Localization of Seizure Onset Zone based on Spatio-Temporal Independent Component Analysis on fMRI

Localization of the seizure onset zone (SOZ) as a step of presurgical planning leads to higher efficiency in surgical and stimulation treatments. However, the clinical localization including structural, ictal, and invasive data acquisition and assessment is a difficult and long procedure with increasing challenges in patients with complex epileptic foci. The interictal methods are proposed to assist in presurgical planning with simpler data acquisition and higher speed. In this study, spatio-temporal component classification is presented for the localization of epileptic foci using resting-state functional magnetic resonance imaging (rs-fMRI) data. This method is based on spatio-temporal independent component analysis (ST-ICA) on rs-fMRI with a component-sorting procedure upon dominant power frequency, biophysical constraints, spatial lateralization, local connectivity, temporal energy, and functional non-Gaussianity. This method aimed to utilize the rs-fMRI potential to reach a high spatial accuracy in localizing epileptic foci from interictal data while retaining the reliability of results for clinical usage. Thirteen patients with temporal lobe epilepsy (TLE) who underwent surgical resection and had seizure-free surgical outcomes after a 12-month follow-up were included in this study. All patients had pre-surgical structural MRI and rs-fMRI while post-surgical MRI images were available for ten. Based on the relationship between the localized foci and resection, the results were classified into three groups “fully concordant”, “partially concordant”, and “discordant”. These groups had the resulting cluster aligned with, in the same lobe with, and outside the lobe of the resection area, respectively. This method showed promising results highlighting valuable features as SOZ functional biomarkers. Contrary to most methods which depend on simultaneous EEG information, the occurrence of epileptic spikes, and the depth of the epileptic foci, the presented method is entirely based on fMRI data making it independent from such information and considerably easier in terms of data acquisition, artifact removal, and implement.