Title: Brain Health Trajectories: Toward predictive personalised medicine using realistic whole brain modeling

Abstract:

As life expectancy continues to rise across the world, understanding and being able to predict individual brain health during aging becomes a paramount challenge for the society. The goal of my project is early stratification of neurodegenerative disorders (NDD), based on personalized brain aging trajectories. To achieve this, I propose developing and validating a set of models, tools and digital technologies, that will help in explaining, and quantifying brain health trajectories in individuals.
Brain function, young or old, healthy or with disorders, must be reflected in brain dynamics. These are recorded at different spatial and temporal scales and by heterogeneous neuroimaging modalities. The main premise of my project is that despite the enormously high-dimensionality of neuronal activity, the behaviour encoded in the dynamics, including the one related to NDD, is of a much lower-dimensionality. This is mathematically a manifold, which can be sampled from the recordings, and linked to the biophysical mechanism using computational models. I will work on building models, metrics and data inference technology that will together allow to estimate parameters of the manifold that are indicative to a pathology at a very early stage, when it is still not expressed itself. To achieve this, I will utilize a causal model that links individualized brain recordings with the structural features of the brain beyond the connectome (e.g., neurotransmitters and amyloid β (Aβ) plaques and tau deposits). With this, I will describe the ability of the brain to act in a certain way, as a function of its underlying structure. Having established causality of underlying structural features, represented by biophysical quantities, prospects for predictive personalized medicine with preventive and therapeutic strategies will follow.