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Cui J, Mivalt F, Sladky V, Kim J, Richner TJ, Lundstrom BN, Van Gompel JJ, Wang HL, Miller KJ, Gregg N, Wu LJ, Denison T, Winter B, Brinkmann BH, Kremen V, Worrell GA. Acute to long-term characteristics of impedance recordings during neurostimulation in humans. J Neural Eng 2024; 21:10.1088/1741-2552/ad3416. [PMID: 38484397 PMCID: PMC11044203 DOI: 10.1088/1741-2552/ad3416] [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: 04/10/2023] [Accepted: 03/14/2024] [Indexed: 03/26/2024]
Abstract
Objective.This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus, and posterior hippocampus over an extended period.Approach.Impedance was periodically sampled every 5-15 min over several months in five subjects with drug-resistant epilepsy using an investigational neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24 h impedance cycle throughout the multi-month recording.Main results.Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 d to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24 h impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures.Significance.Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.
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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
| | - Filip Mivalt
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Vladimir Sladky
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, 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
| | - Jiwon Kim
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | | | - Hai-long Wang
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Long Jun Wu
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford; MRC Brain Network Dynamics Unit, University of Oxford, OX3 7DQ UK
| | - Bailey Winter
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H. Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, Prague, Czech Republic
| | - Gregory A. Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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2
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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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3
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Marks VS, Balzekas I, Grimm JA, Richner TJ, Sladky V, Mivalt F, Gregg NM, Lundstrom BN, Miller KJ, Joseph B, Van Gompel J, Brinkmann B, Croarkin P, Alden EC, Kremen V, Kucewicz M, Worrell GA. High and low frequency anterior nucleus of thalamus deep brain stimulation: Impact on memory and mood in five patients with treatment resistant temporal lobe epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.14.24302765. [PMID: 38405801 PMCID: PMC10888989 DOI: 10.1101/2024.02.14.24302765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
High frequency anterior nucleus of the thalamus deep brain stimulation (ANT DBS) is an established therapy for treatment resistant focal epilepsies. Although high frequency-ANT DBS is well tolerated, patients are rarely seizure free and the efficacy of other DBS parameters and their impact on comorbidities of epilepsy such as depression and memory dysfunction remain unclear. The purpose of this study was to assess the impact of low vs high frequency ANT DBS on verbal memory and self-reported anxiety and depression symptoms. Five patients with treatment resistant temporal lobe epilepsy were implanted with an investigational brain stimulation and sensing device capable of ANT DBS and ambulatory intracranial electroencephalographic (iEEG) monitoring, enabling long-term detection of electrographic seizures. While patients received therapeutic high frequency (100 and 145 Hz continuous and cycling) and low frequency (2 and 7 Hz continuous) stimulation, they completed weekly free recall verbal memory tasks and thrice weekly self-reports of anxiety and depression symptom severity. Mixed effects models were then used to evaluate associations between memory scores, anxiety and depression self-reports, seizure counts, and stimulation frequency. Memory score was significantly associated with stimulation frequency, with higher free recall verbal memory scores during low frequency ANT DBS. Self-reported anxiety and depression symptom severity was not significantly associated with stimulation frequency. These findings suggest the choice of ANT DBS stimulation parameter may impact patients' cognitive function, independently of its impact on seizure rates.
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Affiliation(s)
- Victoria S Marks
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
| | - Irena Balzekas
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States
| | - Jessica A Grimm
- Department of Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Thomas J Richner
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Vladimir Sladky
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czechia
- International Clinic Research Center, St. Anne's University Research Hospital, Brno, Czechia
| | - Filip Mivalt
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Nicholas M Gregg
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Brian N Lundstrom
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czechia
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- International Clinic Research Center, St. Anne's University Research Hospital, Brno, Czechia
- Department of Biostatistics, Mayo Clinic, Rochester, MN, United States
- BioTechMed Center, Brain & Mind Electrophysiology Lab, Multimedia Systems Department, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Boney Joseph
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Jamie Van Gompel
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Benjamin Brinkmann
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
| | - Paul Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Eva C Alden
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Vaclav Kremen
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
| | - Michal Kucewicz
- BioTechMed Center, Brain & Mind Electrophysiology Lab, Multimedia Systems Department, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Gregory A Worrell
- Department of Neurology, Bioelectronics, Neurophysiology, and Engineering Laboratory, Mayo Clinic, Rochester, MN, United States
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Anderson DN, Charlebois CM, Smith EH, Davis TS, Peters AY, Newman BJ, Arain AM, Wilcox KS, Butson CR, Rolston JD. Closed-loop stimulation in periods with less epileptiform activity drives improved epilepsy outcomes. Brain 2024; 147:521-531. [PMID: 37796038 PMCID: PMC10834245 DOI: 10.1093/brain/awad343] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 07/17/2023] [Accepted: 08/28/2023] [Indexed: 10/06/2023] Open
Abstract
In patients with drug-resistant epilepsy, electrical stimulation of the brain in response to epileptiform activity can make seizures less frequent and debilitating. This therapy, known as closed-loop responsive neurostimulation (RNS), aims to directly halt seizure activity via targeted stimulation of a burgeoning seizure. Rather than immediately stopping seizures as they start, many RNS implants produce slower, long-lasting changes in brain dynamics that better predict clinical outcomes. Here we hypothesize that stimulation during brain states with less epileptiform activity drives long-term changes that restore healthy brain networks. To test this, we quantified stimulation episodes during low- and high-risk brain states-that is, stimulation during periods with a lower or higher risk of generating epileptiform activity-in a cohort of 40 patients treated with RNS. More frequent stimulation in tonic low-risk states and out of rhythmic high-risk states predicted seizure reduction. Additionally, stimulation events were more likely to be phase-locked to prolonged episodes of abnormal activity for intermediate and poor responders when compared to super-responders, consistent with the hypothesis that improved outcomes are driven by stimulation during low-risk states. These results support the hypothesis that stimulation during low-risk periods might underlie the mechanisms of RNS, suggesting a relationship between temporal patterns of neuromodulation and plasticity that facilitates long-term seizure reduction.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA
| | - Angela Y Peters
- Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA
| | - Blake J Newman
- Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA
| | - Karen S Wilcox
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher R Butson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL 32608, USA
- Department of Neurology, University of Florida, Gainesville, FL 32611, USA
- Department of Neurosurgery, University of Florida, Gainesville, FL 32608, USA
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - John D Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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5
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Cui J, Mivalt F, Sladky V, Kim J, Richner TJ, Lundstrom BN, Van Gompel JJ, Wang HL, Miller KJ, Gregg N, Wu LJ, Denison T, Winter B, Brinkmann BH, Kremen V, Worrell GA. Acute to long-term characteristics of impedance recordings during neurostimulation in humans. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.23.24301672. [PMID: 38343858 PMCID: PMC10854350 DOI: 10.1101/2024.01.23.24301672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Objective This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus (AMG-HPC), and posterior hippocampus (post-HPC) over an extended period. Approach Impedance was periodically sampled every 5-15 minutes over several months in five subjects with drug-resistant epilepsy using an experimental neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24-hour impedance cycle throughout the multi-month recording. Main results Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 days to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24-hour impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures. Significance Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.
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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
| | - Filip Mivalt
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Vladimir Sladky
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, 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
| | - Jiwon Kim
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | | | - Hai-long Wang
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Long Jun Wu
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford; MRC Brain Network Dynamics Unit, University of Oxford, OX3 7DQ UK
| | - Bailey Winter
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H. Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, Prague, Czech Republic
| | - Gregory A. Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Khambhati AN. Utility of Chronic Intracranial Electroencephalography in Responsive Neurostimulation Therapy. Neurosurg Clin N Am 2024; 35:125-133. [PMID: 38000836 DOI: 10.1016/j.nec.2023.09.004] [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] [Indexed: 11/26/2023]
Abstract
Responsive neurostimulation (RNS) therapy is an effective treatment for reducing seizures in some patients with focal epilepsy. Utilizing a chronically implanted device, RNS involves monitoring brain activity signals for user-defined patterns of seizure activity and delivering electrical stimulation in response. Devices store chronic data including counts of detected activity patterns and brief recordings of intracranial electroencephalography signals. Data platforms for reviewing stored chronic data retrospectively may be used to evaluate therapy performance and to fine-tune detection and stimulation settings. New frontiers in RNS research can leverage raw chronic data to reverse engineer neurostimulation mechanisms and improve therapy effectiveness.
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Affiliation(s)
- Ankit N Khambhati
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California, San Francisco, Joan and Sanford I. Weill Neurosciences Building, 1651 4th Street, 671C, San Francisco, CA 94158, USA.
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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8
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Silva AB, Leonard MK, Oganian Y, D’Esopo E, Krish D, Kopald B, Tran EB, Chang EF, Kleen JK. Interictal epileptiform discharges contribute to word-finding difficulty in epilepsy through multiple cognitive mechanisms. Epilepsia 2023; 64:3266-3278. [PMID: 37753856 PMCID: PMC10841419 DOI: 10.1111/epi.17781] [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: 04/25/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 09/28/2023]
Abstract
OBJECTIVE Cognitive impairment often impacts quality of life in epilepsy even if seizures are controlled. Word-finding difficulty is particularly prevalent and often attributed to etiological (static, baseline) circuit alterations. We sought to determine whether interictal discharges convey significant superimposed contributions to word-finding difficulty in patients, and if so, through which cognitive mechanism(s). METHODS Twenty-three patients undergoing intracranial monitoring for drug-resistant epilepsy participated in multiple tasks involving word production (auditory naming, short-term verbal free recall, repetition) to probe word-finding difficulty across different cognitive domains. We compared behavioral performance between trials with versus without interictal discharges across six major brain areas and adjusted for intersubject differences using mixed-effects models. We also evaluated for subjective word-finding difficulties through retrospective chart review. RESULTS Subjective word-finding difficulty was reported by the majority (79%) of studied patients preoperatively. During intracranial recordings, interictal epileptiform discharges (IEDs) in the medial temporal lobe were associated with long-term lexicosemantic memory impairments as indexed by auditory naming (p = .009), in addition to their established impact on short-term verbal memory as indexed by free recall (p = .004). Interictal discharges involving the lateral temporal cortex and lateral frontal cortex were associated with delayed reaction time in the auditory naming task (p = .016 and p = .018), as well as phonological working memory impairments as indexed by repetition reaction time (p = .002). Effects of IEDs across anatomical regions were strongly dependent on their precise timing within the task. SIGNIFICANCE IEDs appear to act through multiple cognitive mechanisms to form a convergent basis for the debilitating clinical word-finding difficulty reported by patients with epilepsy. This was particularly notable for medial temporal spikes, which are quite common in adult focal epilepsy. In parallel with the treatment of seizures, the modulation of interictal discharges through emerging pharmacological means and neurostimulation approaches may be an opportunity to help address devastating memory and language impairments in epilepsy.
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Affiliation(s)
- Alexander B. Silva
- Department of Neurosurgery, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program, University of California, San Francisco, CA, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Matthew K. Leonard
- Department of Neurosurgery, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | | | - Emma D’Esopo
- Department of Neurology, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Devon Krish
- Department of Neurology, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Brandon Kopald
- Department of Neurology, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Edwina B. Tran
- Department of Neurology, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Edward F. Chang
- Department of Neurosurgery, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Jonathan K. Kleen
- Department of Neurology, Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, USA
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9
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Folkard E, Niel L, Gaitero L, James FMK. Tools and techniques for classifying behaviours in canine epilepsy. Front Vet Sci 2023; 10:1211515. [PMID: 38026681 PMCID: PMC10646580 DOI: 10.3389/fvets.2023.1211515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Idiopathic epilepsy is the most common neurological disease in dogs. Similar to humans, dogs with epilepsy often experience behavioural comorbidities such as increased fear, anxiety, and aggression, as reported by their caregivers. Investigations of behaviour in canine epilepsy have yet to untangle interictal and pre and postictal behaviours, prodromal changes, and seizure-precipitating factors. Under-recognition of absence and focal seizures further complicates these assessments. These complex behavioural presentations in combination with caring for an epileptic animal have a significant negative impact on the dog's and caregiver's quality of life. Despite the growing recognition of behavioural comorbidities and their impact on quality of life in dogs with epilepsy, few objective research methods for classifying and quantifying canine behaviour exist. This narrative review examines the strengths, limitations, and granularity of three tools used in the investigation of canine behaviour and epilepsy; questionnaires, electroencephalography, and actigraphy. It suggests that a prospective combination of these three tools has the potential to offer improvements to the objective classification and quantification of canine behaviour in epilepsy.
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Affiliation(s)
- Emily Folkard
- Department of Clinical Studies, University of Guelph, Guelph, ON, Canada
| | - Lee Niel
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada
| | - Luis Gaitero
- Department of Clinical Studies, University of Guelph, Guelph, ON, Canada
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10
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Ojemann WKS, Scheid BH, Mouchtaris S, Lucas A, LaRocque JJ, Aguila C, Ashourvan A, Caciagli L, Davis KA, Conrad EC, Litt B. Resting-state background features demonstrate multidien cycles in long-term EEG device recordings. Brain Stimul 2023; 16:1709-1718. [PMID: 37979654 DOI: 10.1016/j.brs.2023.11.005] [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: 06/19/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. OBJECTIVE/HYPOTHESIS We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. METHODS We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. RESULTS Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56-0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). CONCLUSIONS Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may temporarily interrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.
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Affiliation(s)
- William K S Ojemann
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA.
| | - Brittany H Scheid
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Sofia Mouchtaris
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Alfredo Lucas
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Joshua J LaRocque
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Carlos Aguila
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Arian Ashourvan
- The University of Kansas, Department of Psychology, 1415 Jayhawk Blvd, Lawrence, KS, 66045, USA
| | - Lorenzo Caciagli
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Kathryn A Davis
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Erin C Conrad
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Brian Litt
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
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11
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Schulze‐Bonhage A, Richardson MP, Brandt A, Zabler N, Dümpelmann M, San Antonio‐Arce V. Cyclical underreporting of seizures in patient-based seizure documentation. Ann Clin Transl Neurol 2023; 10:1863-1872. [PMID: 37608738 PMCID: PMC10578895 DOI: 10.1002/acn3.51880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 07/18/2023] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVE Circadian and multidien cycles of seizure occurrence are increasingly discussed as to their biological underpinnings and in the context of seizure forecasting. This study analyzes if patient reported seizures provide valid data on such cyclical occurrence. METHODS We retrospectively studied if circadian cycles derived from patient-based reporting reflect the objective seizure documentation in 2003 patients undergoing in-patient video-EEG monitoring. RESULTS Only 24.1% of more than 29000 seizures documented were accompanied by patient notifications. There was cyclical underreporting of seizures with a maximum during nighttime, leading to significant deviations in the circadian distribution of seizures. Significant cyclical deviations were found for focal epilepsies originating from both, frontal and temporal lobes, and for different seizure types (in particular, focal unaware and focal to bilateral tonic-clonic seizures). INTERPRETATION Patient seizure diaries may reflect a cyclical reporting bias rather than the true circadian seizure distributions. Cyclical underreporting of seizures derived from patient-based reports alone may lead to suboptimal treatment schemes, to an underestimation of seizure-associated risks, and may pose problems for valid seizure forecasting. This finding strongly supports the use of objective measures to monitor cyclical distributions of seizures and for studies and treatment decisions based thereon.
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Affiliation(s)
- Andreas Schulze‐Bonhage
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
- European Reference Network EpiCARE
| | - Mark P. Richardson
- Division of NeuroscienceInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Armin Brandt
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Nicolas Zabler
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Matthias Dümpelmann
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Victoria San Antonio‐Arce
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
- European Reference Network EpiCARE
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12
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Ojeda Valencia G, Gregg NM, Huang H, Lundstrom BN, Brinkmann BH, Pal Attia T, Van Gompel JJ, Bernstein MA, In MH, Huston J, Worrell GA, Miller KJ, Hermes D. Signatures of Electrical Stimulation Driven Network Interactions in the Human Limbic System. J Neurosci 2023; 43:6697-6711. [PMID: 37620159 PMCID: PMC10538586 DOI: 10.1523/jneurosci.2201-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Stimulation-evoked signals are starting to be used as biomarkers to indicate the state and health of brain networks. The human limbic network, often targeted for brain stimulation therapy, is involved in emotion and memory processing. Previous anatomic, neurophysiological, and functional studies suggest distinct subsystems within the limbic network (Rolls, 2015). Studies using intracranial electrical stimulation, however, have emphasized the similarities of the evoked waveforms across the limbic network. We test whether these subsystems have distinct stimulation-driven signatures. In eight patients (four male, four female) with drug-resistant epilepsy, we stimulated the limbic system with single-pulse electrical stimulation. Reliable corticocortical evoked potentials (CCEPs) were measured between hippocampus and the posterior cingulate cortex (PCC) and between the amygdala and the anterior cingulate cortex (ACC). However, the CCEP waveform in the PCC after hippocampal stimulation showed a unique and reliable morphology, which we term the "limbic Hippocampus-Anterior nucleus of the thalamus-Posterior cingulate, HAP-wave." This limbic HAP-wave was visually distinct and separately decoded from the CCEP waveform in ACC after amygdala stimulation. Diffusion MRI data show that the measured end points in the PCC overlap with the end points of the parolfactory cingulum bundle rather than the parahippocampal cingulum, suggesting that the limbic HAP-wave may travel through fornix, mammillary bodies, and the anterior nucleus of the thalamus (ANT). This was further confirmed by stimulating the ANT, which evoked the same limbic HAP-wave but with an earlier latency. Limbic subsystems have unique stimulation-evoked signatures that may be used in the future to help network pathology diagnosis.SIGNIFICANCE STATEMENT The limbic system is often compromised in diverse clinical conditions, such as epilepsy or Alzheimer's disease, and characterizing its typical circuit responses may provide diagnostic insight. Stimulation-evoked waveforms have been used in the motor system to diagnose circuit pathology. We translate this framework to limbic subsystems using human intracranial stereo EEG (sEEG) recordings that measure deeper brain areas. Our sEEG recordings describe a stimulation-evoked waveform characteristic to the memory and spatial subsystem of the limbic network that we term the "limbic HAP-wave." The limbic HAP-wave follows anatomic white matter pathways from hippocampus to thalamus to the posterior cingulum and shows promise as a distinct biomarker of signaling in the human brain memory and spatial limbic network.
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Affiliation(s)
- Gabriela Ojeda Valencia
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Nicholas M Gregg
- Department of Neurology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Harvey Huang
- Mayo Clinic Medical Scientist Training Program, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Brian N Lundstrom
- Department of Neurology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | | | - Tal Pal Attia
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Jamie J Van Gompel
- Department of Neurologic Surgery, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - John Huston
- Department of Radiology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Gregory A Worrell
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
- Department of Neurology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Kai J Miller
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
- Department of Neurologic Surgery, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
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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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Mivalt F, Sladky V, Worrell S, Gregg NM, Balzekas I, Kim I, Chang SY, Montonye DR, Duque-Lopez A, Krakorova M, Pridalova T, Lepkova K, Brinkmann BH, Miller KJ, Van Gompel JJ, Denison T, Kaufmann TJ, Messina SA, St. Louis EK, Kremen V, Worrell GA. Automated sleep classification with chronic neural implants in freely behaving canines. J Neural Eng 2023; 20:10.1088/1741-2552/aced21. [PMID: 37536320 PMCID: PMC10480092 DOI: 10.1088/1741-2552/aced21] [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: 02/03/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023]
Abstract
Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Samuel Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Nicholas M. Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States of America
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States of America
| | - Inyong Kim
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Su-youne Chang
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Daniel R. Montonye
- Department of Comparative Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Andrea Duque-Lopez
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Martina Krakorova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Tereza Pridalova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Kamila Lepkova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States of America
| | - Jamie J. Van Gompel
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States of America
| | - Timothy Denison
- Department of Engineering Science, Oxford University, Oxford, United Kingdom
| | - Timothy J. Kaufmann
- Department of Neuroradiology, Mayo Clinic, Rochester, MN, United States of America
| | - Steven A. Messina
- Department of Neuroradiology, Mayo Clinic, Rochester, MN, United States of America
| | - Erik K St. Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
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15
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Ojemann WK, Scheid BH, Mouchtaris S, Lucas A, LaRocque JJ, Aguila C, Ashourvan A, Caciagli L, Davis KA, Conrad EC, Litt B. Resting-state background features demonstrate multidien cycles in long-term EEG device recordings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.05.23291521. [PMID: 37461688 PMCID: PMC10350154 DOI: 10.1101/2023.07.05.23291521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Background Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. Objective/Hypothesis We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may disrupt these cycles. Methods We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Results Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). Conclusions Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may disrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.
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Affiliation(s)
- William K.S. Ojemann
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Brittany H. Scheid
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Sofia Mouchtaris
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Alfredo Lucas
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104
| | - Joshua J. LaRocque
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Carlos Aguila
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Arian Ashourvan
- The University of Kansas, Department of Psychology, 1415 Jayhawk Blvd. Lawrence, KS 66045
| | - Lorenzo Caciagli
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Kathryn A. Davis
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Erin C. Conrad
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Brian Litt
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
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16
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Fleming JE, Benjaber M, Toth R, Zamora M, Landin K, Kavoosi A, Ottoway J, Gillbe T, Piper RJ, Noone T, Campbell H, Gillbe I, Kaliakatsos M, Tisdall M, Valentín A, Denison T. An embedded intracranial seizure monitor for objective outcome measurements and rhythm identification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083730 PMCID: PMC7615373 DOI: 10.1109/embc40787.2023.10340850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Providing clinicians with objective outcomes of neuromodulation therapy is a key unmet need, especially in emerging areas such as epilepsy and mood disorders. These diseases have episodic behavior and circadian/multidien rhythm characteristics that are difficult to capture in short clinical follow-ups. This work presents preliminary validation evidence for an implantable neuromodulation system with integrated physiological event monitoring, with an initial focus on seizure tracking for epilepsy. The system was developed to address currently unmet requirements for patients undergoing neuromodulation therapy for neurological disorders, specifically the ability to sense physiological data during stimulation and track events with seconds-level granularity. The system incorporates an interactive software tool to enable optimal configuration of the signal processing chain on an embedded implantable device (the Picostim-DyNeuMo Mk-2) including data ingestion from the device loop recorder, event labeling, generation of filter and classification parameters, as well as summary statistics. When the monitor parameters are optimized, the user can wirelessly update the system for chronic event tracking. The simulated performance of the device was assessed using an in silico model with human data to predict the real-time device performance at tracking recorded seizure activity. The in silico performance was then compared against its performance in an in vitro model to capture the full environmental constraints such as sensing during stimulation at the tissue electrode interface. In vitro modeling demonstrated comparable results to the in silico model, providing verification of the software tool and model. This study provides validation evidence of the suitability of the proposed system for tracking longitudinal seizure activity. Given its flexibility, the event monitor can be adapted to track other disorders with episodic and rhythmic symptoms represented by bioelectrical behavior.Clinical relevance-An implantable neuromodulation system is presented that enables chronic tracking of physiological events in disease. This physiological monitor provides the basis for longitudinal assessments of therapy outcomes for patients, such as those with epilepsy where objective identification of patient seizure activity and rhythms might help guide therapy optimization. The system is configurable for other disease states such as Parkinson's disease and mood disorders.
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Affiliation(s)
- John E. Fleming
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Mayela Zamora
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Kei Landin
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Ali Kavoosi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | | | | | - Rory J. Piper
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | | | | | | | - Marios Kaliakatsos
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Martin Tisdall
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Antonio Valentín
- Department of Basic and Clinical Neuroscience, King’s College London, London SE5 9RT, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
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17
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Zhang S, Qin Y, Wang J, Yu Y, Wu L, Zhang T. Noninvasive Electrical Stimulation Neuromodulation and Digital Brain Technology: A Review. Biomedicines 2023; 11:1513. [PMID: 37371609 DOI: 10.3390/biomedicines11061513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
We review the research progress on noninvasive neural regulatory systems through system design and theoretical guidance. We provide an overview of the development history of noninvasive neuromodulation technology, focusing on system design. We also discuss typical cases of neuromodulation that use modern noninvasive electrical stimulation and the main limitations associated with this technology. In addition, we propose a closed-loop system design solution of the "time domain", "space domain", and "multi-electrode combination". For theoretical guidance, this paper provides an overview of the "digital brain" development process used for noninvasive electrical-stimulation-targeted modeling and the development of "digital human" programs in various countries. We also summarize the core problems of the existing "digital brain" used for noninvasive electrical-stimulation-targeted modeling according to the existing achievements and propose segmenting the tissue. For this, the tissue parameters of a multimodal image obtained from a fresh cadaver were considered as an index. The digital projection of the multimodal image of the brain of a living individual was implemented, following which the segmented tissues could be reconstructed to obtain a "digital twin brain" model with personalized tissue structure differences. The "closed-loop system" and "personalized digital twin brain" not only enable the noninvasive electrical stimulation of neuromodulation to achieve the visualization of the results and adaptive regulation of the stimulation parameters but also enable the system to have individual differences and more accurate stimulation.
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Affiliation(s)
- Shuang Zhang
- The School of Artificial Intelligence, Neijiang Normal University, Neijiang 641000, China
- The School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- The NJNU-OMNISKY Smart Medical Engineering Applications Joint Laboratory, Neijiang Normal University, Neijiang 641004, China
- The High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, Chengdu 610056, China
| | - Yuping Qin
- The School of Artificial Intelligence, Neijiang Normal University, Neijiang 641000, China
- The NJNU-OMNISKY Smart Medical Engineering Applications Joint Laboratory, Neijiang Normal University, Neijiang 641004, China
| | - Jiujiang Wang
- The School of Artificial Intelligence, Neijiang Normal University, Neijiang 641000, China
- The NJNU-OMNISKY Smart Medical Engineering Applications Joint Laboratory, Neijiang Normal University, Neijiang 641004, China
| | - Yuanyu Yu
- The School of Artificial Intelligence, Neijiang Normal University, Neijiang 641000, China
- The NJNU-OMNISKY Smart Medical Engineering Applications Joint Laboratory, Neijiang Normal University, Neijiang 641004, China
| | - Lin Wu
- The School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- The High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, Chengdu 610056, China
| | - Tao Zhang
- The School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
- The High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, Chengdu 610056, China
- The Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 610056, China
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18
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Satzer D, Wu S, Henry J, Doll E, Issa NP, Warnke PC. Ambulatory Local Field Potential Recordings from the Thalamus in Epilepsy: A Feasibility Study. Stereotact Funct Neurosurg 2023; 101:195-206. [PMID: 37232010 PMCID: PMC11227660 DOI: 10.1159/000529961] [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: 11/25/2022] [Accepted: 02/24/2023] [Indexed: 05/27/2023]
Abstract
INTRODUCTION Stimulation of the thalamus is gaining favor in the treatment of medically refractory multifocal and generalized epilepsy. Implanted brain stimulators capable of recording ambulatory local field potentials (LFPs) have recently been introduced, but there is little information to guide their use in thalamic stimulation for epilepsy. This study sought to assess the feasibility of chronically recording ambulatory interictal LFP from the thalamus in patients with epilepsy. METHODS In this pilot study, ambulatory LFP was recorded from patients who underwent sensing-enabled deep brain stimulation (DBS, 2 participants) or responsive neurostimulation (RNS, 3 participants) targeting the anterior nucleus of the thalamus (ANT, 2 electrodes), centromedian nucleus (CM, 7 electrodes), or medial pulvinar (PuM, 1 electrode) for multifocal or generalized epilepsy. Time-domain and frequency-domain LFP was investigated for epileptiform discharges, spectral peaks, circadian variation, and peri-ictal patterns. RESULTS Thalamic interictal discharges were visible on ambulatory recordings from both DBS and RNS. At-home interictal frequency-domain data could be extracted from both devices. Spectral peaks were noted at 10-15 Hz in CM, 6-11 Hz in ANT, and 19-24 Hz in PuM but varied in prominence and were not visible in all electrodes. In CM, 10-15 Hz power exhibited circadian variation and was attenuated by eye opening. CONCLUSION Chronic ambulatory recording of thalamic LFP is feasible. Common spectral peaks can be observed but vary between electrodes and across neural states. DBS and RNS devices provide a wealth of complementary data that have the potential to better inform thalamic stimulation for epilepsy.
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Affiliation(s)
- David Satzer
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA
| | - Shasha Wu
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Julia Henry
- Department of Pediatrics, Child Neurology Section, University of Chicago, Chicago, IL, USA
| | - Emily Doll
- Department of Pediatrics, Child Neurology Section, University of Chicago, Chicago, IL, USA
| | - Naoum P. Issa
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Peter C. Warnke
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA
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19
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Lundstrom BN, Osman GM, Starnes K, Gregg NM, Simpson HD. Emerging approaches in neurostimulation for epilepsy. Curr Opin Neurol 2023; 36:69-76. [PMID: 36762660 PMCID: PMC9992108 DOI: 10.1097/wco.0000000000001138] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
PURPOSE OF REVIEW Neurostimulation is a quickly growing treatment approach for epilepsy patients. We summarize recent approaches to provide a perspective on the future of neurostimulation. RECENT FINDINGS Invasive stimulation for treatment of focal epilepsy includes vagus nerve stimulation, responsive neurostimulation of the cortex and deep brain stimulation of the anterior nucleus of the thalamus. A wide range of other targets have been considered, including centromedian, central lateral and pulvinar thalamic nuclei; medial septum, nucleus accumbens, subthalamic nucleus, cerebellum, fornicodorsocommissure and piriform cortex. Stimulation for generalized onset seizures and mixed epilepsies as well as increased efforts focusing on paediatric populations have emerged. Hardware with more permanently implanted lead options and sensing capabilities is emerging. A wider variety of programming approaches than typically used may improve patient outcomes. Finally, noninvasive brain stimulation with its favourable risk profile offers the potential to treat increasingly diverse epilepsy patients. SUMMARY Neurostimulation for the treatment of epilepsy is surprisingly varied. Flexibility and reversibility of neurostimulation allows for rapid innovation. There remains a continued need for excitability biomarkers to guide treatment and innovation. Neurostimulation, a part of bioelectronic medicine, offers distinctive benefits as well as unique challenges.
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Affiliation(s)
| | | | - Keith Starnes
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Hugh D Simpson
- Department of Neurology, Alfred Health
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
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20
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Sun S, Wang H. Clocking Epilepsies: A Chronomodulated Strategy-Based Therapy for Rhythmic Seizures. Int J Mol Sci 2023; 24:ijms24044223. [PMID: 36835631 PMCID: PMC9962262 DOI: 10.3390/ijms24044223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/08/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Epilepsy is a neurological disorder characterized by hypersynchronous recurrent neuronal activities and seizures, as well as loss of muscular control and sometimes awareness. Clinically, seizures have been reported to display daily variations. Conversely, circadian misalignment and circadian clock gene variants contribute to epileptic pathogenesis. Elucidation of the genetic bases of epilepsy is of great importance because the genetic variability of the patients affects the efficacies of antiepileptic drugs (AEDs). For this narrative review, we compiled 661 epilepsy-related genes from the PHGKB and OMIM databases and classified them into 3 groups: driver genes, passenger genes, and undetermined genes. We discuss the potential roles of some epilepsy driver genes based on GO and KEGG analyses, the circadian rhythmicity of human and animal epilepsies, and the mutual effects between epilepsy and sleep. We review the advantages and challenges of rodents and zebrafish as animal models for epileptic studies. Finally, we posit chronomodulated strategy-based chronotherapy for rhythmic epilepsies, integrating several lines of investigation for unraveling circadian mechanisms underpinning epileptogenesis, chronopharmacokinetic and chronopharmacodynamic examinations of AEDs, as well as mathematical/computational modeling to help develop time-of-day-specific AED dosing schedules for rhythmic epilepsy patients.
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Affiliation(s)
- Sha Sun
- Center for Circadian Clocks, Soochow University, Suzhou 215123, China
- School of Biology and Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou 215123, China
| | - Han Wang
- Center for Circadian Clocks, Soochow University, Suzhou 215123, China
- School of Biology and Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou 215123, China
- Correspondence: or ; Tel.: +86-186-0512-8971
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21
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Reynolds A, Vranic-Peters M, Lai A, Grayden DB, Cook MJ, Peterson A. Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review. Epilepsia 2023; 64:1125-1174. [PMID: 36790369 DOI: 10.1111/epi.17548] [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: 05/30/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.
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Affiliation(s)
- Ashley Reynolds
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Michaela Vranic-Peters
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Alan Lai
- Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Andre Peterson
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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22
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Alcala-Zermeno JL, Starnes K, Gregg NM, Worrell G, Lundstrom BN. Responsive neurostimulation with low-frequency stimulation. Epilepsia 2023; 64:e16-e22. [PMID: 36385467 PMCID: PMC9970035 DOI: 10.1111/epi.17467] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 11/18/2022]
Abstract
Deep brain stimulation and responsive neurostimulation (RNS) use high-frequency stimulation (HFS) per the pivotal trials and manufacturer-recommended therapy protocols. However, not all patients respond to HFS. In this retrospective case series, 10 patients implanted with the RNS System were programmed with low-frequency stimulation (LFS) to treat their seizures; nine of these patients were previously treated with HFS (100 Hz or greater). LFS was defined as frequency < 10 Hz. Burst duration was increased to at least 1000 ms. With HFS, patients had a median seizure reduction (MSR) of 13% (interquartile range [IQR] = -67 to 54) after a median of 19 months (IQR = 8-49). In contrast, LFS was associated with a 67% MSR (IQR = 13-95) when compared to HFS and 76% MSR (IQR = 43-91) when compared to baseline prior to implantation. Charge delivered per hour and pulses per day were not significantly different between HFS and LFS, although time stimulated per day was longer for LFS (228 min) than for HFS (7 min). There were no LFS-specific adverse effects reported by any of the patients. LFS could represent an alternative, effective method for delivering stimulation in focal drug-resistant epilepsy patients treated with the RNS System.
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Affiliation(s)
| | - Keith Starnes
- Department of Child and Adolescent Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
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23
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Löscher W, Worrell GA. Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research. Front Vet Sci 2022; 9:1014269. [PMID: 36337210 PMCID: PMC9631025 DOI: 10.3389/fvets.2022.1014269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/26/2022] [Indexed: 11/25/2022] Open
Abstract
Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.
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Affiliation(s)
- Wolfgang Löscher
- Department of Pharmacology, Toxicology, and Pharmacy, University of Veterinary Medicine, Hanover, Germany
- Center for Systems Neuroscience, Hanover, Germany
- *Correspondence: Wolfgang Löscher
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
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24
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Kreitlow BL, Li W, Buchanan GF. Chronobiology of epilepsy and sudden unexpected death in epilepsy. Front Neurosci 2022; 16:936104. [PMID: 36161152 PMCID: PMC9490261 DOI: 10.3389/fnins.2022.936104] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/27/2022] [Indexed: 11/24/2022] Open
Abstract
Epilepsy is a neurological disease characterized by spontaneous, unprovoked seizures. Various insults render the brain hyperexcitable and susceptible to seizure. Despite there being dozens of preventative anti-seizure medications available, these drugs fail to control seizures in nearly 1 in 3 patients with epilepsy. Over the last century, a large body of evidence has demonstrated that internal and external rhythms can modify seizure phenotypes. Physiologically relevant rhythms with shorter periodic rhythms, such as endogenous circadian rhythms and sleep-state, as well as rhythms with longer periodicity, including multidien rhythms and menses, influence the timing of seizures through poorly understood mechanisms. The purpose of this review is to discuss the findings from both human and animal studies that consider the effect of such biologically relevant rhythms on epilepsy and seizure-associated death. Patients with medically refractory epilepsy are at increased risk of sudden unexpected death in epilepsy (SUDEP). The role that some of these rhythms play in the nocturnal susceptibility to SUDEP will also be discussed. While the involvement of some of these rhythms in epilepsy has been known for over a century, applying the rhythmic nature of such phenomenon to epilepsy management, particularly in mitigating the risk of SUDEP, has been underutilized. As our understanding of the physiological influence on such rhythmic phenomenon improves, and as technology for chronic intracranial epileptiform monitoring becomes more widespread, smaller and less invasive, novel seizure-prediction technologies and time-dependent chronotherapeutic seizure management strategies can be realized.
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Affiliation(s)
- Benjamin L. Kreitlow
- Medical Scientist Training Program, University of Iowa, Iowa City, IA, United States
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, United States
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, United States
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - William Li
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Gordon F. Buchanan
- Medical Scientist Training Program, University of Iowa, Iowa City, IA, United States
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, United States
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, United States
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
- *Correspondence: Gordon F. Buchanan, ; orcid.org/0000-0003-2371-4455
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25
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Nowakowska M, Üçal M, Charalambous M, Bhatti SFM, Denison T, Meller S, Worrell GA, Potschka H, Volk HA. Neurostimulation as a Method of Treatment and a Preventive Measure in Canine Drug-Resistant Epilepsy: Current State and Future Prospects. Front Vet Sci 2022; 9:889561. [PMID: 35782557 PMCID: PMC9244381 DOI: 10.3389/fvets.2022.889561] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/23/2022] [Indexed: 11/28/2022] Open
Abstract
Modulation of neuronal activity for seizure control using various methods of neurostimulation is a rapidly developing field in epileptology, especially in treatment of refractory epilepsy. Promising results in human clinical practice, such as diminished seizure burden, reduced incidence of sudden unexplained death in epilepsy, and improved quality of life has brought neurostimulation into the focus of veterinary medicine as a therapeutic option. This article provides a comprehensive review of available neurostimulation methods for seizure management in drug-resistant epilepsy in canine patients. Recent progress in non-invasive modalities, such as repetitive transcranial magnetic stimulation and transcutaneous vagus nerve stimulation is highlighted. We further discuss potential future advances and their plausible application as means for preventing epileptogenesis in dogs.
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Affiliation(s)
- Marta Nowakowska
- Research Unit of Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
| | - Muammer Üçal
- Research Unit of Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
| | - Marios Charalambous
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | - Sofie F. M. Bhatti
- Small Animal Department, Faculty of Veterinary Medicine, Small Animal Teaching Hospital, Ghent University, Merelbeke, Belgium
| | - Timothy Denison
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Sebastian Meller
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | | | - Heidrun Potschka
- Faculty of Veterinary Medicine, Institute of Pharmacology, Toxicology and Pharmacy, Ludwig-Maximilians-University, Munich, Germany
| | - Holger A. Volk
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
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26
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Fleming JE, Kremen V, Gilron R, Gregg NM, Zamora M, Dijk DJ, Starr PA, Worrell GA, Little S, Denison TJ. Embedding Digital Chronotherapy into Bioelectronic Medicines. iScience 2022; 25:104028. [PMID: 35313697 PMCID: PMC8933700 DOI: 10.1016/j.isci.2022.104028] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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