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.
Applying multilayer analysis to morphological, structural and functional brain networks to identify relevant dysfunction patterns
A. Solé-Ribalta, J. Borge-Holthoefer, J. Casas-Roma, […]