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Network alignment and similarity reveal atlas-based topological differences in structural connectomes

Abstract : Brain atlases are central objects in network neuroscience, where the interactions between different brain regions are modeled as a graph called connectome. In structural connectomes, nodes are parcels from a predefined cortical atlas and edges encode the strength of the axonal connectivity between regions measured via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein, we aim at providing a novel perspective on the evaluation of brain atlases by modeling it as a network alignment problem, with the goal of tackling the following question: given an atlas, how robustly does it capture the network topology across different subjects? To answer such a question, we introduce two novel concepts arising as natural generalizations of previous ones. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Lehman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available.
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Contributor : Matteo Frigo <>
Submitted on : Tuesday, December 1, 2020 - 3:03:36 PM
Last modification on : Saturday, May 8, 2021 - 2:02:01 PM
Long-term archiving on: : Tuesday, March 2, 2021 - 8:29:27 PM


  • HAL Id : hal-03033777, version 1



Matteo Frigo, Emilio Cruciani, David Coudert, Rachid Deriche, Emanuele Natale, et al.. Network alignment and similarity reveal atlas-based topological differences in structural connectomes. 2020. ⟨hal-03033777⟩



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