Friday, November 22, 2024
11/22/2024
Epilepsy is a devastating disease affecting over 50 million people worldwide (WHO). About 30% of patients do not respond positively to medication and are diagnosed as having drug resistant epilepsy (DRE). DRE causes significant costs, morbidity, and mortality. The most effective treatment is to surgically remove the seizure onset zone (SOZ), the region from which seizure activity is triggered. The localization of the SOZ is essential for surgical success. Unfortunately, surgical success rates range from 30%-70% because there is no reliable biomarker of the SOZ. We propose to develop a combined intracranial EEG-fMRI biomarker of the SOZ while the patient is not seizing or at “rest”. One may ask, “how does one identify where seizures start in the brain without ever observing a seizure, and if this is possible why have previous methods failed?” The fundamental limitation of current computational approaches for both resting state fMRI (rs-fMRI) and intracranial EEG (rsiEEG) SOZ localization lies in the fact that they compute static measures from observations produced by a dynamic epileptic network. We believe that a computational method that can provide a characterization of how the observations are dynamically generated in the first place, and how internal network properties can trigger seizures or prevent seizures will be successful in SOZ localization. Therefore, we will construct dynamical network models (DNMs) in this study. DNMs are generative models that capture how every network node (location of centralized network signal processing and transfer) interacts with every other node dynamically. DNMs uncover internal properties including bandwidth, stability, controllability, system gain, and most important to this application - connectivity. We propose that when a patient is not having a seizure, it is because the SOZ is being inhibited by neighboring nodes (brain regions). We thus will apply DNM algorithms in a novel manner to identify two groups of network nodes from rs-fMRI and rs-iEEG: those that are continuously inhibiting a set of their neighboring nodes (denoted as “sources”) and the inhibited nodes themselves (denoted as “sinks”). Thus, in line with the most recent advancement in precision medicine, for each patient, we will build DNMs customized to identify and quantify, via a score, key sources and sinks, optimized to localize the primary causative SOZ nodes in the epileptogenic network and their connectivity properties. We will leverage functional imaging data while patients are “at rest” in a study population of children with DRE who are undergoing epilepsy surgery evaluation. Specifically, we will construct DNMs from rs-fMRI and rs-iEEG data and test our novel “source-sink” hypothesis that may point to the SOZ when patients are not seizing. If successful, the proposed DNMs could significantly increase surgical candidacy and improve surgical outcomes by increasing the yield of surgically actionable results and precision of SOZ localization. Furthermore, by removing the need to capture seizures, this novel dynamic network model-based SOZ localization biomarker may substantially reduce invasive monitoring times, avoiding further risks to patients and reducing costs to hospitals.
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