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Schroeder GM, Karoly PJ, Maturana M, Panagiotopoulou M, Taylor PN, Cook MJ, Wang Y. Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states. Brain Commun 2023; 5:fcad205. [PMID: 37693811 PMCID: PMC10484289 DOI: 10.1093/braincomms/fcad205] [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: 01/24/2023] [Revised: 06/07/2023] [Accepted: 07/18/2023] [Indexed: 09/12/2023] Open
Abstract
Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterized the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states. We then compared seizure network state occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. In most patients, the occurrence or duration of at least one seizure network state was associated with the time since implantation. Some patients had one or more seizure network states that were associated with phases of circadian and/or multidien spike rate cycles. A given seizure network state's occurrence and duration were usually not associated with the same timescale. Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a seizure network state's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures and similar principles likely apply to other neurological conditions.
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Affiliation(s)
- Gabrielle M Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Philippa J Karoly
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Matias Maturana
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
- Research Department, Seer Medical Pty Ltd., Melbourne, Victoria 3000, Australia
| | - Mariella Panagiotopoulou
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Mark J Cook
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
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2
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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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3
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Löscher W. Dogs as a Natural Animal Model of Epilepsy. Front Vet Sci 2022; 9:928009. [PMID: 35812852 PMCID: PMC9257283 DOI: 10.3389/fvets.2022.928009] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 05/18/2022] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disease in both humans and domestic dogs, making dogs an ideal translational model of epilepsy. In both species, epilepsy is a complex brain disease characterized by an enduring predisposition to generate spontaneous recurrent epileptic seizures. Furthermore, as in humans, status epilepticus is one of the more common neurological emergencies in dogs with epilepsy. In both species, epilepsy is not a single disease but a group of disorders characterized by a broad array of clinical signs, age of onset, and underlying causes. Brain imaging suggests that the limbic system, including the hippocampus and cingulate gyrus, is often affected in canine epilepsy, which could explain the high incidence of comorbid behavioral problems such as anxiety and cognitive alterations. Resistance to antiseizure medications is a significant problem in both canine and human epilepsy, so dogs can be used to study mechanisms of drug resistance and develop novel therapeutic strategies to benefit both species. Importantly, dogs are large enough to accommodate intracranial EEG and responsive neurostimulation devices designed for humans. Studies in epileptic dogs with such devices have reported ictal and interictal events that are remarkably similar to those occurring in human epilepsy. Continuous (24/7) EEG recordings in a select group of epileptic dogs for >1 year have provided a rich dataset of unprecedented length for studying seizure periodicities and developing new methods for seizure forecasting. The data presented in this review substantiate that canine epilepsy is an excellent translational model for several facets of epilepsy research. Furthermore, several techniques of inducing seizures in laboratory dogs are discussed as related to therapeutic advances. Importantly, the development of vagus nerve stimulation as a novel therapy for drug-resistant epilepsy in people was based on a series of studies in dogs with induced seizures. Dogs with naturally occurring or induced seizures provide excellent large-animal models to bridge the translational gap between rodents and humans in the development of novel therapies. Furthermore, because the dog is not only a preclinical species for human medicine but also a potential patient and pet, research on this species serves both veterinary and human medicine.
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Affiliation(s)
- Wolfgang Löscher
- Department of Pharmacology, Toxicology and Pharmacy, University of Veterinary Medicine, Hannover, Germany
- Center for Systems Neuroscience, Hannover, Germany
- *Correspondence: Wolfgang Löscher
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4
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Camarillo-Rodriguez L, Leenen I, Waldman Z, Serruya M, Wanda PA, Herweg NA, Kahana MJ, Rubinstein D, Orosz I, Lega B, Podkorytova I, Gross RE, Worrell G, Davis KA, Jobst BC, Sheth SA, Weiss SA, Sperling MR. Temporal lobe interictal spikes disrupt encoding and retrieval of verbal memory: A subregion analysis. Epilepsia 2022; 63:2325-2337. [PMID: 35708911 DOI: 10.1111/epi.17334] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE The medial temporal lobe (MTL) encodes and recalls memories and can be a predominant site for interictal spikes (IS) in patients with focal epilepsy. It is unclear whether memory deficits are due to IS in the MTL producing a transient decline. Here, we investigated whether IS in the MTL subregions and lateral temporal cortex impact episodic memory encoding and recall. METHODS Seventy-eight participants undergoing presurgical evaluation for medically refractory focal epilepsy with depth electrodes placed in the temporal lobe participated in a verbal free recall task. IS were manually annotated during the pre-encoding, encoding, and recall epochs. We examined the effect of IS on word recall using mixed-effects logistic regression. RESULTS IS in the left hippocampus (odds ratio [OR] = .73, 95% confidence interval [CI] = .63-.84, p < .001) and left middle temporal gyrus (OR = .46, 95% CI = .27-.78, p < .05) during word encoding decreased subsequent recall performance. Within the left hippocampus, this effect was specific for area CA1 (OR = .76, 95% CI = .66-.88, p < .01) and dentate gyrus (OR = .74, 95% CI = .62-.89, p < .05). IS in other MTL subregions or inferior and superior temporal gyrus and IS occurring during the prestimulus window did not affect word encoding (p > .05). IS during retrieval in right hippocampal (OR = .22, 95% CI = .08-.63, p = .01) and parahippocampal regions (OR = .24, 95% CI = .07-.8, p < .05) reduced the probability of recalling a word. SIGNIFICANCE IS in medial and lateral temporal cortex contribute to transient memory decline during verbal episodic memory.
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Affiliation(s)
| | - Iwin Leenen
- Faculty of Psychology, National Autonomous University of Mexico, Mexico City, Mexico
| | - Zachary Waldman
- Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Mijail Serruya
- Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Paul A Wanda
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nora A Herweg
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel Rubinstein
- Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Iren Orosz
- Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA
| | | | | | - Robert E Gross
- Department of Neurosurgery, Emory University, Atlanta, Georgia, USA
| | | | - Kathryn A Davis
- Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Barbara C Jobst
- Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Shennan A Weiss
- Department of Neurology, State University of New York Downstate Medical Center, Brooklyn, New York, USA.,Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, New York, USA.,Departments of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, New York, USA
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
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Circannual incidence of seizure evacuations from the Canadian Arctic. Epilepsy Behav 2022; 127:108503. [PMID: 34954513 DOI: 10.1016/j.yebeh.2021.108503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 11/23/2022]
Abstract
OBJECTIVES Emerging evidence suggests that circadian rhythms affect seizure propensity in addition to, and possibly independent of, sleep-wake states. Subject to extreme seasonal changes in light and dark, the northerly Arctic can serve as a "natural experiment" to assess the real-life impact of environmental influences on seizure severity. Therefore, we evaluated the timing of seizure evacuations over 11.25 years in a well-defined region of the Canadian Arctic. METHODS Retrospective review of EEG database and patient records at the single "bottleneck" hospital to which all patients from the Kivalliq Region in Nunavut, Canada are evacuated for seizure emergencies. We calculated the mean resultant length (MRL) of circular data for circannual analysis, and conducted Rayleigh's test to assess for a statistical departure from circular uniformity. RESULTS Screening 40,392 EEGs, we found 117 medical evacuations from 99 distinct individuals from September 2009 to November 2020. Most evacuations occurred month-wise in May (19%); week-wise within a 7-day period in February (5%), June (5%), or November (5%); and day-wise within a 24-hour period in June (3%) or November (3%). Maximal MRL clustering occurred in April no matter if analyzed by day (0.16333, p = 0.04), week (0.16296, p = 0.04), or month (0.1736, p = 0.03). CONCLUSIONS A relative circannual increase in seizure evacuations between the winter and summer solstices may be related to increasing sleep loss when day length grows. Fewer evacuations between the summer and winter solstices may be related to decreased daylight and "catching up" on sleep when night length grows. Additional factors likely also play a role in circannual variation of seizure evacuations in the Arctic, which warrants further research.
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6
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Müller J, Yang H, Eberlein M, Leonhardt G, Uckermann O, Kuhlmann L, Tetzlaff R. Coherent false seizure prediction in epilepsy, coincidence or providence? Clin Neurophysiol 2021; 133:157-164. [PMID: 34844880 DOI: 10.1016/j.clinph.2021.09.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data. METHODS We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods. RESULTS For 9 out of 12 individuals both methods showed a performance better than chance. For all individuals we observed a positive correlation in predictions. For individuals with strong correlation in false predictions we were able to boost the performance of one method by excluding test samples based on the results of the second method. CONCLUSIONS Substantially different algorithms exhibit a highly consistent performance and a strong coherency in false and missing alarms. Hence, changing the underlying hypothesis of a preictal state of fixed time length prior to each seizure to a proictal state is more helpful than further optimizing classifiers. SIGNIFICANCE The outcome is significant for the evaluation of seizure prediction algorithms on continuous data.
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Affiliation(s)
- Jens Müller
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany.
| | - Hongliu Yang
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany
| | - Matthias Eberlein
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany
| | - Georg Leonhardt
- TU Dresden, Neurosurgery of University Hospital Carl Gustav Carus, Fetscherstr. 74, 01307 Dresden, Germany
| | - Ortrud Uckermann
- TU Dresden, Neurosurgery of University Hospital Carl Gustav Carus, Fetscherstr. 74, 01307 Dresden, Germany
| | - Levin Kuhlmann
- Department of Medicine, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Ronald Tetzlaff
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany
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7
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Khambhati AN, Shafi A, Rao VR, Chang EF. Long-term brain network reorganization predicts responsive neurostimulation outcomes for focal epilepsy. Sci Transl Med 2021; 13:13/608/eabf6588. [PMID: 34433640 DOI: 10.1126/scitranslmed.abf6588] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/12/2021] [Accepted: 06/15/2021] [Indexed: 12/21/2022]
Abstract
Responsive neurostimulation (RNS) devices, able to detect imminent seizures and to rapidly deliver electrical stimulation to the brain, are effective in reducing seizures in some patients with focal epilepsy. However, therapeutic response to RNS is often slow, is highly variable, and defies prognostication based on clinical factors. A prevailing view holds that RNS efficacy is primarily mediated by acute seizure termination; yet, stimulations greatly outnumber seizures and occur mostly in the interictal state, suggesting chronic modulation of brain networks that generate seizures. Here, using years-long intracranial neural recordings collected during RNS therapy, we found that patients with the greatest therapeutic benefit undergo progressive, frequency-dependent reorganization of interictal functional connectivity. The extent of this reorganization scales directly with seizure reduction and emerges within the first year of RNS treatment, enabling potential early prediction of therapeutic response. Our findings reveal a mechanism for RNS that involves network plasticity and may inform development of next-generation devices for epilepsy.
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Affiliation(s)
- Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.,Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Alia Shafi
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.,Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Vikram R Rao
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA. .,Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA. .,Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
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8
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Ferlauto L, Vagni P, Fanelli A, Zollinger EG, Monsorno K, Paolicelli RC, Ghezzi D. All-polymeric transient neural probe for prolonged in-vivo electrophysiological recordings. Biomaterials 2021; 274:120889. [PMID: 33992836 DOI: 10.1016/j.biomaterials.2021.120889] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 04/26/2021] [Accepted: 05/06/2021] [Indexed: 10/21/2022]
Abstract
Transient bioelectronics has grown fast, opening possibilities never thought before. In medicine, transient implantable devices are interesting because they could eliminate the risks related to surgical retrieval and reduce the chronic foreign body reaction. Despite recent progress in this area, the potential of transient bioelectronics is still limited by their short functional lifetime owed to the fast dissolution rate of degradable metals, which is typically a few days or weeks. Here we report that a switch from degradable metals to an entirely polymer-based approach allows for a slower degradation process and a longer lifetime of the transient probe, thus opening new possibilities for transient medical devices. As a proof-of-concept, we fabricated all-polymeric transient neural probes that can monitor brain activity in mice for a few months, rather than a few days or weeks. Also, we extensively evaluated the foreign body reaction around the implant during the probe degradation. This kind of devices might pave the way for several applications in neuroprosthetics.
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Affiliation(s)
- Laura Ferlauto
- Medtronic Chair in Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École polytechnique fédérale de Lausanne, Switzerland
| | - Paola Vagni
- Medtronic Chair in Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École polytechnique fédérale de Lausanne, Switzerland
| | - Adele Fanelli
- Medtronic Chair in Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École polytechnique fédérale de Lausanne, Switzerland
| | - Elodie Geneviève Zollinger
- Medtronic Chair in Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École polytechnique fédérale de Lausanne, Switzerland
| | - Katia Monsorno
- Department of Biomedical Sciences, Faculty of Biology and Medicine, University of Lausanne, Switzerland
| | - Rosa Chiara Paolicelli
- Department of Biomedical Sciences, Faculty of Biology and Medicine, University of Lausanne, Switzerland
| | - Diego Ghezzi
- Medtronic Chair in Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École polytechnique fédérale de Lausanne, Switzerland.
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9
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Beniczky S, Karoly P, Nurse E, Ryvlin P, Cook M. Machine learning and wearable devices of the future. Epilepsia 2020; 62 Suppl 2:S116-S124. [PMID: 32712958 DOI: 10.1111/epi.16555] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/05/2020] [Accepted: 05/08/2020] [Indexed: 01/06/2023]
Abstract
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippa Karoly
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Ewan Nurse
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland
| | - Mark Cook
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
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10
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Conrad EC, Tomlinson SB, Wong JN, Oechsel KF, Shinohara RT, Litt B, Davis KA, Marsh ED. Spatial distribution of interictal spikes fluctuates over time and localizes seizure onset. Brain 2020; 143:554-569. [PMID: 31860064 DOI: 10.1093/brain/awz386] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 10/15/2019] [Accepted: 10/25/2019] [Indexed: 12/21/2022] Open
Abstract
The location of interictal spikes is used to aid surgical planning in patients with medically refractory epilepsy; however, their spatial and temporal dynamics are poorly understood. In this study, we analysed the spatial distribution of interictal spikes over time in 20 adult and paediatric patients (12 females, mean age = 34.5 years, range = 5-58) who underwent intracranial EEG evaluation for epilepsy surgery. Interictal spikes were detected in the 24 h surrounding each seizure and spikes were clustered based on spatial location. The temporal dynamics of spike spatial distribution were calculated for each patient and the effects of sleep and seizures on these dynamics were evaluated. Finally, spike location was assessed in relation to seizure onset location. We found that spike spatial distribution fluctuated significantly over time in 14/20 patients (with a significant aggregate effect across patients, Fisher's method: P < 0.001). A median of 12 sequential hours were required to capture 80% of the variability in spike spatial distribution. Sleep and postictal state affected the spike spatial distribution in 8/20 and 4/20 patients, respectively, with a significant aggregate effect (Fisher's method: P < 0.001 for each). There was no evidence of pre-ictal change in the spike spatial distribution for any patient or in aggregate (Fisher's method: P = 0.99). The electrode with the highest spike frequency and the electrode with the largest area of downstream spike propagation both localized the seizure onset zone better than predicted by chance (Wilcoxon signed-rank test: P = 0.005 and P = 0.002, respectively). In conclusion, spikes localize seizure onset. However, temporal fluctuations in spike spatial distribution, particularly in relation to sleep and post-ictal state, can confound localization. An adequate duration of intracranial recording-ideally at least 12 sequential hours-capturing both sleep and wakefulness should be obtained to sufficiently sample the interictal network.
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Affiliation(s)
- Erin C Conrad
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Samuel B Tomlinson
- Division of Child Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA
| | - Jeremy N Wong
- Division of Child Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kelly F Oechsel
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Litt
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Eric D Marsh
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.,Division of Child Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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11
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Schroeder GM, Diehl B, Chowdhury FA, Duncan JS, de Tisi J, Trevelyan AJ, Forsyth R, Jackson A, Taylor PN, Wang Y. Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy. Proc Natl Acad Sci U S A 2020; 117:11048-11058. [PMID: 32366665 PMCID: PMC7245106 DOI: 10.1073/pnas.1922084117] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.
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Affiliation(s)
- Gabrielle M Schroeder
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - John S Duncan
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Andrew J Trevelyan
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Rob Forsyth
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Andrew Jackson
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom;
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
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12
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Nasseri M, Kremen V, Nejedly P, Kim I, Chang SY, Joon Jo H, Guragain H, Nelson N, Patterson E, Sturges BK, Crowe CM, Denison T, Brinkmann BH, Worrell GA. Semi-supervised Training Data Selection Improves Seizure Forecasting in Canines with Epilepsy. Biomed Signal Process Control 2019; 57. [PMID: 32863855 DOI: 10.1016/j.bspc.2019.101743] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Objective Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. Methods We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. Results In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p<0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. Conclusion Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. Significance The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.
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Affiliation(s)
- Mona Nasseri
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Vaclav Kremen
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.,Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Petr Nejedly
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Inyong Kim
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Su-Youne Chang
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.,Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Hang Joon Jo
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hari Guragain
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Nathaniel Nelson
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Edward Patterson
- Department of Veterinary Clinical Sciences, University of Minnesota College of Veterinary Medicine, St. Paul, MN, USA
| | - Beverly K Sturges
- Veterinary Medical Teaching Hospital, University of California at Davis, Davis, CA 95616, USA
| | - Chelsea M Crowe
- Veterinary Medical Teaching Hospital, University of California at Davis, Davis, CA 95616, USA
| | - Tim Denison
- Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK
| | - Benjamin H Brinkmann
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Gregory A Worrell
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
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13
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Devinsky O, Boesch JM, Cerda-Gonzalez S, Coffey B, Davis K, Friedman D, Hainline B, Houpt K, Lieberman D, Perry P, Prüss H, Samuels MA, Small GW, Volk H, Summerfield A, Vite C, Wisniewski T, Natterson-Horowitz B. A cross-species approach to disorders affecting brain and behaviour. Nat Rev Neurol 2019; 14:677-686. [PMID: 30287906 DOI: 10.1038/s41582-018-0074-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Structural and functional elements of biological systems are highly conserved across vertebrates. Many neurological and psychiatric conditions affect both humans and animals. A cross-species approach to the study of brain and behaviour can advance our understanding of human disorders via the identification of unrecognized natural models of spontaneous disorders, thus revealing novel factors that increase vulnerability or resilience, and via the assessment of potential therapies. Moreover, diagnostic and therapeutic advances in human neurology and psychiatry can often be adapted for veterinary patients. However, clinical and research collaborations between physicians and veterinarians remain limited, leaving this wealth of comparative information largely untapped. Here, we review pain, cognitive decline syndromes, epilepsy, anxiety and compulsions, autoimmune and infectious encephalitides and mismatch disorders across a range of animal species, looking for novel insights with translational potential. This comparative perspective can help generate novel hypotheses, expand and improve clinical trials and identify natural animal models of disease resistance and vulnerability.
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Affiliation(s)
- Orrin Devinsky
- Department of Neurology, New York University (NYU) Langone Medical Center and NYU School of Medicine, New York, NY, USA.
| | - Jordyn M Boesch
- College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | | | - Barbara Coffey
- Department of Child and Adolescent Psychiatry, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kathryn Davis
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel Friedman
- Department of Neurology, New York University (NYU) Langone Medical Center and NYU School of Medicine, New York, NY, USA
| | - Brian Hainline
- Department of Neurology, New York University (NYU) Langone Medical Center and NYU School of Medicine, New York, NY, USA
| | - Katherine Houpt
- College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Daniel Lieberman
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Pamela Perry
- College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Harald Prüss
- Department of Neurology with Experimental Neurology, Charité University Medicine Berlin, Berlin, Germany, and German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | | | - Gary W Small
- University of California-Los Angeles (UCLA) Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Holger Volk
- Veterinary Neurology and Neurosurgery, The Royal Veterinary College, University of London, London, UK
| | - Artur Summerfield
- Institute of Virology and Immunology and Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Charles Vite
- School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas Wisniewski
- Department of Neurology, New York University (NYU) Langone Medical Center and NYU School of Medicine, New York, NY, USA
| | - Barbara Natterson-Horowitz
- Department of Ecology and Evolutionary Biology, Division of Cardiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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14
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Weiss SA, Waldman Z, Raimondo F, Slezak D, Donmez M, Worrell G, Bragin A, Engel J, Staba R, Sperling M. Localizing epileptogenic regions using high-frequency oscillations and machine learning. Biomark Med 2019; 13:409-418. [PMID: 31044598 DOI: 10.2217/bmm-2018-0335] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.
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Affiliation(s)
- Shennan A Weiss
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Zachary Waldman
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Federico Raimondo
- Department of Computer Science, Faculty of Exact & Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific & Technical Research Council, University of Buenos Aires, Buenos Aires, Argentina
| | - Diego Slezak
- Department of Computer Science, Faculty of Exact & Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific & Technical Research Council, University of Buenos Aires, Buenos Aires, Argentina
| | - Mustafa Donmez
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), Mayo Clinic, Rochester, MN 55905, USA
| | - Anatol Bragin
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Michael Sperling
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
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15
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Seneviratne U, Karoly P, Freestone DR, Cook MJ, Boston RC. Methods for the Detection of Seizure Bursts in Epilepsy. Front Neurol 2019; 10:156. [PMID: 30873108 PMCID: PMC6400839 DOI: 10.3389/fneur.2019.00156] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 02/07/2019] [Indexed: 12/15/2022] Open
Abstract
Background: Seizure clusters and “bursts” are of clinical importance. Clusters are reported to be a marker of antiepileptic drug resistance. Additionally, seizure clustering has been found to be associated with increased morbidity and mortality. However, there are no statistical methods described in the literature to delineate bursting phenomenon in epileptic seizures. Methods: We present three automatic burst detection methods referred to as precision constrained grouping (PCG), burst duration constrained grouping (BCG), and interseizure interval constrained grouping (ICG). Concordance correlation coefficients were used to confirm the pairwise agreement between common bursts isolated using these three automatic burst detection procedures. Additionally, three graphical methods were employed to demonstrate seizure bursts: modified scatter plots, staircase plots, and dropline plots. Burst detection procedures are demonstrated on data from continuous intracranial ambulatory EEG monitoring in a patient diagnosed with drug-refractory focal epilepsy. Results: We analyzed 1,569 seizures, from our assigned index patient, captured on ambulatory intracranial EEG monitoring. A total of 31, 32, and 32 seizure bursts were detected by the three quantitative methods (BCG, ICG, and PCG), respectively. The concordance correlation coefficient was ≥0.99 signifying considerably stronger than chance burst detector agreements with one another. Conclusions: Bursting is a quantifiable temporal phenomenon in epilepsy and seizure bursts can be reliably detected using our methodology.
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Affiliation(s)
- Udaya Seneviratne
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC, Australia.,Department of Neuroscience, Monash Medical Centre, Melbourne, VIC, Australia.,Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Philippa Karoly
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC, Australia.,Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Dean R Freestone
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC, Australia.,Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Mark J Cook
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Ray C Boston
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC, Australia
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16
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Abstract
PURPOSE OF REVIEW The endogenous circadian rhythms are one of the cardinal processes that control sleep. They are self-sustaining biological rhythms with a periodicity of approximately 24 hours that may be entrained by external zeitgebers (German for time givers), such as light, exercise, and meal times. This article discusses the physiology of the circadian rhythms, their relationship to neurologic disease, and the presentation and treatment of circadian rhythm sleep-wake disorders. RECENT FINDINGS Classic examples of circadian rhythms include cortisol and melatonin secretion, body temperature, and urine volume. More recently, the impact of circadian rhythm on several neurologic disorders has been investigated, such as the timing of occurrence of epileptic seizures as well as neurobehavioral functioning in dementia. Further updates include a more in-depth understanding of the symptoms, consequences, and treatment of circadian sleep-wake disorders, which may occur because of extrinsic misalignment with clock time or because of intrinsic dysfunction of the brain. An example of extrinsic misalignment occurs with jet lag during transmeridian travel or with intrinsic circadian rhythm sleep-wake disorders such as advanced or delayed sleep-wake phase disorders. In advanced sleep-wake phase disorder, which is most common in elderly individuals, sleep onset and morning arousal are undesirably early, leading to impaired evening function with excessive sleepiness and sleep-maintenance insomnia with early morning awakening. By contrast, delayed sleep-wake phase disorder is characterized by an inability to initiate sleep before the early morning hours, with subsequent delayed rise time, leading to clinical symptoms of severe sleep-onset insomnia coupled with excessive daytime sleepiness in the morning hours, as patients are unable to "sleep in" to attain sufficient sleep quantity. Irregular sleep-wake rhythm disorder is misentrainment with patches of brief sleep and wakefulness spread throughout the day, leading to unstable sleep and waking behavioral patterns and an entirely idiosyncratic sleep-wake schedule. SUMMARY Familiarity with these major circadian rhythm sleep-wake disorder phenotypes and their overlap with other neurologic disorders is essential for the neurologist so that clinicians may intervene and improve patient functioning and quality of life.
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17
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The roles of surgery and technology in understanding focal epilepsy and its comorbidities. Lancet Neurol 2018; 17:373-382. [DOI: 10.1016/s1474-4422(18)30031-0] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 01/12/2018] [Accepted: 01/16/2018] [Indexed: 01/21/2023]
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18
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Ung H, Baldassano SN, Bink H, Krieger AM, Williams S, Vitale F, Wu C, Freestone D, Nurse E, Leyde K, Davis KA, Cook M, Litt B. Intracranial EEG fluctuates over months after implanting electrodes in human brain. J Neural Eng 2017; 14:056011. [PMID: 28862995 PMCID: PMC5860812 DOI: 10.1088/1741-2552/aa7f40] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. APPROACH Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient's recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. MAIN RESULTS A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. SIGNIFICANCE These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.
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Affiliation(s)
- Hoameng Ung
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
| | - Steven N. Baldassano
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
| | - Hank Bink
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
| | - Abba M Krieger
- Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shawniqua Williams
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
| | - Chengyuan Wu
- Department of Neurosurgery, Thomas Jefferson University Hospitals, Philadelphia, PA, USA
| | - Dean Freestone
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Victoria, Australia
| | - Ewan Nurse
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Victoria, Australia
- Department of Biomedical Engineering, University of Melbourne, Victoria, Australia
| | - Kent Leyde
- Cascade Medical Devices, Seattle, Washington
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Cook
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Victoria, Australia
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
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