In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or grey matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural and functional brain connectivity networks. The aim of this study was to combine the morphological, structural and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analysing multiple types of relational data from the same objects simultaneously using graph-mining techniques. The main contribution of this research is the design, development and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with grey matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with MS, and results show that several brain regions with a synchronised connectivity deterioration could be identified.

This study presents the design, development and validation of a framework that merges 4 morphological, structural and functional brain connectivity networks into one multilayer network. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with MS, and results show that some brain regions with a synchronised connectivity deterioration could be identified.

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Author notes

These authors contributed equally.

Handling Editor: Olaf Sporns

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