friedman D, Liu A, Melloni L, Selesnick I, Wang B, Meador K, Devinsky O. Effects of hippocampal interictal discharge timing, duration, and spatial extent on list learning. Epilepsy Behav 2021; 123:108209. [PMID: 34416521 PMCID: PMC9169111 DOI: 10.1016/j.yebeh.2021.108209] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/11/2021] [Accepted: 06/30/2021] [Indexed: 11/15/2022]
Abstract
Interictal epileptiform discharges (IEDs) can impair memory. The properties of IEDs most detrimental to memory, however, are undefined. We studied the impact of temporal and spatial characteristics of IEDs on list learning. Subjects completed a memory task during intracranial EEG recordings including hippocampal depth and temporal neocortical subdural electrodes. Subjects viewed a series of objects, and after a distracting task, recalled the objects from the list. The impacts of IED presence, duration, and propagation to neocortex during encoding of individual stimuli were assessed. The effects of IED total number and duration during maintenance and recall periods on delayed recall performance were also determined. The influence of IEDs during recall was further investigated by comparing the likelihood of IEDs preceding correctly recalled items vs. periods of no verbal response. Across 6 subjects, we analyzed 28 hippocampal and 139 lateral temporal contacts. Recall performance was poor, with a median of 17.2% correct responses (range 10.4-21.9%). Interictal epileptiform discharges during encoding, maintenance, and recall did not significantly impact task performance, and there was no significant difference between the likelihood of IEDs during correct recall vs. periods of no response. No significant effects of discharge duration during encoding, maintenance, or recall were observed. Interictal epileptiform discharges with spread to lateral temporal cortex during encoding did not adversely impact recall. A post hoc analysis refining model assumptions indicated a negative impact of IED count during the maintenance period, but otherwise confirmed the above results. Our findings suggest no major effect of hippocampal IEDs on list learning, but study limitations, such as baseline hippocampal dysfunction, should be considered. The impact of IEDs during the maintenance period may be a focus of future research.
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Affiliation(s)
- Beth Leeman-Markowski
- Research Service, VA New York Harbor Healthcare System, 423 E. 23rd St., New York, NY 10010, United States; Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Health, 223 E. 34th St., New York, NY 10016, United States.
| | - Richard Hardstone
- Neuroscience Institute, New York University Langone Health, 550 1st Ave., New York, NY, US 10016
| | - Lynn Lohnas
- Department of Psychology, New York University, 6 Washington Pl., New York, NY, US 10003
| | - Benjamin Cowen
- Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY, US 11201
| | - Lila Davachi
- Department of Psychology, New York University, 6 Washington Pl., New York, NY, US 10003
| | - Werner Doyle
- Department of Neurosurgery, New York University Langone Health, 530 1st Ave., New York, NY, US 10016
| | - Patricia Dugan
- Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Health, 223 E. 34th St., New York, NY, US 10016
| | - Daniel Friedman
- Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Health, 223 E. 34th St., New York, NY, US 10016
| | - Anli Liu
- Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Health, 223 E. 34th St., New York, NY, US 10016,Neuroscience Institute, New York University Langone Health, 550 1st Ave., New York, NY, US 10016
| | - Lucia Melloni
- Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Health, 223 E. 34th St., New York, NY, US 10016,Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322 Frankfurt am Main, Germany
| | - Ivan Selesnick
- Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY, US 11201
| | - Binhuan Wang
- Department of Population Health, New York University Langone Health, 180 Madison Ave., New York, NY, US 10016
| | - Kimford Meador
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, 213 Quarry Road, Palo Alto, CA, US 94304
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Health, 223 E. 34th St., New York, NY, US 10016,Neuroscience Institute, New York University Langone Health, 550 1st Ave., New York, NY, US 10016,Department of Neurosurgery, New York University Langone Health, 530 1st Ave., New York, NY, US 10016,Department of Psychiatry, New York University Langone Health, 550 1st Ave., New York, NY, US 10016
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42
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Musical components important for the Mozart K448 effect in epilepsy. Sci Rep 2021; 11:16490. [PMID: 34531410 PMCID: PMC8446029 DOI: 10.1038/s41598-021-95922-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 07/28/2021] [Indexed: 11/08/2022] Open
Abstract
There is growing evidence for the efficacy of music, specifically Mozart’s Sonata for Two Pianos in D Major (K448), at reducing ictal and interictal epileptiform activity. Nonetheless, little is known about the mechanism underlying this beneficial “Mozart K448 effect” for persons with epilepsy. Here, we measured the influence that K448 had on intracranial interictal epileptiform discharges (IEDs) in sixteen subjects undergoing intracranial monitoring for refractory focal epilepsy. We found reduced IEDs during the original version of K448 after at least 30-s of exposure. Nonsignificant IED rate reductions were witnessed in all brain regions apart from the bilateral frontal cortices, where we observed increased frontal theta power during transitions from prolonged musical segments. All other presented musical stimuli were associated with nonsignificant IED alterations. These results suggest that the “Mozart K448 effect” is dependent on the duration of exposure and may preferentially modulate activity in frontal emotional networks, providing insight into the mechanism underlying this response. Our findings encourage the continued evaluation of Mozart’s K448 as a noninvasive, non-pharmacological intervention for refractory epilepsy.
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Yang JC, Paulk AC, Salami P, Lee SH, Ganji M, Soper DJ, Cleary D, Simon M, Maus D, Lee JW, Nahed BV, Jones PS, Cahill DP, Cosgrove GR, Chu CJ, Williams Z, Halgren E, Dayeh S, Cash SS. Microscale dynamics of electrophysiological markers of epilepsy. Clin Neurophysiol 2021; 132:2916-2931. [PMID: 34419344 DOI: 10.1016/j.clinph.2021.06.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics. METHODS Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing. RESULTS We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs. CONCLUSIONS Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network. SIGNIFICANCE These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.
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Affiliation(s)
- Jimmy C Yang
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Sang Heon Lee
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Daniel J Soper
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel Cleary
- Department of Neurosurgery, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mirela Simon
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Garth Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Ziv Williams
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Eric Halgren
- Department of Radiology, University of California, San Diego; 9500 Gilman Dr.; La Jolla, CA 92093, USA
| | - Shadi Dayeh
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA.
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Chen S, Tan Z, Xia W, Gomes CA, Zhang X, Zhou W, Liang S, Axmacher N, Wang L. Theta oscillations synchronize human medial prefrontal cortex and amygdala during fear learning. SCIENCE ADVANCES 2021; 7:7/34/eabf4198. [PMID: 34407939 PMCID: PMC8373137 DOI: 10.1126/sciadv.abf4198] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 06/29/2021] [Indexed: 05/20/2023]
Abstract
Numerous animal studies have demonstrated that fear acquisition and expression rely on the coordinated activity of medial prefrontal cortex (mPFC) and amygdala and that theta oscillations support interregional communication within the fear network. However, it remains unclear whether these results can be generalized to fear learning in humans. We addressed this question using intracranial electroencephalography recordings in 13 patients with epilepsy during a fear conditioning paradigm. We observed increased power and inter-regional synchronization of amygdala and mPFC in theta (4 to 8 hertz) oscillations for conditioned stimulus (CS+) versus CS-. Analysis of information flow revealed that the dorsal mPFC (dmPFC) led amygdala activity in theta oscillations. Last, a computational model showed that trial-by-trial changes in amygdala theta oscillations predicted the model-based associability (i.e., learning rate). This study provides compelling evidence that theta oscillations within and between amygdala, ventral mPFC, and dmPFC constitute a general mechanism of fear learning across species.
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Affiliation(s)
- Si Chen
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Zheng Tan
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wenran Xia
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Carlos Alexandre Gomes
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Xilei Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Wenjing Zhou
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Shuli Liang
- Functional Neurosurgery Department, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Quon RJ, Leslie GA, Camp EJ, Meisenhelter S, Steimel SA, Song Y, Ettinger AB, Bujarski KA, Casey MA, Jobst BC. 40-Hz auditory stimulation for intracranial interictal activity: A pilot study. Acta Neurol Scand 2021; 144:192-201. [PMID: 33893999 DOI: 10.1111/ane.13437] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/08/2021] [Accepted: 04/11/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To study the effects of auditory stimuli on interictal epileptiform discharge (IED) rates evident with intracranial monitoring. MATERIALS AND METHODS Eight subjects undergoing intracranial EEG monitoring for refractory epilepsy participated in this study. Auditory stimuli consisted of a 40-Hz tone, a 440-Hz tone modulated by a 40-Hz sinusoid, Mozart's Sonata for Two Pianos in D Major (K448), and K448 modulated by a 40-Hz sinusoid (modK448). Subjects were stratified into high- and low-IED rate groups defined by baseline IED rates. Subject-level analyses identified individual responses to auditory stimuli, discerned specific brain regions with significant reductions in IED rates, and examined the influence auditory stimuli had on whole-brain sigma power (12-16 Hz). RESULTS All subjects in the high baseline IED group had a significant 35.25% average reduction in IEDs during the 40-Hz tone; subject-level reductions localized to mesial and lateral temporal regions. Exposure to Mozart K448 showed significant yet less homogeneous responses. A post hoc analysis demonstrated two of the four subjects with positive IED responses had increased whole-brain power at the sigma frequency band during 40-Hz stimulation. CONCLUSIONS Our study is the first to evaluate the relationship between 40-Hz auditory stimulation and IED rates in refractory epilepsy. We reveal that 40-Hz auditory stimuli may be a noninvasive adjunctive intervention to reduce IED burden. Our pilot study supports the future examination of 40-Hz auditory stimuli in a larger population of subjects with high baseline IED rates.
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Affiliation(s)
- Robert J. Quon
- Department of Neurology Geisel School of Medicine at Dartmouth Hanover NH USA
| | - Grace A. Leslie
- Department of Music Georgia Institute of Technology Atlanta GA USA
| | - Edward J. Camp
- Department of Neurology Dartmouth‐Hitchcock Medical Center Lebanon NH USA
| | | | - Sarah A. Steimel
- Department of Neurology Geisel School of Medicine at Dartmouth Hanover NH USA
| | - Yinchen Song
- Department of Neurology Geisel School of Medicine at Dartmouth Hanover NH USA
- Department of Neurology Dartmouth‐Hitchcock Medical Center Lebanon NH USA
| | | | - Krzysztof A. Bujarski
- Department of Neurology Geisel School of Medicine at Dartmouth Hanover NH USA
- Department of Neurology Dartmouth‐Hitchcock Medical Center Lebanon NH USA
| | - Michael A. Casey
- Department of Music at Dartmouth College Hanover NH USA
- Department of Computer Science at Dartmouth College Hanover NH USA
| | - Barbara C. Jobst
- Department of Neurology Geisel School of Medicine at Dartmouth Hanover NH USA
- Department of Neurology Dartmouth‐Hitchcock Medical Center Lebanon NH USA
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46
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Liu J, Yu T, Wu J, Pan Y, Tan Z, Liu R, Wang X, Ren L, Wang L. Anterior thalamic stimulation improves working memory precision judgments. Brain Stimul 2021; 14:1073-1080. [PMID: 34284167 DOI: 10.1016/j.brs.2021.07.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 06/25/2021] [Accepted: 07/15/2021] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND The anterior nucleus of thalamus (ANT) has been suggested as an extended hippocampal system. The circuit of ANT and hippocampus has been widely demonstrated to be associated with memory function. Both lesions to each region and disrupting inter-regional information flow can induce working memory impairment. However, the role of this circuit in working memory precision remains unknown. OBJECTIVE To test the role of the hippocampal-anterior thalamic pathway in working memory precision, we delivered intracranially electrical stimulation to the ANT. We hypothesize that ANT stimulation can improve working memory precision. METHODS Presurgical epilepsy patients with depth electrodes in ANT and hippocampus were recruited to perform a color-recall working memory task. Participants were instructed to point out the color they were supposed to recall by clicking a point on the color wheel, while the intracranial EEG data were synchronously recorded. For randomly selected half trials, a bipolar electrical stimulation was delivered to the ANT electrodes. RESULTS We found that compared to non-stimulation trials, working memory precision judgements were significantly improved for stimulation trials. ANT electrical stimulation significantly increased spectral power of gamma (30-100 Hz) oscillations and decreased interictal epileptiform discharges (IED) in the hippocampus. Moreover, the increased gamma power during the pre-stimulus and retrieval period predicted the improvement of working memory precision judgements. CONCLUSION ANT electrical stimulation can improve working memory precision judgements and modulate hippocampal gamma activity, providing direct evidence on the role of the human hippocampal-anterior thalamic axis in working memory precision.
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Affiliation(s)
- Jiali Liu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Tao Yu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; Comprehensive Epilepsy Center of Beijing, The Beijing Key Laboratory of Neuromodulation, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jinfeng Wu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yali Pan
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Zheng Tan
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Ruobing Liu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xueyuan Wang
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; Comprehensive Epilepsy Center of Beijing, The Beijing Key Laboratory of Neuromodulation, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Liankun Ren
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; Comprehensive Epilepsy Center of Beijing, The Beijing Key Laboratory of Neuromodulation, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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Paulk AC, Yang JC, Cleary DR, Soper DJ, Halgren M, O’Donnell AR, Lee SH, Ganji M, Ro YG, Oh H, Hossain L, Lee J, Tchoe Y, Rogers N, Kiliç K, Ryu SB, Lee SW, Hermiz J, Gilja V, Ulbert I, Fabó D, Thesen T, Doyle WK, Devinsky O, Madsen JR, Schomer DL, Eskandar EN, Lee JW, Maus D, Devor A, Fried SI, Jones PS, Nahed BV, Ben-Haim S, Bick SK, Richardson RM, Raslan AM, Siler DA, Cahill DP, Williams ZM, Cosgrove GR, Dayeh SA, Cash SS. Microscale Physiological Events on the Human Cortical Surface. Cereb Cortex 2021; 31:3678-3700. [PMID: 33749727 PMCID: PMC8258438 DOI: 10.1093/cercor/bhab040] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 01/14/2023] Open
Abstract
Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jimmy C Yang
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Daniel R Cleary
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel J Soper
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Mila Halgren
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Sang Heon Lee
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Yun Goo Ro
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Hongseok Oh
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Lorraine Hossain
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Jihwan Lee
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Youngbin Tchoe
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Nicholas Rogers
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
| | - Kivilcim Kiliç
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sang Baek Ryu
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Seung Woo Lee
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - John Hermiz
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Vikash Gilja
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - István Ulbert
- Research Centre for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, 1519 Budapest, Hungary
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, H-1444 Budapest, Hungary
| | - Daniel Fabó
- Epilepsy Centrum, National Institute of Clinical Neurosciences, 1145 Budapest, Hungary
| | - Thomas Thesen
- Department of Biomedical Sciences, University of Houston College of Medicine, Houston, TX 77204, USA
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Werner K Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Joseph R Madsen
- Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Donald L Schomer
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Albert Einstein College of Medicine, Montefiore Medical Center, Department of Neurosurgery, Bronx, NY 10467, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anna Devor
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Shelley I Fried
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Boston VA Healthcare System, 150 South Huntington Avenue, Boston, MA 02130, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Sharona Ben-Haim
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Sarah K Bick
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Ahmed M Raslan
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Dominic A Siler
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Shadi A Dayeh
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Nanoengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
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Azeem A, von Ellenrieder N, Hall J, Dubeau F, Frauscher B, Gotman J. Interictal spike networks predict surgical outcome in patients with drug-resistant focal epilepsy. Ann Clin Transl Neurol 2021; 8:1212-1223. [PMID: 33951322 PMCID: PMC8164864 DOI: 10.1002/acn3.51337] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/16/2021] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVE To determine if properties of epileptic networks could be delineated using interictal spike propagation seen on stereo-electroencephalography (SEEG) and if these properties could predict surgical outcome in patients with drug-resistant epilepsy. METHODS We studied the SEEG of 45 consecutive drug-resistant epilepsy patients who underwent subsequent epilepsy surgery: 18 patients with good post-surgical outcome (Engel I) and 27 with poor outcome (Engel II-IV). Epileptic networks were derived from interictal spike propagation; these networks described the generation and propagation of interictal epileptic activity. We compared the regions in which spikes were frequent and the regions responsible for generating spikes to the area of resection and post-surgical outcome. We developed a measure termed source spike concordance, which integrates information about both spike rate and region of spike generation. RESULTS Inclusion in the resection of regions with high spike rate is associated with good post-surgical outcome (sensitivity = 0.82, specificity = 0.73). Inclusion in the resection of the regions responsible for generating interictal epileptic activity independently of rate is also associated with good post-surgical outcome (sensitivity = 0.88, specificity = 0.82). Finally, when integrating the spike rate and the generators, we find that the source spike concordance measure has strong predictability (sensitivity = 0.91, specificity = 0.94). INTERPRETATIONS Epileptic networks derived from interictal spikes can determine the generators of epileptic activity. Inclusion of the most active generators in the resection is strongly associated with good post-surgical outcome. These epileptic networks may aid clinicians in determining the area of resection during pre-surgical evaluation.
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Affiliation(s)
- Abdullah Azeem
- Department of Neurology and NeurosurgeryMontreal Neurological InstituteMcGill UniversityMontréalQuebecCanada
| | - Nicolas von Ellenrieder
- Department of Neurology and NeurosurgeryMontreal Neurological InstituteMcGill UniversityMontréalQuebecCanada
| | - Jeffery Hall
- Department of Neurology and NeurosurgeryMontreal Neurological Institute and HospitalMcGill UniversityMontréalQuebecCanada
| | - Francois Dubeau
- Department of Neurology and NeurosurgeryMontreal Neurological Institute and HospitalMcGill UniversityMontréalQuebecCanada
| | - Birgit Frauscher
- Department of Neurology and NeurosurgeryMontreal Neurological Institute and HospitalMcGill UniversityMontréalQuebecCanada
| | - Jean Gotman
- Department of Neurology and NeurosurgeryMontreal Neurological InstituteMcGill UniversityMontréalQuebecCanada
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49
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Kragel JE, Schuele S, VanHaerents S, Rosenow JM, Voss JL. Rapid coordination of effective learning by the human hippocampus. SCIENCE ADVANCES 2021; 7:7/25/eabf7144. [PMID: 34144985 PMCID: PMC8213228 DOI: 10.1126/sciadv.abf7144] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 05/06/2021] [Indexed: 06/12/2023]
Abstract
Although the human hippocampus is necessary for long-term memory, controversial findings suggest that it may also support short-term memory in the service of guiding effective behaviors during learning. We tested the counterintuitive theory that the hippocampus contributes to long-term memory through remarkably short-term processing, as reflected in eye movements during scene encoding. While viewing scenes for the first time, short-term retrieval operative within the episode over only hundreds of milliseconds was indicated by a specific eye-movement pattern, which was effective in that it enhanced spatiotemporal memory formation. This viewing pattern was predicted by hippocampal theta oscillations recorded from depth electrodes and by shifts toward top-down influence of hippocampal theta on activity within visual perception and attention networks. The hippocampus thus supports short-term memory processing that coordinates behavior in the service of effective spatiotemporal learning.
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Affiliation(s)
- James E Kragel
- Interdepartmental Neuroscience Program, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Stephan Schuele
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Stephen VanHaerents
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Joshua M Rosenow
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Joel L Voss
- Interdepartmental Neuroscience Program, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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50
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Henin S, Shankar A, Borges H, Flinker A, Doyle W, Friedman D, Devinsky O, Buzsáki G, Liu A. Spatiotemporal dynamics between interictal epileptiform discharges and ripples during associative memory processing. Brain 2021; 144:1590-1602. [PMID: 33889945 DOI: 10.1093/brain/awab044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 11/16/2020] [Accepted: 12/06/2020] [Indexed: 12/13/2022] Open
Abstract
We describe the spatiotemporal course of cortical high-gamma activity, hippocampal ripple activity and interictal epileptiform discharges during an associative memory task in 15 epilepsy patients undergoing invasive EEG. Successful encoding trials manifested significantly greater high-gamma activity in hippocampus and frontal regions. Successful cued recall trials manifested sustained high-gamma activity in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. Interictal epileptiform discharges during encoding were associated with 15% decreased odds of remembering in hippocampus (95% confidence interval 6-23%). Hippocampal interictal epileptiform discharges during retrieval predicted 25% decreased odds of remembering (15-33%). Odds of remembering were reduced by 25-52% if interictal epileptiform discharges occurred during the 500-2000 ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal interictal epileptiform discharges were followed by a transient decrease in ripple rate. We hypothesize that interictal epileptiform discharges impair associative memory in a regionally and temporally specific manner by decreasing physiological hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological interictal epileptiform discharge events competing with physiological ripples, interictal epileptiform discharges represent a promising therapeutic target for memory remediation in patients with epilepsy.
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Affiliation(s)
- Simon Henin
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA
| | - Anita Shankar
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA
| | - Helen Borges
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA
| | - Adeen Flinker
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA
| | - Werner Doyle
- NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA.,NYU Langone Health, Department of Neurosurgery, New York, NY 10016, USA
| | - Daniel Friedman
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA
| | - Orrin Devinsky
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA
| | - György Buzsáki
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,New York University, Neuroscience Institute, New York, NY 10016, USA
| | - Anli Liu
- NYU Langone Health, Department of Neurology, New York, NY 10017, USA.,NYU Langone Health, Comprehensive Epilepsy Center, New York, NY 10016, USA.,New York University, Neuroscience Institute, New York, NY 10016, USA
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