Project Summary
For children with drug-resistant epilepsy (DRE), epilepsy surgery is the best treatment to stop seizures and
prevent a life of disability. Crucial to the success of surgery is the ability to identify the area of the brain that is
responsible for generating seizures, called epileptogenic zone (EZ). The best way to estimate the EZ is by
recording the brain activity invasively via intracranial electroencephalography (icEEG), aiming to capture
seizures and locate the area that generates them. Yet, one of three patients continue to have seizures after
surgery. This suggests that there is still an unmet need for new methods that go beyond traditional icEEG
interpretation and offer novel information on underlying epileptogenicity in patients undergoing epilepsy surgery
evaluation. To address this need, we propose a novel approach to analyze icEEG that takes advantage of new
“invisible” signal characteristics, which can inform us on epileptogenicity, albeit not visible to the human reader.
Epileptogenicity is a very complex brain property that depends on the interplay between altered excitability and
connectivity. Recent evidence suggests that, to treat focal DRE, we must localize pathological regions (depicted
by altered excitability) and also appreciate how they interact within the epileptogenic network (identifying altered
connections). In this application, we propose to develop a novel twofold approach to optimize the interpretation
of icEEG, which quantifies and integrates both local brain excitability (via phase-amplitude coupling, PAC) and
functional connectivity (FC), using “silent” icEEG epochs (i.e. without frank epileptiform patterns), in order to
define novel measures of “interconnected-excitability” (which we will call Network-PAC). Our main goal is to
develop a new computer-aided approach to boost icEEG reading and improve surgical planning in children with
DRE, without requiring the recording of seizures or even the identification of frank interictal epileptiform activity.
We hypothesize that the EZ is characterized not only by a high ‘local excitability level’ (strong PAC) but also by
strong connections with other ‘excitable’ tissue, thus generating a hyper-excitable network that is responsible for
generating seizures. We will pursue two specific aims: (1) Identify regions of high inter-connected excitability and
assess their ability to define the seizure onset zone (SOZ); (2) Develop a predictive model that integrates patient-
specific icEEG information about both local PAC and functional networks (independently from the presence of
frank epileptiform patterns) to predict surgical outcome following a resection. This application will combine the
use of cutting-edge electrophysiological and signal processing concepts (cross-frequency coupling, connectivity,
and graph theory) together with extensive neuroimaging and clinical experience with children. Our research will
present to the epilepsy community a new approach to estimate the EZ before epilepsy surgery, which will go
beyond the visual identification of seizures or spikes on the EEG. This can significantly impact the clinical care
of children with DRE in the long-term, by boosting the pre-surgical interpretation of icEEG and reducing the need
for extended invasive monitoring - which is often needed to capture spontaneous seizures.
