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Updated: Jul 11, 2026
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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Graph theoretical analysis of complex networks in the brain
Cornelis J Stam1, Jaap C Reijneveld
1Department of Clinical Neurophysiology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. cj.stam@vumc.nl.
Nonlinear Biomedical Physics
|October 3, 2007
Summary
Complex systems are studied using network analysis, revealing the human brain
Area of Science:
- Neuroscience and Complex Systems Analysis
Background:
- The study of complex systems has advanced significantly with network theory, particularly small-world and scale-free networks.
- Understanding the interplay between network structure and dynamics is crucial, with applications emerging in neuroscience.
- Graph analysis is increasingly used to study neural networks, anatomical, and functional connectivity (fMRI, EEG, MEG).
Purpose of the Study:
- To investigate the human brain as a complex network with a small-world structure.
- To explore the hypothesized optimal properties of this small-world brain network, including synchronization and information transfer.
- To examine the relationship between functional network topology, genetic/anatomical factors, task-related modifications, and neurological diseases.
Main Methods:
- Utilizing graph spectral analysis to determine network synchronizability.
- Applying graph analysis to models of neural networks and empirical data from fMRI, EEG, and MEG.
- Comparing functional network topology in healthy individuals with patterns observed in brain diseases.
Main Results:
- The human brain exhibits a small-world network structure in both anatomical and functional connectivity.
- This small-world topology is associated with efficient synchronization, information transfer, and balanced processing.
- Deviations from the optimal small-world pattern are linked to neurological conditions like Alzheimer's disease and schizophrenia.
Conclusions:
- The human brain can be effectively modeled as a complex small-world network.
- This network structure is crucial for optimal brain function, balancing local and global information processing.
- Alterations in functional network topology may serve as biomarkers for various brain diseases.
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