Topological Homogeneity for Electron Microscopy Images

Molina‐Abril, Helena; Díaz-del-Río, Fernando; Guerrero-Lebrero, Maria P.; Real, Pedro; Bárcena, Guillermo; Braza, V.; Guerrero, Elisa; González, David; Galindo, Pedro L.

doi:10.1007/978-3-030-10828-1_13

Cited by 1 publication

(2 citation statements)

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“…Note that e is considered here as a vector. They connect consecutive hypergraph components, preserving homological information [14,15]. Extracting from a BS 2 -model classical and new (local and global) topological indices is the method of TSF for topologically discriminating brain graphs.…”

Section: Boundary-scale Theory For Hypergraphsmentioning

confidence: 99%

“…In this work, an innovative software tool developed in Python language is presented for the analysis of brain graphs, based on the new "Topological Scale Framework" (TSF) [14,15]. More concretely, the set of algorithms proposed here conforms an iterative process that uses as initial value the incidence matrix (Figure 1) of the original brain graph, to gradually generate a sequence of associated hypergraphs parameterized by a scale of topological nature.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Connectome Graphs Based on Boundary Scale

Moron-Fernández,

Amedeo,

Monterroso Muñoz

et al. 2023

Sensors

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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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Section: Boundary-scale Theory For Hypergraphsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%