Aim

Brain disorders cause human suffering and damage to society’s resilience and the economy.

To improve the accuracy, cost, and time horizon of predictions and treatments we need a better understanding of the impact of disease on the brain’s functioning.
Imaging methods like magnetoencephalography (MEG) have enabled the capture of brain activity at a high temporal resolution, leading to the description of transiently activating brain networks.
Multiple models have been used, e.g. based on hidden Markov modeling recurrent neural networks.
These models enable the description of brain activity as a sequence of transient brain networks, but they have limitations.

Improving our understanding of how the brain works at a fundamental level could lead to groundbreaking discoveries.

This project will focus on new ways of modeling the dynamics of functional connectivity within brains to gain insights into the spatio-temporal dynamics between brain regions.

Partners

The project is led by two groups: the VUB AI lab, experienced in developing new AI methods and the VUB AIMS lab, experienced in clinical neuroscience and in the application of AI methods.