Neural Fragility as an EEG Marker of the Seizure Onset Zone

Abstract

Epilepsy 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 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.1 Also reachable at adam2392 at gmail dot com. For more information, visit the NCSL website: https://sarmalab.icm.jhu.edu/.

Neural Fragility of the Intracranial EEG Network Decreases after Surgical Resection of the Epileptogenic Zone

“…Moreover, intraoperative data may refine and modify the resection plan in real-time, by capturing primarily interictal data to study electrophysiological changes within the irritative zone following resection. 36,37,38 To extend our work in 31 and, 32 we first provide some theoretical analysis of neural fragility to demonstrate that it is a well-defined metric assuming we have a good estimator for the linear system from data. Now that we have a well-defined metric, we hypothesize that neural fragility of iEEG data will modulate with respect to the successful surgical resection of the EZ.…”

“…Typically, approaches have tried to either i) build a prediction model for the clinically annotated epileptic channels, 24,64 ii) build a prediction model for the clinical annotations on only successful patients 65 and iii) building a prediction model conditioned on the clinical annotations that predicts surgical outcome. 32 Building a prediction model for the clinical annotations would not obtain a model of the EZ, since current outcomes vary between 30-70%. 21 Building the prediction model for only patients with successful surgical outcomes limits the amount of data one can use, but also is limited since not all the clinically annotated electrodes are necessarily epileptogenic.…”

“…Recently, it was proposed that neural fragility, a structured perturbation of a networked dynamical system estimated from iEEG ictal data has the potential to be a robust biomarker of the EZ. 31,32,33 This model is dynamic, based on a robust least-squares estimate 34,35 and models intrinsic stability properties of the system. That is, the propensity for seizures in our model occur as a property of eigenvalue perturbations.…”

White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures

“…matter contacts when creating iEEG montages to interpret, diagnose, and localize the seizure onset zone (SOZ). Research 303 studies [21][22][23][24][25] utilizing SEEG will typically exclude white matter 304 contacts. The exclusion of white matter contacts is perhaps 305 not unexpected given the fact that no validated studies exist 306 that demonstrate a rigorous approach to incorporating white 307 matter contacts.…”

In pursuit of the epileptogenic zone in focal epilepsy: A dynamical network biomarker approach

“…Examples include methods for analyzing functional connectivity via computation of linear or nonlinear measures of time-series correlations between brain regions or measures of causality for capturing directionality of interactions (Bartolomei et al, 2017). Graph theoretic approaches were also employed in attempts of identifying subnetworks comprising the EZ, often not without the challenge of finding clear interpretation of such measures in relevant neurophysiological terms (Bartolomei et al, 2017; Li et al, 2021). Network based statistics were also shown to provide promising features for training statistical learning algorithms that assign likelihood of individual regions belonging to the EZ (Li et al, 2018).…”