QLS Seminar Series - Sridevi Sarma

Network Biomarkers for Epilepsy Diagnosis and Treatment: combining brain imaging data with systems modeling

Sridevi Sarma, Johns Hopkins University
Tuesday March 26, 12-1pm
Zoom Link: https://mcgill.zoom.us/j/86855481591
In Person: 550 Sherbrooke, Room 189

Abstract: Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 60 million people worldwide, with a burden comparable to breast and lung cancer. First-line treatments include anti-epileptic drugs (AEDs), but if ineffective, options like surgical resection or brain stimulation are considered. Despite available treatments, accurate diagnosis and effective treatment can take years, leading to stigma, side effects, and costly hospital stays. The absence of reliable biomarkers complicates management. This talk explores leveraging brain imaging data and dynamic network modeling to identify reliable epileptogenic biomarkers.

Technical abstract: Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the EZ exist. Localizing the EZ is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients.

IEEG offers a unique opportunity to observe epileptic cortical network dynamics, and in this talk we will identify three novel iEEG markers of the EZ using (i) seizure data, (iii) non seizure data, and (iii) single pulse electrical stimulation response iEEG data. Specifically, patient-specific dynamical network models (DNMs) are estimated from the iEEG data and their connectivity properties will highlight the EZ under each condition. For seizure data, stability analysis of the DNMs will highlight “fragile” nodes or regions pointing to the EZ. For non-seizure data, network inhibition of the EZ, quantified by DNMs, will point to the EZ. Finally, for stimulation response data, neural resonance gleaned from the DNMs will highlight the EZ. These three studies demonstrate how network dynamics in epileptic brain networks is key to identifying pathological regions.