Network Neuroscience 2401-CS-22-NN-s2

Network neuroscience is a novel approach to understanding the structure of the brain using tools from network science. The brian connectome (a comprehensive map of neural connections in the brain) can be studied across scales of organization: from molecules and neurons to circuits and systems. During the course students will gain knowledge about the fundamentals of network neuroscience, graph theory and basic features of brain network organization. Students will also learn how to estimate connectivity based on neuroimaging data, apply various network neuroscience tools to neuroimaging data.
Specifically, the course and practical tutorials will cover the following topics:

1. Graphs as models of complex systems: introduction to graph theory, concepts of nodes and edges;
2. Imaging brain connectome across scales: microscale, mesoscale and macroscale;
3. Economy of brain network organization: small-world network, hubs, and modularity;
4. Neuroimaging of the human connectome: imaging and denoising techniques, connectivity estimation;
5. Brain parcellations: different approaches of reducing dimensionality of neuroimaging data;
6. Connectivity matrices: directionality, thresholding, and binarization;
7. Connectome visualization: comparing different tools and approaches;
8. Basic measures of node connectivity: node strength, node degree, degree distributions;
9. Detecting brain hubs: centrality measures, methods of identification of hubs;
10. Network modularity: community detection algorithms, multilayer community detection.
11. Applications of network neuroscience: understanding the brain in health and disease.

All tutorials will be provided in Python programming language.