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Meta-dynamic modelling of functional brain networks (MEMOBRAIN) (FWOAL1118)

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.
I mproving our understanding of how the brain works at a fundamental level could lead to
groundbreaking discoveries. To improve our understanding of how the brain’s function changes in
disease we submit an interdisciplinary proposal with two PhDs, one from neuroscience and the other
from computer science/AI, to work together. 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. 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
Date:1 Jan 2024 →  Today
Keywords:hierarchical modeling, multiple sclerosis, biological and artificial cognition
Disciplines:Computational biomodelling and machine learning, Cognitive neuroscience, Bio-informatics, Symbolic computing