Connectivity-Informed Brain Activity Interpretation

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Related lectures (31)

Page 1 of 4

Introduces the basics of brain connectomics, including terminology, data preprocessing, functional MRI, connectivity measures, and modular structure.

Brain Connectomics: Basics of Neuroimaging

Covers the basics of brain connectomics, including brain networks, terminology, data schemes, preprocessing, node connectivity, and functional connectome structure.

Graph Models and Brain Connectomics

Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Introduction to Networks and Brain Networks

Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.

Graph Theory in Neural Signal Processing

Explores neural signal processing through graph theory, multivariate methods, and fMRI analysis.

Neural Signals and Connectomes

Explores neural signals, connectomes, graph theory, and multi-voxel pattern analysis in fMRI trials.

Neuronal Connectivity Patterns

Explores neuronal connectivity patterns, connection probabilities, and experimental techniques used to study synaptic connectivity.

Sparsest Cut: ARV Theorem

Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.

Statistical analysis of network data

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Belief Propagation

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.