Identifying Functional Brain Networks with Conditional Independence Graphs

Explore the fundamentals of constructing functional brain networks from neuroimaging data in this comprehensive lecture by Victor Solo, PhD. Delve into the limitations of correlation-based approaches and discover how partial correlation and conditional independence graphs offer improved solutions. Learn about the application of sparsity methods for scaling to larger networks and examine the extension to autocorrelated signals using frequency domain-based techniques and state space models. Gain insights into the practical application of these concepts through real-data examples, all presented with minimal mathematical complexity. This talk is ideal for researchers and students in neuroscience, biomedical imaging, and related fields seeking to enhance their understanding of functional brain network analysis.