Title: Network models and machine learning for neuroimaging data
Speaker: Paola Galdi
Abstract:

Brain networks or connectomes are commonly used abstractions to model brain imaging data. Network nodes usually represent spatially contiguous regions of the brain, while edges model relationships between brain regions, that might reflect underlying anatomy (e.g., bundles of nerve fibres connecting neurons) or more complex interactions, such as correlated brain activity over time. In this talk, I will present four different brain network models derived from magnetic resonance imaging: functional connectivity networks, tractography-based connectomes, morphometric similarity networks and cortical meshes. I will then discuss a few examples of applications where features derived from brain networks are used to train predictive models and characterise clinical populations.