Clinical benefit of presurgical EEG-fMRI in difficult-to-localize focal epilepsy : A single-institution retrospective review.

Objective The aim of this report is to present our clinical experience of electroencephalography–functional magnetic resonance imaging (EEG-fMRI) in localizing the epileptogenic focus, and to evaluate the clinical impact and challenges associated with the use of EEG-fMRI in pharmacoresistant focal epilepsy. Methods We identified EEG-fMRI studies (n = 118) in people with focal epilepsy performed at our center from 2003 to 2018. Participants were referred from our Comprehensive Epilepsy Program in an exploratory research effort to address often difficult clinical questions, due to complex and difficult-to-localize epilepsy. We assessed the success of each study, the clinical utility of the result, and when surgery was performed, the postoperative outcome. Results Overall, 50% of EEG-fMRI studies were successful, meaning that data were of good quality and interictal epileptiform discharges were recorded. With an altered recruitment strategy since 2012 with increased inclusion of patients who were inpatients for video-EEG monitoring, we found that this patients in this selected group were more likely to have epileptic discharges detected during EEG-fMRI (96% of inpatients vs 29% of outpatients, P<.0001). To date, 48% (57 of 118) of patients have undergone epilepsy surgery. In 10 cases (17% of the 59 successful studies) the EEG-fMRI result had a “critical impact” on the surgical decision. These patients were difficult to localize because of subtle abnormalities, apparently normal MRI, or extensive structural abnormalities. All 10 had a good seizure outcome at 1 year after surgery (mean follow-up 6.5 years). Significance EEG-fMRI results can assist identification of the epileptogenic focus in otherwise difficult-to-localize cases of pharmacoresistant focal epilepsy. Surgery determined largely by localization from the EEG-fMRI result can lead to good seizure outcomes. A limitation of this study is its retrospective design with nonconsecutive recruitment. Prospective clinical trials with well-defined inclusion criteria are needed to determine the overall benefit of EEG-fMRI for preoperative localization and postoperative outcome in focal epilepsy

Human GABRG2 generalized epilepsy increased somatosensory and striatothalamic connectivity

Objective: To map functional MRI (fMRI) connectivity within and between the somatosensory cortex, putamen, and ventral thalamus in individuals from a family with a GABAergic deficit segregating with febrile seizures and genetic generalized epilepsy. Methods: We studied 5 adults from a family with early-onset absence epilepsy and/or febrile seizures and a GABAA receptor subunit gamma2 pathogenic variant (GABRG2[R43Q]) vs 5 age-matched controls. We infer differences between participants with the GABRG2 pathogenic variant and controls in resting-state fMRI connectivity within and between the somatosensory cortex, putamen, and ventral thalamus. Results: We observed increased fMRI connectivity within the somatosensory cortex and between the putamen and ventral thalamus in all individuals with the GABRG2 pathogenic variant compared with controls. Post hoc analysis showed less pronounced changes in fMRI connectivity within and between the primary visual cortex and precuneus. Conclusions: Although our sample size was small, this preliminary study suggests that individuals with a GABRG2 pathogenic variant, raising risk of febrile seizures and generalized epilepsy, display underlying increased functional connectivity both within the somatosensory cortex and in striatothalamic networks. This human network model aligns with rodent research and should be further validated in larger cohorts, including other individuals with generalized epilepsy with and without known GABA pathogenic variants

Dynamic Functional Connectivity Captures Individuals’ Unique Brain Signatures

Recent neuroimaging evidence suggest that there exists a unique individual-specific functional connectivity (FC) pattern consistent across tasks. The objective of our study is to utilize FC patterns to identify an individual using a supervised machine learning approach. To this end, we use two previously published data sets that comprises resting-state and task-based fMRI responses. We use static FC measures as input to a linear classifier to evaluate its performance. We additionally extend this analysis to capture dynamic FC using two approaches: the common sliding window approach and the more recent phase synchrony-based measure. We found that the classification models using dynamic FC patterns as input outperform their static analysis counterpart by a significant margin for both data sets. Furthermore, sliding window-based analysis proved to capture more individual-specific brain connectivity patterns than phase synchrony measures for resting-state data while the reverse pattern was observed for the task-based data set. Upon investigating the effects of feature reduction, we found that feature elimination significantly improved results up to a point with near-perfect classification accuracy for the task-based data set while a gradual decrease in the accuracy was observed for resting-state data set. The implications of these findings are discussed. The results we have are promising and present a novel direction to investigate further.peerReviewe