Module 09: From Neurons to Networks: Basics of Graph Theory in Connectomics

Learn how to represent the brain as a graph and analyze its structure using basic network theory.

🔎 Representing the Brain as a Network

Connectomes can be interpreted as graphs, where neurons are nodes and synapses are edges. This abstraction allows us to apply network science to analyze brain structure and function.

  • Adjacency matrices and edge lists
  • Directed vs. undirected graphs
  • Weighted and unweighted connections

📊 Key Network Metrics

Quantifying the structure of neural graphs helps us uncover patterns of connectivity and information flow.

  • Node degree and degree distribution
  • Betweenness and closeness centrality
  • Clustering coefficient and small-worldness

🎭 Visualizing Neural Graphs

Visual representations help us identify motifs and unusual structures in large connectomic graphs.

  • Force-directed layouts and 3D viewers
  • Highlighting hubs and motifs
  • Interactive notebooks and Gephi tools

🎯 COMPASS Integration

  • Knowledge: Graph theory concepts applied to neuroscience
  • Skills: Code-based analysis, visualization, network interpretation
  • Character: Curiosity, precision, perseverance
  • Meta-Learning: Transfer of graph concepts to new datasets

📚 References & Resources

✅ Assessment

  • Create a basic graph representation of a neuron-synapse dataset
  • Calculate centrality metrics and explain their meaning
  • Visualize a simple connectome using network plotting tools