ArticlePLoS computational biology2021
Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics.Scientific reports · 2024Article
- Correspondence between scalp-EEG and stereoelectroencephalography seizure-onset patterns in patients with MRI-negative drug-resistant focal epilepsy.Epilepsia open · 2024Article
- Utilizing GO/PEDOT:PSS/PtNPs-enhanced high-stability microelectrode arrays for investigating epilepsy-induced striatal electrophysiology alterations.Frontiers in bioengineering and biotechnology · 2024Article
- Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics.Network neuroscience (Cambridge, Mass.) · 2024Article
- Electric Field, Ictal Theta Power, and Clinical Outcomes in Electroconvulsive Therapy.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Ictal Theta Power as an Electroconvulsive Therapy Safety Biomarker: A Pilot Study.The journal of ECT · 2022Article
- Computational modeling of seizure spread on a cortical surface.Journal of computational neuroscience · 2022Article
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Authors and funding
5 authors.
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Abstract
Intracranial electroencephalography is a standard tool in clinical evaluation of patients with focal epilepsy. Various early electrographic seizure patterns differing in frequency, amplitude, and waveform of the oscillations are observed. The pattern most common in the areas of seizure propagation is the so-called theta-alpha activity (TAA), whose defining features are oscillations in the θ - α range and gradually increasing amplitude. A deeper understanding of the mechanism underlying the generation of the TAA pattern is however lacking. In this work we evaluate the hypothesis that the TAA patterns are caused by seizures spreading across the cortex. To do so, we perform simulations of seizure dynamics on detailed patient-derived cortical surfaces using the spreading seizure model as well as reference models with one or two homogeneous sources. We then detect the occurrences of the TAA patterns both in the simulated stereo-electroencephalographic signals and in the signals of recorded epileptic seizures from a cohort of fifty patients, and we compare the features of the groups of detected TAA patterns to assess the plausibility of the different models. Our results show that spreading seizure hypothesis is qualitatively consistent with the evidence available in the seizure recordings, and it can explain the features of the detected TAA groups best among the examined models.
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