Abstracts

Rationale: A novel software algorithm combining non-invasive EEG and rsfMRI data to map resting state functional networks related to the epileptogenic zone was previously reported and was used in the current study. Network-level alterations in patients with epilepsy before and after surgery have been found in previous studies using concurrently acquired EEG and resting state functional MRI (rsfMRI). The relationship between epilepsy network connectivity and seizure freedom after surgery was investigated using a non-invasive and non-concurrent modeling algorithm. Methods: Scalp EEG and rsfMRI were acquired for fourteen patients undergoing surgery for mesial temporal lobe epilepsy (mTLE). The hypothetical epileptogenic zone was localized using a forward cortical dipole computation model and inverse Bayesian reconstruction to localize epileptic discharges. rsfMRI data was used to map regions functionally correlated with the epileptogenic zone, which represented the putative epilepsy network. Network connectivity was measured pre- and post-operatively. To compare to healthy control patients, network connectivity was also measured in age matched control patients. Results: Epilepsy network connectivity was significantly higher in the patients with mTLE compared to the healthy control patients, suggesting the existence of a pathologic, hyper-connected network related to that patient’s epilepsy. In this cohort, nine patients were seizure-free (SF) and five patients were not seizure-free (N-SF) after surgery. In the SF patients, epilepsy network interconnectivity was significantly reduced after surgery compared to the pre-operative state. Conversely, patients with seizure recurrence after surgery did not have a significant reduction in epilepsy network connectivity. Conclusions: A novel method for modeling networks was applied to surgical patients with mTLE, and preliminary data shows a significant network disconnection in patients who were SF after surgery, and not in patients with seizure recurrence. This finding is significant because it suggests a possible underlying mechanism why some patients respond to surgery while others do not despite similar pre-operative assessment and surgical technique. Importantly, these findings were generated using only scalp EEG and rsfMRI, data available to any comprehensive epilepsy center. Future studies aim to identify the patients with epilepsy networks that may be more readily disconnected by surgery pre-operatively. Funding: Neurosurgery Research and Education Foundation 2017 Medical Student Summer Research Fellowship