dc.contributor.author
Kashyap, Amrit
dc.contributor.author
Müller, Paul
dc.contributor.author
Miron, Gadi
dc.contributor.author
Meisel, Christian
dc.date.accessioned
2025-07-09T14:31:09Z
dc.date.available
2025-07-09T14:31:09Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/48182
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-47904
dc.description.abstract
Epilepsy is characterized by recurrent, unprovoked seizures. Accurate prediction of seizure occurrence has long been a clinical goal since this would allow to optimize patient treatment, prevent injuries due to seizures, and alleviate the patient burden of unpredictability. Advances in implantable electroencephalographic (EEG) devices, allowing for long-term interictal EEG recordings, have facilitated major progress in this field. Recently, it has been discovered that interictal brain activity demonstrates circadian and multi-dien cycles that are strongly aligned, or phase locked, with seizure risk. Thus, cyclical brain activity patterns have been used to forecast seizures. However, in the effort to develop a clinically useful EEG based seizure forecasting system, challenges remain. Firstly, multiple EEG features demonstrate cyclical patterns, but it remains unclear which feature is best suited for predicting seizures. Secondly, the technology for long-term EEG recording is currently limited in both spatial and temporal sampling resolution. In this study, we compare five established EEG metrics:synchrony, spatial correlation, temporal correlation, signal variance which have been motivated from critical dynamics theory, and interictal epileptiform discharge (IED) which are a traditional marker of seizure propensity. We assess their effectiveness in detecting 24-h and seizure cycles as well as their robustness under spatial and temporal subsampling. Analyzing intracranial EEG data from 23 patients, we report that all examined features exhibit 24-h cycles. Spatial correlation, signal variance, and synchrony showed the highest phase locking with seizures, while IED rates were the lowest. Notably, spatial and temporal correlation were also found to be highly correlated to each other, as were signal variance and IED-suggesting some features may reflect similar aspects of cortical dynamics, whereas others provide complementary information. All features proved robust under subsampling, indicating that the dynamic properties of interictal activity evolve slowly and are not confined to specific brain regions. Our results may aid future translational research by assisting in design and testing of EEG based seizure forecasting systems.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Critical dynamics and interictal epileptiform discharge: a comparative analysis with respect to tracking seizure risk cycles
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
1420217
dcterms.bibliographicCitation.doi
10.3389/fnetp.2024.1420217
dcterms.bibliographicCitation.journaltitle
Frontiers in Network Physiology
dcterms.bibliographicCitation.originalpublishername
Frontiers Media SA
dcterms.bibliographicCitation.volume
4
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
39044940
dcterms.isPartOf.eissn
2674-0109