Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns.
Functional connectivity
Gray matter networks
Multilayer
Multiple sclerosis
Structural connectivity
Journal
Network neuroscience (Cambridge, Mass.)
ISSN: 2472-1751
Titre abrégé: Netw Neurosci
Pays: United States
ID NLM: 101719149
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
10
03
2022
accepted:
07
06
2022
entrez:
6
1
2023
pubmed:
7
1
2023
medline:
7
1
2023
Statut:
epublish
Résumé
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 gray 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 analyzing 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 gray 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 multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.
Identifiants
pubmed: 36605412
doi: 10.1162/netn_a_00258
pii: netn_a_00258
pmc: PMC9810367
doi:
Types de publication
Journal Article
Langues
eng
Pagination
916-933Informations de copyright
© 2022 Massachusetts Institute of Technology.
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