ArticleNetwork neuroscience (Cambridge, Mass.)2021
Network alignment and similarity reveal atlas-based topological differences in structural connectomes.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Contamination of Engram Coactivity Networks During Forgetting.bioRxiv : the preprint server for biology · 2026Article
- Inter-individual alignment of multimodal brain networks with anatomical constraints.Network neuroscience (Cambridge, Mass.) · 2026Article
- Dynamic brain connectivity predicts emotional arousal during naturalistic movie-watching.PLoS computational biology · 2025Article
- Graph alignment exploiting the spatial organization improves the similarity of brain networks.Human brain mapping · 2024Article
- To be and not to be: wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Problems, principles and progress in computational annotation of NMR metabolomics data.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- Network Analyses Based on Machine Learning Methods to Quantify Effects of Peptide-Protein Complexes as Drug Targets Using Cinnamon in Cardiovascular Diseases and Metabolic Syndrome as a Case Study.Frontiers in genetics · 2021Article
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6 authors.
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Abstract
The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas that can be estimated via diffusion magnetic resonance imaging (MRI) tractography. Herein, we aim to provide a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. 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-Leman (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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