Explore statistical comparisons of spatio-temporal networks in this 49-minute conference talk by Sophie Achard at the Centre International de Rencontres Mathématiques in Marseille, France. Delve into brain network analysis, comparing healthy volunteers and patients using graph statistics. Learn about new methods for graph comparisons, including nodal statistics-based structural patterns and structural roles. Examine results from PC models with varying sparsity and orthogonality in simulated Watts-Strogatz and Barabási–Albert models. Discover applications in classification using orthogonality curves. Gain insights into the future of network analysis in neuroscience through this comprehensive presentation on machine learning and signal processing on graphs.
Statistical Comparisons of Spatio-Temporal Networks - Sophie Achard
Centre International de Rencontres Mathématiques via YouTube
Overview
Syllabus
Intro
The brain is both a structural and functional network
Exploring the brain using networks analysis: pipeline
Usual graph statistics
Comparisons of healthy volunteers and patients
Graph comparisons: other methods
Graph comparisons: new methods needed
Nodal statistics-based structural pattern on single graph
Nodal structural roles
Structural patterns for graph collections characterization
Results: PC for different sparsity graph models
Results of orthogonality on simulated WS and BA models
Illustrations of classification on orthogonality curves
Conclusion and future work
Taught by
Centre International de Rencontres Mathématiques
