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Updated: Jun 6, 2026
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Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
Brain graphs: graphical models of the human brain connectome
Edward T Bullmore1, Danielle S Bassett
1Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0SZ, United Kingdom. etb23@cam.ac.uk
Annual Review of Clinical Psychology
|December 7, 2010
Summary
Brain graphs model the human connectome using graph theory. This review guides neuroimaging analysis, focusing on assumptions and trade-offs in studying brain networks.
Area of Science:
- Neuroscience
- Graph Theory
- Network Science
Background:
- Brain graphs abstractly model the human connectome, representing anatomical regions as nodes and connections as edges.
- This approach is increasingly popular for analyzing complex brain structures and functions.
Purpose of the Study:
- To provide a conceptual review and methodological guide for graphical analysis of human neuroimaging data.
- To highlight key assumptions, issues, and trade-offs in brain graph analysis.
Main Methods:
- Utilizing graph theory to define nervous systems as networks of nodes and edges.
- Measuring and comparing topological and geometrical properties of brain graphs.
- Analyzing both structural and functional human brain graphs.
Main Results:
- Human brain graphs consistently exhibit properties like small-worldness, modularity, and heterogeneous degree distributions.
- Brain graphs demonstrate a near-minimization of wiring cost, a significant geometric property.
Conclusions:
- Graphical analysis offers a powerful framework for understanding the human brain connectome.
- Investigators must carefully consider assumptions and trade-offs when applying these methods to neuroimaging data.
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