The neuroscience lab specializes in exploring brain functioning both in small animals as in humans. Multimodal PET, SPECT, EEG, fMRI and optogenetics are used to study dynamic brain functioning in the healthy and epileptic rat brain. In humans, mainly epilepsy is studied and the focus is on EEG data. For this, a close collaboration exists between the neuroscience lab and the neurology department in the University Hospital of Ghent. The neuroscience lab is expert in the field of EEG source imaging (ESI), with advanced forward modelling techniques. Next to this, expertise in brain connectivity analysis is present. Also the use of machine learning in this context is investigated.
- Combining optogenetics, chemogenetics, intracranial electroencephalography and functional magnetic resonance imaging to investigate abnormal functioning of brain networks during epileptogenesis
- Seizure onset zone localization from ictal high-density EEG
- Brain-Computer Interfaces
- Advanced forward models for EEG source reconstruction
Combining optogenetics, chemogenetics, intracranial electroencephalography and functional magnetic resonance imaging to investigate abnormal functioning of brain networks during epileptogenesis
Epilepsy is a disease characterized by recurrent seizures. It is difficult to predict which patients will respond to the different treatment options, because little is known about the development of epilepsy and the neural networks that might be involved. With functional magnetic resonance imaging (fMRI) whole-brain activity can be visualized, functionally connected brain regions can be identified and functional networks can be constructed. These networks can be characterized using graph theory measures. Optogenetics and chemogenetics are new techniques that allow very specific activation or inhibition of neurons, using light and specific drugs respectively. Optogenetics will be used to induce seizures during scanning, while chemogenetics will be used to try to modulate the epileptic network changes and to suppress spontaneous epileptic seizures.
|(adapted from: Harris, N. G., et al. “Disconnection and hyper-connectivity underlie reorganization after TBI: A rodent functional connectomic analysis.” Experimental neurology 277 (2016): 124-138.)|
Contact: Emma Christiaen
For patients with refractory epilepsy, surgery is the most efficient treatment option. To make this possible, an accurate delineation of the epileptic focus is necessary. In this research, we investigate how to localize this seizure onset zone based on high-density EEG recordings during a seizure. Therefore, we focus on the fact that there is increasing evidence that epilepsy is a network disease. During a seizure, several brain regions become simultaneously active in an epileptic network and it is hard to discern the main driver of this network. First, we identify the brain activity during a seizure using ESI. Next, we discriminate the epileptic network and try to find it’s driver using functional connectivity analysis.
A Brain-Computer Interface (BCI) is a system in which a computer is controlled directly through the use of brain signals, measured as EEG, MEG, fMRI etc. BCIs have a very broad field of application, ranging from computer games to biomedical applications. In this last domain, BCIs are used to increase the standard of living for disabled people. BCIs based on motor imagery enable motor impaired patients to move a wheelchair or an exoskeleton using their brain. Those based on event-related potentials are especially used as communication systems for patiens with the locked-in syndrome (e.g. ALS patients). Extensive research is needed to find accurate methods that extract as much information as possible from the user’s brain waves and transform this information to the right control signals. The final goal of this research is to build accurate, cheap and user-friendly BCIs.
|Figure 1: Example of a BCI application|
Contact: Thibault Verhoeven
EEG source reconstruction techniques try to reconstruct the generating sources of electrical activity measured on the scalp. In order to do so, a forward model is required. Such a model is characterized by a volume conductor model of the head and a corresponding source space. The volume conductor model reflects the geometrical and electromagnetic properties of the head while the source space constricts the solution space in order to be able to reconstruct the generating sources. We are developing techniques to improve the accuracy of the reconstructed activity using MRI based subject specific finite difference forward models.
|Figure 1: Flow chart to do EEG source reconstruction using MRI based forward models||Figure 2: Advanced subject specific volume conductor model including, gray and white matter, CSF, skull and scalp|