By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Explores neuroimaging basics, brain network scales, connectivity, history, and physics, emphasizing the importance of understanding data at different scales.
Explores brain network modules and community structure, including the natural modular functional connectome, network modularity, and community detection algorithms.
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.
Explores Graph Signal Processing applied to brain networks, emphasizing the relationship between brain function and structure using methods like Graph Fourier Transform and Structural-Decoupling Index.
Covers the caveats and summary of Simulation Neuroscience, emphasizing the importance of critical data and the three fundamental approaches to understanding the brain.
Discusses the definitions and assessment of consciousness levels through neuroimaging and brain networks, focusing on connICA for mapping functional connectome traits.
