Epilepsy is a neurological disease affecting 70 million people worldwide. For most individuals, these seizures can be controlled using medications, however nearly 1 in 3 people may need surgery to achieve seizure freedom. For this surgery to be successful, the brain region generating the seizures, which contains the critical seizure onset zone (SOZ), must be accurately identified and removed. Unfortunately, the surgical success rate is low likely due to imprecise determination of the SOZ. As a novel approach to SOZ identification, the collection of intracranial electroencephalography and functional magnetic resonance imaging (iEEG-fMRI) has been proposed as a novel method of identifying the SOZ. However, iEEG-fMRI faces the methodological challenge of artifact introduced from MR scanning which completely obscures the physiological EEG signal. Therefore, the first step towards bringing iEEG-fMRI into the clinical realm is to improve methods for extracting the physiological EEG signal from the iEEG-fMRI data. To this end, the first study in this thesis validated a set of methods aimed at removing fMRI artifact from iEEG, culminating in the creation of the first automatic iEEG pre-processing pipeline. The next step towards clinical utility for iEEG-fMRI is improving our interpretation of iEEG-fMRI results. Traditionally, only positive IED related fMRI activation maps were considered in relation to SOZ localization, and the negative response was ignored. It has been suggested that both positive and negative activation maps should be considered, and the maximal cluster of these two maps, regardless of polarity, should be used to localize the SOZ. In the second study, the concept was tested using iEEG-fMRI and it was found that the use of the maximal negative cluster had limited utility for SOZ localization. The results of this thesis provide a new method for preparing EEG data from iEEG-fMRI experiments and it shows that the bulk of maximal negative fMRI clusters have limited reliability for clinical applications
