92
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Critical slowing down as a biomarker for seizure susceptibility

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.

          Abstract

          Critical slowing (associated with increased variance and autocorrelation) can precede critical state transitions. Here, the authors show critical slowing can be used as a marker in seizure forecasting algorithms.

          Related collections

          Most cited references34

          • Record: found
          • Abstract: found
          • Article: not found

          Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.

          Seizure prediction would be clinically useful in patients with epilepsy and could improve safety, increase independence, and allow acute treatment. We did a multicentre clinical feasibility study to assess the safety and efficacy of a long-term implanted seizure advisory system designed to predict seizure likelihood and quantify seizures in adults with drug-resistant focal seizures. We enrolled patients at three centres in Melbourne, Australia, between March 24, 2010, and June 21, 2011. Eligible patients had between two and 12 disabling partial-onset seizures per month, a lateralised epileptogenic zone, and no history of psychogenic seizures. After devices were surgically implanted, patients entered a data collection phase, during which an algorithm for identification of periods of high, moderate, and low seizure likelihood was established. If the algorithm met performance criteria (ie, sensitivity of high-likelihood warnings greater than 65% and performance better than expected through chance prediction of randomly occurring events), patients then entered an advisory phase and received information about seizure likelihood. The primary endpoint was the number of device-related adverse events at 4 months after implantation. Our secondary endpoints were algorithm performance at the end of the data collection phase, clinical effectiveness (measures of anxiety, depression, seizure severity, and quality of life) 4 months after initiation of the advisory phase, and longer-term adverse events. This trial is registered with ClinicalTrials.gov, number NCT01043406. We implanted 15 patients with the advisory system. 11 device-related adverse events were noted within four months of implantation, two of which were serious (device migration, seroma); an additional two serious adverse events occurred during the first year after implantation (device-related infection, device site reaction), but were resolved without further complication. The device met enabling criteria in 11 patients upon completion of the data collection phase, with high likelihood performance estimate sensitivities ranging from 65% to 100%. Three patients' algorithms did not meet performance criteria and one patient required device removal because of an adverse event before sufficient training data were acquired. We detected no significant changes in clinical effectiveness measures between baseline and 4 months after implantation. This study showed that intracranial electroencephalographic monitoring is feasible in ambulatory patients with drug-resistant epilepsy. If these findings are replicated in larger, longer studies, accurate definition of preictal electrical activity might improve understanding of seizure generation and eventually lead to new management strategies. NeuroVista. Copyright © 2013 Elsevier Ltd. All rights reserved.
            • Record: found
            • Abstract: not found
            • Article: not found

            Complex systems: ecology for bankers.

              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Multi-day rhythms modulate seizure risk in epilepsy

              Epilepsy is defined by the seemingly random occurrence of spontaneous seizures. The ability to anticipate seizures would enable preventative treatment strategies. A central but unresolved question concerns the relationship of seizure timing to fluctuating rates of interictal epileptiform discharges (here termed interictal epileptiform activity, IEA), a marker of brain irritability observed between seizures by electroencephalography (EEG). Here, in 37 subjects with an implanted brain stimulation device that detects IEA and seizures over years, we find that IEA oscillates with circadian and subject-specific multidien (multi-day) periods. Multidien periodicities, most commonly 20–30 days in duration, are robust and relatively stable for up to 10 years in men and women. We show that seizures occur preferentially during the rising phase of multidien IEA rhythms. Combining phase information from circadian and multidien IEA rhythms provides a novel biomarker for determining relative seizure risk with a large effect size in most subjects.

                Author and article information

                Contributors
                matiasim@unimelb.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                1 May 2020
                1 May 2020
                2020
                : 11
                : 2172
                Affiliations
                [1 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Medicine, St Vincent’s Hospital, , The University of Melbourne, ; Melbourne, Australia
                [2 ]Seer Medical, Melbourne, Australia
                [3 ]ISNI 0000 0001 1091 2917, GRID grid.412282.f, Department of Neurology, , University Clinic Carl Gustav Carus, ; Dresden, Germany
                [4 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Boston Children’s Hospital, ; Boston, MA USA
                [5 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Graeme Clark Institute, , The University of Melbourne, ; Melbourne, Australia
                [6 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Biomedical Engineering, , The University of Melbourne, ; Melbourne, Australia
                [7 ]ISNI 0000 0004 1937 116X, GRID grid.4491.8, Department of Physiology, Second Faculty of Medicine, , Charles University, ; Prague, Czech Republic
                [8 ]ISNI 0000 0001 1015 3316, GRID grid.418095.1, Department of Developmental Epileptology, Institute of Physiology, , Czech Academy of Sciences, ; Prague, Czech Republic
                [9 ]ISNI 0000000121738213, GRID grid.6652.7, Department of Circuit Theory, Faculty of Electrical Engineering, , Czech Technical University in Prague, ; Prague, Czech Republic
                [10 ]ISNI 0000 0004 0369 3922, GRID grid.448092.3, Institute of Computer Science of the Czech Academy of Sciences, ; Prague, Czech Republic
                [11 ]GRID grid.447902.c, National Institute of Mental Health, ; Klecany, Czech Republic
                [12 ]ISNI 0000 0004 1936 7857, GRID grid.1002.3, Faculty of Information Technology, , Monash University, ; Clayton, Victoria Australia
                [13 ]ISNI 0000 0004 0409 2862, GRID grid.1027.4, Centre for Human Psychopharmacology, , Swinburne University of Technology, ; Hawthorn, Victoria Australia
                Author information
                http://orcid.org/0000-0003-1432-8835
                http://orcid.org/0000-0002-1750-5131
                http://orcid.org/0000-0002-5497-7234
                http://orcid.org/0000-0001-5672-2772
                http://orcid.org/0000-0003-4955-8577
                http://orcid.org/0000-0003-1402-1470
                http://orcid.org/0000-0002-8875-4135
                http://orcid.org/0000-0002-5108-6348
                Article
                15908
                10.1038/s41467-020-15908-3
                7195436
                32358560
                67cb946b-b3aa-45cb-b143-1a8657a6713a
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 April 2019
                : 30 March 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                cognitive neuroscience,epilepsy
                Uncategorized
                cognitive neuroscience, epilepsy

                Comments

                Comment on this article

                Related Documents Log