Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation
Rattachement africain : ir, us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators. The proposed method approaches the localization process in the component domain, rather than the electrode domain (EEG), and localizes the generators through investigating the spatial correlation between the candidate components and the spike template, as well as the medical records of the patient. To evaluate the contribution of EEG-fMRI and concordance between fMRI and EEG, this method was applied on the data of 30 patients with refractory epilepsy. The results demonstrated the significant numbers of 29 and 24 for concordance and contribution, respectively, which mark improvement as compared to the existing literature. This study also shows that while conventional methods often fail to properly localize the epileptogenic zones in deep brain structures, the proposed method can be of particular use. For further evaluation, the concordance level between IED-related BOLD clusters and Seizure Onset Zone (SOZ) has been quantitatively investigated by measuring the distance between IED/SOZ locations and the BOLD clusters in all patients. The results showed the superiority of the proposed method in delineating the spike-generating network compared to conventional EEG-fMRI approaches. In all, the proposed method goes beyond the conventional methods by breaking the dependency on spikes and using the outside-the-scanner spike templates and the selected components, achieving an accuracy of 97%. Doing so, this method contributes to improving the yield of EEG-fMRI and creates a more realistic perception of the neural behavior of epileptic generators which is almost without precedent in the literature.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation
- Date Crossref
- 15/11/2021
- Éditeur
- Frontiers Media SA
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
University of Tehran pays non établi dans la noticeUniversité ou école supérieure
-
Institute for Research in Fundamental Sciences pays non établi dans la noticeStructure de recherche
-
George Mason University Neural Engineering Laboratory pays non établi dans la noticeUniversité ou école supérieure
-
The State University of New Jersey Rutgers pays non établi dans la noticeUniversité ou école supérieure
-
Empire State University pays non établi dans la noticeUniversité ou école supérieure
-
Henry Ford Health pays non établi dans la noticeOrganisation à but non lucratif
-
School of Electrical and Computer Engineering CIPCE pays non établi dans la noticeUniversité ou école supérieure
-
School of Cognitive Sciences pays non établi dans la noticeUniversité ou école supérieure
-
Rutgers University Behavioral and Neural Sciences Graduate Program pays non établi dans la noticeUniversité ou école supérieure
-
School of Graduate Studies pays non établi dans la noticeUniversité ou école supérieure
-
Image Analysis Laboratory pays non établi dans la noticeStructure de recherche
University of Tehran, Institute for Research in Fundamental Sciences et Neural Engineering Laboratory — George Mason University, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.