A New Perspective in Epilepsy Classification: Applying the Taxonomy of Seizure Dynamotypes to Non-Invasive EEG and examining dynamical changes across sleep stages. DOI Creative Commons
Miriam Guendelman, Rotem Vekslar, Oren Shriki

et al.

eNeuro, Journal Year: 2025, Volume and Issue: unknown, P. ENEURO.0157 - 24.2024

Published: Jan. 2, 2025

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using dynamical systems–based framework—the taxonomy dynamotypes—previously examined only in invasive data. We applied principal component independent analysis recordings from 1,177 seizures 158 patients with focal epilepsy, decomposing signals into components (ICs). The ICs were visually labeled for clear bifurcation morphologies, which then Bayesian multilevel modeling context factors. Our reveals that certain onset bifurcations (SNIC SupH) are more prevalent during wakefulness compared their reduced rate non-rapid eye movement (NREM) sleep, particularly NREM3. discuss possible implications our results approaches suggest additional avenues continue this exploration. Furthermore, we demonstrate feasibility automating process machine learning, achieving high performance identifying seizure-related classifying inter-spike interval changes. findings noise may obscure technical improvements could enhance detection accuracy. Expanding dataset incorporating long-term biological rhythms, such as circadian multiday cycles, provide comprehensive understanding improve decision-making. Significance statement Traditional focuses symptoms electrophysiological signs but overlooks dynamics. dynamotypes introduces novel computational approach links transition signatures these While previously recordings, extends non-invasive EEG. relationship between sleep stages integrating models reveal insights timing generalization, opening new pathways better diagnostics. Broader adoption is limited its labor-intensive visual inspection process. Here, potential automated classification, enabling scale larger cohorts.

Language: Английский

Seizure event detection using intravital two-photon calcium imaging data DOI Creative Commons
Matthew Stern, Eric R. Cole, Robert E. Gross

et al.

Neurophotonics, Journal Year: 2024, Volume and Issue: 11(02)

Published: Jan. 25, 2024

Intravital cellular calcium imaging has emerged as a powerful tool to investigate how different types of neurons interact at the microcircuit level produce seizure activity, with newfound potential understand epilepsy. Although many methods exist measure seizure-related activity in traditional electrophysiology, few yet for imaging.

Language: Английский

Citations

5
Modeling seizures: From single neurons to networks DOI Creative Commons
Damien Depannemaecker, Alain Destexhe, Viktor Jirsa

et al.

Seizure, Journal Year: 2021, Volume and Issue: 90, P. 4 - 8

Published: June 17, 2021

Dynamical system tools offer a complementary approach to detailed biophysical seizure modeling, with high potential for clinical applications. This review describes the theoretical framework that provides basis theorizing certain properties of seizures and their classification according dynamical at onset offset. We describe various modeling approaches spanning different scales, from single neurons large-scale networks. narrative an accessible overview this field, including non-exhaustive examples key recent works.

Language: Английский

Citations

31
Wheels Within Wheels: Theory and Practice of Epileptic Networks DOI Open Access
Kathryn A. Davis, Viktor Jirsa, Catherine A. Schevon

et al.

Epiliepsy currents/Epilepsy currents, Journal Year: 2021, Volume and Issue: 21(4), P. 243 - 247

Published: May 14, 2021

Language: Английский

Citations

29
A library of quantitative markers of seizure severity DOI Creative Commons
Sarah J Gascoigne,

Leonard Waldmann,

Gabrielle M. Schroeder

et al.

Epilepsia, Journal Year: 2023, Volume and Issue: 64(4), P. 1074 - 1086

Published: Feb. 2, 2023

Abstract Objective Understanding fluctuations in seizure severity within individuals is important for determining treatment outcomes and responses to therapy, as well assessing novel treatments epilepsy. Current methods grading rely on qualitative interpretations from patients clinicians. Quantitative measures of would complement existing approaches electroencephalographic (EEG) monitoring, outcome prediction. Therefore, we developed a library quantitative EEG markers that assess the spread intensity abnormal electrical activity during after seizures. Methods We analyzed intracranial (iEEG) recordings 1009 seizures 63 patients. For each seizure, computed 16 capture signal magnitude, spread, duration, postictal suppression Results distinguished focal versus subclinical across In individual patients, 53% had moderate large difference (rank sum , ) between three or more markers. Circadian longer term changes were found majority Significance demonstrate feasibility using iEEG measure severity. Our distinguish types are therefore sensitive established differences results also suggest modulated over different timescales. envisage our proposed will be expanded updated collaboration with epilepsy research community include modalities.

Language: Английский

Citations

12
A New Perspective in Epilepsy Classification: Applying the Taxonomy of Seizure Dynamotypes to Non-Invasive EEG and examining dynamical changes across sleep stages. DOI Creative Commons
Miriam Guendelman, Rotem Vekslar, Oren Shriki

et al.

eNeuro, Journal Year: 2025, Volume and Issue: unknown, P. ENEURO.0157 - 24.2024

Published: Jan. 2, 2025

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using dynamical systems–based framework—the taxonomy dynamotypes—previously examined only in invasive data. We applied principal component independent analysis recordings from 1,177 seizures 158 patients with focal epilepsy, decomposing signals into components (ICs). The ICs were visually labeled for clear bifurcation morphologies, which then Bayesian multilevel modeling context factors. Our reveals that certain onset bifurcations (SNIC SupH) are more prevalent during wakefulness compared their reduced rate non-rapid eye movement (NREM) sleep, particularly NREM3. discuss possible implications our results approaches suggest additional avenues continue this exploration. Furthermore, we demonstrate feasibility automating process machine learning, achieving high performance identifying seizure-related classifying inter-spike interval changes. findings noise may obscure technical improvements could enhance detection accuracy. Expanding dataset incorporating long-term biological rhythms, such as circadian multiday cycles, provide comprehensive understanding improve decision-making. Significance statement Traditional focuses symptoms electrophysiological signs but overlooks dynamics. dynamotypes introduces novel computational approach links transition signatures these While previously recordings, extends non-invasive EEG. relationship between sleep stages integrating models reveal insights timing generalization, opening new pathways better diagnostics. Broader adoption is limited its labor-intensive visual inspection process. Here, potential automated classification, enabling scale larger cohorts.

Language: Английский

Citations

0