Quantitative determination of concordance in localizing epileptic focus by component-based EEG-fMRI.


Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Aug 2019
Historique:
received: 27 12 2018
revised: 07 05 2019
accepted: 04 06 2019
entrez: 20 7 2019
pubmed: 20 7 2019
medline: 23 1 2020
Statut: ppublish

Résumé

Accurate seizure onset zone (SOZ) localization is an essential step in pre-surgical assessment of patients with refractory focal epilepsy. Complex pathophysiology of epileptic cerebral structures, seizure types and frequencies have not been considered as influential features for accurate identification of SOZ using EEG-fMRI. There is a crucial need to quantitatively measure concordance between presumed SOZ and IED-related BOLD response in different brain regions to improve SOZ delineation. A novel component-based EEG-fMRI approach is proposed to measure physical distance between BOLD clusters and selected component dipole location using patient-specific high resolution anatomical images. The method is applied on 18 patients with refractory focal epilepsy to localize epileptic focus and determine concordance quantitatively and compare between maximum BOLD cluster with identified component dipole. To measure concordance, distance from a voxel with maximal z-score of maximum BOLD to center of extracted component dipole is measured. BOLD clusters to spikes distances for concordant (<25 mm), partially concordant (25-50 mm), and discordant (>50 mm) groups were significantly different (p < 0.0001). The results showed full concordance in 17 IED types (17.85 ± 4.69 mm), partial concordance in 4 (36.47 ± 8.84 mm), and nodiscordance, which is a significant rise compared to the existing literature. The proposed method is premised on the cross-correlation between the spike template outside the scanner and the highly-ranked extracted components. It successfully surpasses the limitations of conventional EEG-fMRI studies which are largely dependent on inside-scanner spikes. More significantly, the proposed method improves localization accuracy to 97% which marks a dramatic rise compared to conventional works. This study demonstrated that BOLD changes were related to epileptic spikes in different brain regions in patients with refractory focal epilepsy. In a systematic quantitative approach, concordance levels based on the distance between center of maximum BOLD cluster and dipole were determined by component-based EEG-fMRI method. Therefore, component-based EEG-fMRI can be considered as a reliable predictor of SOZ in patients with focal epilepsy and included as part of clinical evaluation for patients with medically resistant epilepsy.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Accurate seizure onset zone (SOZ) localization is an essential step in pre-surgical assessment of patients with refractory focal epilepsy. Complex pathophysiology of epileptic cerebral structures, seizure types and frequencies have not been considered as influential features for accurate identification of SOZ using EEG-fMRI. There is a crucial need to quantitatively measure concordance between presumed SOZ and IED-related BOLD response in different brain regions to improve SOZ delineation.
METHODS METHODS
A novel component-based EEG-fMRI approach is proposed to measure physical distance between BOLD clusters and selected component dipole location using patient-specific high resolution anatomical images. The method is applied on 18 patients with refractory focal epilepsy to localize epileptic focus and determine concordance quantitatively and compare between maximum BOLD cluster with identified component dipole. To measure concordance, distance from a voxel with maximal z-score of maximum BOLD to center of extracted component dipole is measured.
RESULTS RESULTS
BOLD clusters to spikes distances for concordant (<25 mm), partially concordant (25-50 mm), and discordant (>50 mm) groups were significantly different (p < 0.0001). The results showed full concordance in 17 IED types (17.85 ± 4.69 mm), partial concordance in 4 (36.47 ± 8.84 mm), and nodiscordance, which is a significant rise compared to the existing literature. The proposed method is premised on the cross-correlation between the spike template outside the scanner and the highly-ranked extracted components. It successfully surpasses the limitations of conventional EEG-fMRI studies which are largely dependent on inside-scanner spikes. More significantly, the proposed method improves localization accuracy to 97% which marks a dramatic rise compared to conventional works.
CONCLUSIONS CONCLUSIONS
This study demonstrated that BOLD changes were related to epileptic spikes in different brain regions in patients with refractory focal epilepsy. In a systematic quantitative approach, concordance levels based on the distance between center of maximum BOLD cluster and dipole were determined by component-based EEG-fMRI method. Therefore, component-based EEG-fMRI can be considered as a reliable predictor of SOZ in patients with focal epilepsy and included as part of clinical evaluation for patients with medically resistant epilepsy.

Identifiants

pubmed: 31319952
pii: S0169-2607(18)31889-3
doi: 10.1016/j.cmpb.2019.06.003
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

231-241

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Elias Ebrahimzadeh (E)

CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada. Electronic address: e_ebrahimzadeh@ut.ac.ir.

Mohammad Shams (M)

Department of Biomedical Engineering, George Washington University, Washington D.C., USA.

Farahnaz Fayaz (F)

Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran.

Lila Rajabion (L)

Department of IT, College of Business, University of South Florida Sarasota-Manatee, USA.

Mahya Mirbagheri (M)

CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Babak Nadjar Araabi (B)

CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Hamid Soltanian-Zadeh (H)

CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Image Analysis Laboratory, Department of Radiology, Henry Ford Hospital, Detroit, MI, USA.

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Classifications MeSH