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Cluster-span threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity
Summary
We introduce the Cluster-Span Threshold (CST), a novel method for network analysis. This unbiased threshold effectively distinguishes functional connectivity differences in brain activity, offering a new standard for network research.
Area of Science:
- Neuroscience
- Network Science
- Data Analysis
Background:
- Network analysis often relies on arbitrary thresholds, potentially obscuring meaningful topological features.
- Existing methods may not optimally capture the balance between local clustering and global spanning properties in complex networks.
Purpose of the Study:
- To introduce and validate a new, unbiased threshold for network analysis: the Cluster-Span Threshold (CST).
- To establish a more objective method for thresholding weighted networks, particularly in functional connectivity research.
Main Methods:
- The Cluster-Span Threshold (CST) is proposed, based on balancing clustering and spanning properties within network topology.
- The CST was applied to electroencephalogram (EEG) data from visual short-term memory tasks.
- Performance was compared against other thresholding methods, including maximum spanning trees.
Main Results:
- The CST demonstrated sensitivity in distinguishing functional connectivity differences between tasks.
- The proposed method provides an objective approach to thresholding weighted networks.
Conclusions:
- The Cluster-Span Threshold (CST) offers a sensitive and objective method for network analysis.
- This approach has the potential to significantly influence future research in functional connectivity and network science.
