Clinical MEG Service | Research & Innovation

Using non-invasive techniques like MEG and scalp electroencephalograms (EEG), our research team members are developing novel computer-aided methods to estimate the location and extent of the area responsible for generating seizures (also called epileptogenic zone) in the brain of children with drug-resistant epilepsy. Our principal lines of research are the following:

  • Development of new analytical methods to optimize the localization of interictal epileptiform discharges on MEG and scalp EEG. A large variety of methods exist to estimate the brain generators of the epileptiform activity that we observe on MEG or scalp EEG. Therefore it is important to evaluate which are the existing methods showing the highest clinical value for epilepsy surgery and to develop new approaches to further optimize them in terms of their precision to localize the epileptogenic focus.
  • Understanding how different regions in the epileptic brain interact with each other (functional connectivity analysis). Since epilepsy is increasingly conceptualized as a brain network disorder, rather than the dysfunction a single isolated region, understanding how different brain regions interact with each other is extremely important; this can allow us to unravel when there are alterations in such interactions and to investigate whether such alterations play a crucial role in triggering dynamic epileptic activity. Functional connectivity analysis allows us to estimate the functional connections between brain regions regardless of their structural connections, and it may provide insights into the ability of each brain region to generate seizures as well as into their propagation mechanisms. We also correlate functional connectivity results with structural connectivity results as calculated from the diffusion-weighted imaging scans of epilepsy patients.
  • Taking the most from “silent” EEG or MEG data. When we record MEG or scalp EEG data from patients with epilepsy, it is possible that we do not capture any epileptiform activity or seizure. We can refer to this data as “silent” as they are not able to tell us anything about underlying epileptogenicity based on our current knowledge and practice. We are then investigating advanced signal analysis techniques that are able to assess “invisible” characteristics of the signals and exploring whether they are informative of underlying epileptogenicity.
  • Localizing very fast activity (high-frequency oscillations or HFOs) that is generated within the brain of epilepsy patients in between seizures. This type of epileptiform activity is typically recorded using invasive techniques such as electrocorticography or stereo encephalography and has been demonstrated to help localize the epileptogenic brain tissue even in absence of actual seizures. The possibility to record this fast activity using non-invasive techniques would be highly beneficial as it would complement the current clinical practice of using MEG and scalp EEG, which is primarily dedicated to identifying more traditional epilepsy biomarkers, such as spikes.
  • Working with our outside collaborators to test and develop user-friendly software platforms for streamlining the acquisition and analysis of epilepsy data. With these tools we hope to help implement real-time solutions, which could dramatically improve the speed of epileptic zone localization within the brain and thus improve patient experience.