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Analysis of Connectome Graphs Based on Boundary Scale.

María José Moron-Fernández1, Ludovica Maria Amedeo2, Alberto Monterroso Muñoz1

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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.

Keywords:
Betti numbersconnectomehypergraph theorytopological scale

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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.