An edge-centric model for harmonizing multi-relational network datasets

Abstract

Functional and structural connections vary across conditions, measurements, and time. However, how to resolve multi-relational measures of connectivity remains an open challenge. Here, we propose an extension of structural covariance and morphometric similarity methods to integrate multiple estimates of connectivity into a single edge-centric network representation. We highlight the utility of this method through two applications: an analysis of multi-task functional connectivity data and multi-measure structural networks. In these analyses, we use data-driven clustering techniques to identify collections of edges that covary across tasks and measures, revealing overlapping mesoscale architecture. We also link these features to node-level properties such as modularity and canonical descriptors of brain systems. We further demonstrate that, in the case of multi-task functional networks, edge-level features are consistent across individuals yet exhibit subject-specificity. We conclude by highlighting other instances where the edge-centric model may be useful.

Edges in brain networks: Contributions to models of structure and function

“…Such a procedure can increase the reliability of intrinsic connectivity estimation. Relatedly, correlation values from various scan sessions can form a feature set at each edge ( Figure 2D ), which can be used to create an edge-centric representation of edge covariance across conditions ( Faskowitz et al, 2021 ). Thus, edges can report multifaceted relationships incorporating a variety of data sources.…”

“…(C) Clustering an edge-edge network representation, in which network incidence (e.g., line graph) or pairwise edge similarity is assessed, results in an edge community structure; by affiliating each edge with a cluster, each node is associated multiple (or overlapping) communities (figure reproduced from M. A. de Reus, Saenger, Kahn, & van den Heuvel, 2014 , with permission from The Royal Society, UK). (D) The pairwise similarity between edges can be assessed by correlating feature sets at the edges, such as multiple tractography streamline weights or functional correlation measures taken during distinct tasks (figure adapted from Faskowitz, Tanner, Misic, & Betzel, 2021 ).…”

“…We acknowledge the following manuscripts from which figures were adapted: Figure 2 , de Reus et al (2014) (with permission from The Royal Society, UK), Faskowitz et al (2021) ; Figure 3 , Z.-Q. Liu et al (2020) (CC BY 4.0), Balsters et al (2020) (CC BY 4.0), Betzel et al (2018) (CC BY 4.0), Sizemore et al (2018) (CC BY 4.0); Figure 4 , Sarwar et al (2021) (CC BY 4.0); Figure 5 , Faskowitz et al (2020) .…”

Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

“…This interaction (i.e., the connection between brain regions) reflects the temporal dependence of the time series of each brain area, called brain functional connectivity, which is obtained by calculating the Pearson correlation coefficient between any two brain regions' time series. More recently, however, Faskowitz has created a network model that is focused on the edge and computes eTS and eFC [21–23] …”

“…More recently, however, Faskowitz has created a network model that is focused on the edge and computes eTS and eFC. [21][22][23]…”