Neural Signals and Connectomes

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Related lectures (29)

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Graph Theory in Neural Signal Processing

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

Graph Models and Brain Connectomics

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

Fundamentals of Brain Connectomics

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.

Introduction to Networks and Brain Networks

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

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Statistical analysis of network data

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

Connectivity-Informed Brain Activity Interpretation

Explores integrating brain connectivity to decode and interpret brain activity using graph signal processing and spectral residual networks.