Abstracts

Rationale: Epilepsy is thought to be a network disease in which many different brain areas and regions are involved. Electromagnetic source imaging (ESI) is particularly useful in determining the location and dynamics of underlying ictal nodes. Our proposed source imaging algorithm, namely the FAST-IRES, is capable of estimating the location, extent and dynamics of seizure networks. This is particularly important in determining the epileptogenic zone (EZ); determining the EZ is critical in planning for epilepsy treatment specifically for surgical resection of the EZ or for placing electrodes for neurostimulation therapies. Methods: We have performed computer simulations by placing multiple extended sources on a real geometry cortex that evolved over time with a realistic time-course of activity, and then solved the forward problem to simulate EEG recordings (different signal to noise ratio were considered such as 5, 10 and 20 dB). Solving the inverse problem using FAST-IRES we estimated the location, extent and the time-course of activity for all these sources and compared our estimates to simulated sources. Additionally we applied this method to the ictal recordings of 10 focal epilepsy patients (who became seizure-free after surgery) and compared our estimated EZ with clinical findings such as the surgical resection volume in these patients. Results: The estimated results by FAST-IRES matches well with the location, extent and time-course activity of simulated sources. Our initial results suggest a localization error of about 3.5 mm (distance between the simulated and estimated source). The correlation between the time-course of activity of the estimated and simulated sources, is over 96%. A precise estimation of underlying sources’ time-courses is an important feature for calculating functional connectivity. In our clinical data analysis we showed that our estimated EZ overlapped with the ground truth, surgical volume, over 90%. Additionally, the average distance of our estimated solutions to SOZ electrodes over all SOZ electrodes and patients is less than 2.7 mm. This indicates that our approach can estimate the underlying EZ without applying subjective thresholds to ESI solutions. Ictal activities can propagate very fast and spread to a larger cortical regions than the seizure onset zone (SOZ), but looking at the dominant ictal frequency band and the region of maximal energy deposition in cortical tissue, we successfully estimated EZ from EEG recordings, noninvasively. Conclusions: We have proposed a new source imaging algorithm that solves the ESI problem efficiently to provide a spatio-temporal source distribution that effectively determines the epileptogenic tissue in focal epilepsy patients. FAST-IRES can estimate underlying sources’ time-course of activity precisely which is important in determining functional connectivity among underlying sources, which can be used to determine the ictal onset zone objectively. Our initial results from analyzing 10 focal epilepsy patients confirms the aforementioned conclusions. Funding: This work was supported in part by NIH R01 NS096761 and EB021027. AS was supported in part by a Doctoral Dissertation Fellowship from the Graduate School of University of Minnesota.