Neu3CA is a multidisciplinary brain research program involving academic and commercial partners from both the Netherlands and Belgium. We combine technical and clinical expertise to get the most our of our research, and carry out joint projects around the theme neurocognition (more specifically neurocognitive decline). Higher brain functions such as cognition are compromised in a range of (neurodegenerative) brain disorders, including epilepsy, depression and stroke. Our research involves brain MRI, advanced simulations of brain networks, and neuromodulation.
We aim to learn more about neurocognition by studying epilepsy, the most common neurological disease, which affects 1-2% of the population. Epilepsy is characterized by epileptic seizures, but in more than half the patients also causes cognitive impairments, a.o. accelerated cognitive aging. We study the associated changes in brain organization to develop neuromodulation protocols for treatment. Also other brain conditions which affect cognition, e.g. depression and stroke, are within our scope.
Our research is typically based on MRI of the brain (anatomical/functional/diffusion weighted/spectroscopy), but may also involve EEG or MEG. We have a close collaboration between technical and clinical partners, to keep our research as relevant and aligned with clinical needs as possible. This dual approach (technical/clinical) is apparent at all levels of our team (staff, PhD candidates, students).
Neurofeedback is the concept of making someone aware of his own neuronal activity. The idea is that by making someone aware of his mental state, he may learn how to control or modify this. fMRI neurofeedback typically involves monitoring the activity in a certain region-of-interest. Within the Neu3CA program, we are developing fMRI neurofeedback for (complete) neuronal networks, with the aim of treating cognitive impairments.
EEG (elecotro-encephalograpy) provides a direct window on neuronal activity: it captures the rapid changes in electical fields generated by neuronal firing. In this project, we combine EEG data with data-mining techniques and graph theory to find EEG-markers of neurological diseases.