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Stochastic geometric network models for groups of functional and structural connectomes
Eric J Friedman1, Adam S Landsberg2, Julia P Owen3
1International Computer Science Institute, Berkeley, USA; Department of Computer Science, University of California, Berkeley, USA.
Neuroimage
|July 29, 2014
Summary
This study enhances stochastic models for analyzing brain connectomes, improving comparisons between groups of networks by accounting for variations. Connectomes exhibit significant "small-worldness" beyond geometric factors.
Area of Science:
- Neuroscience
- Network Science
- Biostatistics
Background:
- Connectome analysis is crucial for understanding brain function and disorders.
- Current methods use standard stochastic models, which may not be optimal for closely related network groups (e.g., controls vs. patients).
Purpose of the Study:
- To extend stochastic models for better connectome analysis.
- To develop statistical methods addressing inter-subject variations in connectomes.
- To improve group network comparisons.
Main Methods:
- Incorporated geometric network information (distances, asymmetries) into stochastic models.
- Utilized stochastic network density levels to capture connectivity variance.
- Developed new statistical fitting methodologies for group comparisons.
Main Results:
- Extended models better adapt to connectome structures.
- New methods allow comparison of average characteristics and variations within network groups.
- Connectomes demonstrate high "small-worldness" not solely explained by geometry or degree.
Conclusions:
- Enhanced stochastic models and statistical tools offer improved connectome analysis.
- These methods better account for biological variability in brain networks.
- Findings highlight unique network properties of connectomes.
