Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns

Abstract

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.

Musicians’ brains at rest: Multilayer network analysis of MEG data

“…investigated in (Battiston et al, 2017(Battiston et al, , 2018Lim et al, 2019) for one fMRIbased and an additional structural network layer for healthy controls. Recently, a framework for including -next to structural and functional layers -also a third morphological grey matter network layer has been proposed (Casas-Roma et al, 2022). Therefore, the multilayer network approach allows us to study in an individual how brain processes at different scales, from functional to structural, undergo plasticity related changes.…”

Multilayer network changes in patients with migraine

“…The multilayer clustering coefficient quantifies the average clustering coefficient of a node across all layers, accounting for both intralayer and interlayer clustering (Buldu & Papo, 2018;Casas-Roma et al, 2022;De Domenico, 2018;Lv et al, 2021;Puxeddu et al, 2021;Shahabi et al, 2023). The mean clustering coefficient indicates how wellneighboring nodes are connected, which is related to the segregation of the network.…”

More than the sum of its parts: disrupted core-periphery of multiplex networks in multiple sclerosis

“…As the information conveyed by connectivity data is multivariate in nature and multimodal datasets become increasingly available, it has been advocated that multilayer networks, rather than single-layer architectures, may represent the ideal mathematical framework to study the brain as a complex system. [10][11][12] However, how to meaningfully model together structural and functional aspects of brain connectivity is still debated, with novel possible methodological solutions continuing to emerge. [10][11][12] The method adopted here to detect the core-periphery of multiplex networks has the advantage of minimizing the need for a priori assumptions, reducing the variable degree of arbitrariness and information loss that are inevitably associated with the processes of, e.g., thresholding/binarizing connectivity matrices or assigning predefined weights to the different layers.…”

“…[10][11][12] However, how to meaningfully model together structural and functional aspects of brain connectivity is still debated, with novel possible methodological solutions continuing to emerge. [10][11][12] The method adopted here to detect the core-periphery of multiplex networks has the advantage of minimizing the need for a priori assumptions, reducing the variable degree of arbitrariness and information loss that are inevitably associated with the processes of, e.g., thresholding/binarizing connectivity matrices or assigning predefined weights to the different layers. 12 In our large multi-centre population, the data-driven optimization procedure showed that all three modalities are necessary to maximize the separation between PwMS and HC in regional coreness, with greater estimated contribution coefficients for structural rather than functional layers.…”

“…9,10 Intriguingly, such a model can capture higher-order emergent properties that are not evident from conventional single-layer architectures, thus potentially containing additional and novel information relating to clinical status. [9][10][11] Among the topological properties shown to meaningfully scale to a multilayer setting is the core-periphery structure, 12,13 a fundamental feature of many real-world complex networks (including the brain), characterized by a subgraph of densely connected and topologically central nodes (the core), and a set of nodes that are strongly connected with the core but All rights reserved. No reuse allowed without permission.…”