Graph Models and Brain Connectomics

This lecture covers the basics of graph theory applied to brain connectomics, including graph representations, neural networks at different scales, and graph partitioning. It explores the relevance of network analysis in understanding the brain's organizational principles and its applications in diagnosing brain disorders. The instructor discusses the use of MRI techniques to study brain regions, white matter orientation, and functional connectivity. The lecture also delves into graph measures, graph models like Erdös-Rényi and Watts-Strogatz, and graph partitioning algorithms. Additionally, it touches upon the importance of network modularity and the role of functional connectomes as individual fingerprints.