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Analysis of Connectome Graphs Based on Boundary Scale
María José Moron-Fernández1, Ludovica Maria Amedeo2, Alberto Monterroso Muñoz1
1Higher Technical School of Informatics Engineering, University of Seville, Avda. Reina Mercedes, s/n, 41012 Seville, Spain.
Sensors (Basel, Switzerland)
|October 28, 2023
Summary
This study uses topological analysis of brain connectome graphs to reveal neural connectivity differences between sexes. The Boundary Scale (BS2) model enhances topological information for clinical insights.
Area of Science:
- Computational neuroscience
- Network science
- Topological data analysis
Background:
- Connectome graphs represent brain connectivity.
- Understanding neural connectivity differences is crucial for clinical insights.
- Topological methods can enrich graph analysis.
Purpose of the Study:
- To advance the computational study of connectome graphs using topology.
- To enrich topological information extracted from brain graphs.
- To provide insightful clinical descriptions of neural connectivity.
Main Methods:
- Utilized a sequence of hypergraphs derived from brain graphs via the Boundary Scale (BS2) model.
- Analyzed the scale-space representation using topological features like Betti numbers and average node/edge degrees.
- Empirically analyzed neuroimaging data from the Human Connectome Project (96 healthy subjects).
Main Results:
- The BS2 model substantially enriched topological information from the original graph.
- Topological features revealed differences in neural connectivity between male and female subjects.
- Demonstrated the qualitative and quantitative information gain of the BS2 model.
Conclusions:
- Topological analysis of connectome graphs offers valuable clinical insights.
- The BS2 model provides an effective framework for enhanced connectome analysis.
- Significant sex-based differences in neural connectivity were identified.
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