Explores centrality, hubs, eigenvectors, clustering coefficients, small-world networks, network failures, and percolation theory in brain networks.
Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.
Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.
Covers the basics of networks, focusing on brain networks, historical breakthroughs, small-world and scale-free network discoveries, and the importance of the human connectome.
Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.
Explores brain node connectivity, node degree, strength, random networks, power law distributions, and the complexity of real networks.
Introduces the basics of brain connectomics, including terminology, data preprocessing, functional MRI, connectivity measures, and modular structure.
Covers bond percolation on a square lattice, discussing percolation phases, critical threshold, mean cluster size, and critical point scenarios.
Explores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.
