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Resting-state background features demonstrate multidien cycles in long-term EEG device recordings

BACKGROUND: Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles c...

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Autores principales: Ojemann, William K.S., Scheid, Brittany H., Mouchtaris, Sofia, Lucas, Alfredo, LaRocque, Joshua J., Aguila, Carlos, Ashourvan, Arian, Caciagli, Lorenzo, Davis, Kathryn A., Conrad, Erin C., Litt, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350154/
https://www.ncbi.nlm.nih.gov/pubmed/37461688
http://dx.doi.org/10.1101/2023.07.05.23291521
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author Ojemann, William K.S.
Scheid, Brittany H.
Mouchtaris, Sofia
Lucas, Alfredo
LaRocque, Joshua J.
Aguila, Carlos
Ashourvan, Arian
Caciagli, Lorenzo
Davis, Kathryn A.
Conrad, Erin C.
Litt, Brian
author_facet Ojemann, William K.S.
Scheid, Brittany H.
Mouchtaris, Sofia
Lucas, Alfredo
LaRocque, Joshua J.
Aguila, Carlos
Ashourvan, Arian
Caciagli, Lorenzo
Davis, Kathryn A.
Conrad, Erin C.
Litt, Brian
author_sort Ojemann, William K.S.
collection PubMed
description BACKGROUND: Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. OBJECTIVE/HYPOTHESIS: We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may disrupt these cycles. METHODS: We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. RESULTS: Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). CONCLUSIONS: Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may disrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.
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spelling pubmed-103501542023-07-17 Resting-state background features demonstrate multidien cycles in long-term EEG device recordings Ojemann, William K.S. Scheid, Brittany H. Mouchtaris, Sofia Lucas, Alfredo LaRocque, Joshua J. Aguila, Carlos Ashourvan, Arian Caciagli, Lorenzo Davis, Kathryn A. Conrad, Erin C. Litt, Brian medRxiv Article BACKGROUND: Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. OBJECTIVE/HYPOTHESIS: We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may disrupt these cycles. METHODS: We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. RESULTS: Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). CONCLUSIONS: Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may disrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices. Cold Spring Harbor Laboratory 2023-07-07 /pmc/articles/PMC10350154/ /pubmed/37461688 http://dx.doi.org/10.1101/2023.07.05.23291521 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Ojemann, William K.S.
Scheid, Brittany H.
Mouchtaris, Sofia
Lucas, Alfredo
LaRocque, Joshua J.
Aguila, Carlos
Ashourvan, Arian
Caciagli, Lorenzo
Davis, Kathryn A.
Conrad, Erin C.
Litt, Brian
Resting-state background features demonstrate multidien cycles in long-term EEG device recordings
title Resting-state background features demonstrate multidien cycles in long-term EEG device recordings
title_full Resting-state background features demonstrate multidien cycles in long-term EEG device recordings
title_fullStr Resting-state background features demonstrate multidien cycles in long-term EEG device recordings
title_full_unstemmed Resting-state background features demonstrate multidien cycles in long-term EEG device recordings
title_short Resting-state background features demonstrate multidien cycles in long-term EEG device recordings
title_sort resting-state background features demonstrate multidien cycles in long-term eeg device recordings
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350154/
https://www.ncbi.nlm.nih.gov/pubmed/37461688
http://dx.doi.org/10.1101/2023.07.05.23291521
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