EEG synchronization measures predict epilepsy-related BOLD-fMRI fluctuations better than commonly used univariate metrics. Issue 3 (March 2018)

• Record Type: Journal Article
• Authors: Abreu, Rodolfo; Leal, Alberto; Lopes da Silva, Fernando; Figueiredo, Patrícia
• Is Part Of: Clinical neurophysiology. Volume 129:Issue 3(2018:Mar.)
• Page Start: 618
• Page End: 635
• Publication Date: 2018-03
• Subjects: Simultaneous EEG-fMRI -- Epilepsy -- EEG synchronization -- BOLD; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2017.12.038
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: EEG synchronization measures were used to predict epilepsy-related BOLD fluctuations. Phase synchronization index (PSI) yielded the most reliable epileptic networks. First study in which synchronization measures were used in simultaneous EEG-fMRI epilepsy studies. Abstract: Objective: We hypothesize that the hypersynchronization associated with epileptic activity is best described by EEG synchronization measures, and propose to use these as predictors of epilepsy-related BOLD fluctuations. Methods: We computed the phase synchronization index (PSI) and global field synchronization (GFS), within two frequency bands, a broadband (1–45 Hz) and a narrower band focused on the presence of epileptic activity (3–10 Hz). The associated epileptic networks were compared with those obtained using conventional unitary regressors and two power-weighted metrics (total power and root mean square frequency), on nine simultaneous EEG-fMRI datasets from four epilepsy patients, exhibiting inter-ictal epileptiform discharges (IEDs). Results: The average PSI within 3–10 Hz achieved the best performance across several measures reflecting reliability in all datasets. The results were cross-validated through electrical source imaging of the IEDs. The applicability of PSI when no IEDs are recorded on the EEG was evaluated on three additional patients, yielding partially plausible networks in all cases. Conclusions: Epileptic networks can be mapped based on the EEG PSI metric within an IED-specific frequency band, performing better than commonly used EEG metrics. Significance: This is the first study to investigate EEG synchronization measures as potential predictors of epilepsy-related BOLD fluctuations.