AI Article Synopsis

  • The study aims to enhance the understanding of brain connectome graphs through topological analysis using hypergraphs tied to the Boundary Scale model (BS2).
  • By applying classical topological features like Betti numbers and edge degrees, the research enriches the topological insight extracted from the brain's graph, offering valuable clinical interpretations.
  • The analysis, based on neuroimaging data from 96 healthy individuals (52 women and 44 men), highlights differences in neural connectivity between genders, providing deeper insights into brain function.

Article Abstract

The purpose of this work is to advance in the computational study of connectome graphs from a topological point of view. Specifically, starting from a sequence of hypergraphs associated to a brain graph (obtained using the Boundary Scale model, BS2), we analyze the resulting scale-space representation using classical topological features, such as Betti numbers and average node and edge degrees. In this way, the topological information that can be extracted from the original graph is substantially enriched, thus providing an insightful description of the graph from a clinical perspective. To assess the qualitative and quantitative topological information gain of the BS2 model, we carried out an empirical analysis of neuroimaging data using a dataset that contains the connectomes of 96 healthy subjects, 52 women and 44 men, generated from MRI scans in the Human Connectome Project. The results obtained shed light on the differences between these two classes of subjects in terms of neural connectivity.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610691PMC
http://dx.doi.org/10.3390/s23208607DOI Listing

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