title: Improving electroencephalographic source localization of epileptogenic zones with time-frequency analysis.
abstract: The combination of recently developed methods for electroencephalographic (EEG) space-time-frequency analysis can provide noninvasive functional neuroimages necessary for obtaining an accurate localization of the epileptogenic zone. The aim of this study was to determine if time-frequency (TF) analysis, followed by EEG source localization, would improve the detection and identification of epileptogenic and related activity. Seventeen patients with refractory frontal lobe epilepsy (FLE) were studied using video EEG recording. TF analysis identified the first epileptogenic EEG changes. Using the Bayesian model averaging (BMA) approach, we compared brain electromagnetic tomographic (BET) images, constructed from the TF domain, with BET images constructed from the time domain only. We determined if the localization identified by BET images was concordant with the localization from medical history and video EEG recording. TF analysis provided a clear display of subtle EEG features, including EEG lateralization, and more concordant and delimited epileptogenic zones, compared with time-domain source analysis. In conclusion, EEG TF analysis improves source localization. After a thorough validation, this methodology could become a useful noninvasive tool for localizing the epileptogenic zone in clinical practice.
authors.affiliation: Havana Institute of Neurology and Neurosurgery, Havana, Cuba cuspineda@yahoo.com.
authors.forename: Elena R
authors.initials: ER
authors.lastname: Cuspineda-Bravo
authors.affiliation: Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Martínez-Montes
authors.affiliation: Havana Institute of Neurology and Neurosurgery, Havana, Cuba.
authors.forename: Miguel
authors.initials: M
authors.lastname: Farach-Fumero
authors.affiliation: Havana Institute of Neurology and Neurosurgery, Havana, Cuba.
authors.forename: Calixto
authors.initials: C
authors.lastname: Machado-Curbelo
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis; D058067:Wavelet Analysis; D055815:Young Adult
keywords: Bayesian model averaging; EEG inverse problem; EEG time-frequency analysis; epileptic discharge sources; partial epilepsy