Nodal Fragility of Intracranial EEG Networks: Towards an EEG Fingerprint for the Epileptogenic Zone [article]

Adam Li, Zachary Fitzgerald, Iahn Cajigas, Damian Brusko, Jonathan Jagid, Angel Claudio, Andres Kanner, Jennifer Hopp, Stephanie Chen, Jennifer Haagensen, Emily Johnson, William Anderson (+6 others)
2019 bioRxiv   pre-print
ABSTRACTEpilepsy is a global epidemic and 30% of the 60 million patients do not respond to medication treatment. The only treatment options for patients with medically refractory epilepsy are surgical removal or electrical stimulation of the epileptogenic zone (EZ) i.e. the source of their seizures. Despite extensive evaluations with neuroimaging, visual EEG analysis and clinical testing, surgical success rates vary between 30-70%. Currently, no computational methods have been translated into
more » ... n translated into the clinic to assist in localizing the EZ. Here, we applied a dynamical network model that quantifies the fragility of nodes within a patient's intracranial EEG (iEEG) brain network. Fragility is quantified as the minimal amount of perturbation that must to be applied to a node's influence on a "balanced" network to cause imbalance. Here, a balanced network is one in which the connectivity between excitatory and inhibitory nodes render a stable system, and an imbalanced network is unstable and hence can generate seizures. Using iEEG data from 91 patients treated across 5 epilepsy centers (44 successes, 47 failures), we demonstrated that nodal fragility is greater in electrodes within the EZ. In addition, we compared fragility of iEEG nodes to 7 frequency-based and 14 graph theoretic features of the EZ in both seizure (n=91) and non-seizure data (n=54). We calculated a confidence statistic, defined as the ratio of the value of a given feature averaged across electrodes in the clinically annotated seizure onset zone to its average across all other electrodes. Fragility has a significantly greater effect size difference between surgical outcomes when compared to other features. This novel feature, outperformed the most popular iEEG features when comparing across surgical outcomes, possibly defining a superior network-based EEG fingerprint for the EZ.
doi:10.1101/862797 fatcat:tshp4pxudrhmtousstqtjhexry