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

Faskowitz, J.; Tanner, J.; Misic, B.; Betzel, R.

doi:10.1101/2021.01.07.425450

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bioRxiv · 10.1101/2021.01.07.425450

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

Faskowitz, J.·Tanner, J.·Misic, B.·Betzel, R.

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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.

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Faskowitz, J., Tanner, J., Misic, B., Betzel, R.. 2021-01-08. An edge-centric model for harmonizing multi-relational network datasets. https://doi.org/10.1101/2021.01.07.425450

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