Ongoing Projects

Seizure Networks

Sometimes the clinical exploration of epilepsy necessitates localizing seizures with direct brain recordings, called i-EEG. In partnership with the Sleep-Wake-Epilepsy Center at Inselspital, we analyze these recordings to better understand seizure networks. We rely on advanced signal processing and cutting-edge machine-learning approaches to accelerate our understanding of complex signals.

Signaling Dynamics within the Human Brain

Amid an intricate tangle of nerve fibers, how can one discern the orderly directed signals that underpin communication within the human brain? We developed novel methods to map actual signal transmission across brain regions with millimeter and millisecond precision. We rely on probing electrical stimulations delivered intracortically and find evidence for signal transmission from cortical response in any other nearby or distant brain region. Our approach offers the fundamental advantage of causality and an alternative to passive brain recordings with fMRI or EEG which cannot measure directionality with certainty. We believe that the directness of our methods bring us one step closer to the holy grail of the human connectome.

See video.

Neuroscience – Chronobiology of Epilepsy

We launched the project Chronobiology of Epilepsy, a series of rodent experiments, to accelerate our mechanistic understanding of cycles in epilepsy. Based on clinical data, Maxime Baud characterized the phenomenon of multidien cycles in epilepsy in a Nature communications paper. Why does epilepsy occur is unclear in most instances. But once epilepsy has started, its defining feature is the recurrence of spontaneous seizures. Not at any moment though, but during periods of high seizure risk. So, why do seizure occur when they do ? What are the environmental influence? What systemic or brain state are favorable to seizure occurrence ? To answer some of these questions we have partnered up with Antoine Adamantidis, an expert in optogenetics.

Neuro-engineering – Forecasting seizures

In 2020, we showed the feasibility of forecasting seizure risk over days. This breakthrough was achieved using sophisticated statistical models called point-process generalized linear models, that can take into account a number of temporal features upon which the momentary risk of seizures may depend. One major variable influencing changes in seizure risk day after day are underlying multidien cycles of epileptic brain activity that can be measured with chronic EEG. This study opened the way to providing personalized probabilistic seizure forecasts, akin to weather forecasts, as explained in the video.

Seizure Cycles

Starting in 2017, we made significant contributions to characterizing ‘seizure cycles’, the fact that epileptic seizures tend to recur with a certain periodicity in people with epilepsy. Using years-long EEG recordings from over 200 patients implanted with an intracranial EEG device, we found that seizures occur at preferential times (a circadian modulation) and with quasi-periodic seizure-free intervals, a phenomenon for which we coined the term ‘multidien cycles’ (multi-day) of epileptic activity. We discovered that seizures recur during so-called ‘pro-ictal’ states, when the epileptic brain generates abnormally high numbers of epileptic discharges. In fact, within the same person, seizure risk may fluctuate concomitantly at different timescales, over hours within a day, over days within a week or a month and over weeks within a year.

Seizure Dynamics

To characterize the dynamics of hippocampal circuits, we developed a model of optogenetics seizures on demand. By stimulating entorhinal neurons that project to the hippocampus, we can probe excitability and trigger seizures. These manipulations coupled with high-density recordings enable the study of neuronal dynamics underpinning the initiation of seizures.

Neuroengineering – EpiOs Project

The Wyss Center for neuroengineering is developing a minimally invasive device to enable continuous recording of brain activity while people are at home, work or school. Electrodes placed under the skin of the scalp will monitor the electrical activity of the brain. A small implanted unit will collect readings from the electrodes and communicate wirelessly with a portable device that uploads brain data to remote storage in the cloud. This engineering project is lead by George Kouvas, Dipl. -Ing., MBA. The device is currently in the design and development phase. The first clinical trials are anticipated to take place by 2020 at Bern University Hospital.