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

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 which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein, we aim at providing 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-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.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

“…The reproducibility of the network was evaluated by node similarity. 58 The average node similarity of GCMN KEGG (3.49E × 10 −2 ) was larger than that of the KEGG MRN (1.98E × 10 −4 ) (Table 1), which implied that better consistent annotation results can be obtained under different conditions.…”

Dynamic functional connectivity encodes generalizable representations of emotional arousal across individuals and situational contexts

“…Statistical significance of network overlap was assessed both using a hypergeometric cumulative density function (53) and using the Jaccard index as the measure of network similarity (54) (see Methods). The two methods generated consistent results.…”

“…The other was the Jaccard index (54), which measures the similarity between two sets by computing the ratio between the size of their intersection and the size of their union. It was implemented using sklearn.metrix.jaccard_score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.…”