Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI

The general linear model (GLM) has been used to analyze simultaneous EEG–fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG–fMRI data in which abnormalities are not apparent on scalp EEG

Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy. A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities. The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions. In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas