Evidence map›Paper›PMID 32986695›Full record

ArticlePLoS computational biology2020

Domino-like transient dynamics at seizure onset in epilepsy.

Jennifer Creaser, Congping Lin, Thomas Ridler, Jonathan T Brown, Wendyl D'Souza, Udaya Seneviratne, Mark Cook, John R Terry, Krasimira Tsaneva-Atanasova

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 5 countries.

Jennifer CreaserDepartment of Mathematics, University of Exeter, Exeter, EX4 4QF, UK.ORCID 0000-0001-7009-5381
Congping LinCenter for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.ORCID 0000-0001-9723-8880
Thomas RidlerInstitute of Biomedical and Clinical Sciences, College of Medicine and Health, University of Exeter, EX4 4PS, UK.ORCID 0000-0002-8236-9033
Jonathan T BrownInstitute of Biomedical and Clinical Sciences, College of Medicine and Health, University of Exeter, EX4 4PS, UK.ORCID 0000-0001-5269-7661
Wendyl D'SouzaDepartment of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC 3065, Australia.
Udaya SeneviratneDepartment of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC 3065, Australia.ORCID 0000-0003-0182-9702
Mark CookDepartment of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC 3065, Australia.ORCID 0000-0002-8875-4135
John R TerryEPSRC Centre for Predictive modeling in Healthcare, University of Exeter, Exeter, EX4 4QJ, UK.ORCID 0000-0002-6829-5736
Krasimira Tsaneva-AtanasovaDepartment of Mathematics, University of Exeter, Exeter, EX4 4QF, UK.ORCID 0000-0002-6294-7051
Engineering and Physical Sciences Research Council · GBThe University of Melbourne · AUUniversity of Exeter · GBHuazhong University of Science and Technology · CN

Funding

Medical Research Council MR/S019499/1
6 · The paper itself

Abstract

The International League Against Epilepsy (ILAE) groups seizures into "focal", "generalized" and "unknown" based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.

Indexed as

AnimalsElectroencephalographyEpilepsyHumansMiceModels, BiologicalSeizures

Identifiers

PMID32986695
PMCPMC7544071
OpenAlexW3087935031

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.