ArticleNetwork neuroscience (Cambridge, Mass.)2022
Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns.
Article in Network neuroscience (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Cognitive outcomes in multiple sclerosis are shaped by divergent functional connectivity trajectories.Brain communications · 2026Article
- Evaluating topological and graph-theoretical approaches to extract complex multimodal brain connectivity patterns in multiple sclerosis.Health information science and systems · 2025Article
- Musicians' brains at rest: multilayer network analysis of magnetoencephalography data.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- More Than the Sum of Its Parts: Disrupted Core Periphery of Multiplex Brain Networks in Multiple Sclerosis.Human brain mapping · 2025Article
- Multilayer network analysis across cortical depths in 7-T resting-state fMRI.Network neuroscience (Cambridge, Mass.) · 2025Article
- Multilayer network analysis in patients with end-stage kidney disease.Scientific reports · 2024Article
- Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI.bioRxiv : the preprint server for biology · 2024Article
- Retinal Damage and Visual Network Reconfiguration Defines Visual Function Recovery in Optic Neuritis.Neurology(R) neuroimmunology & neuroinflammation · 2024Article
- Multilayer network analysis in patients with juvenile myoclonic epilepsy.Neuroradiology · 2024Article
- Improving explanation of motor disability with diffusion-based graph metrics at onset of the first demyelinating event.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024Article
- Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- covSTATIS: a multi-table technique for network neuroscience.Aperture neuro · 2024Article
- Multilayer network changes in patients with migraine.Brain and behavior · 2023Article
- Involvement of the default mode network in patients with transient global amnesia: multilayer network.Neuroradiology · 2023Article
- Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder.Psychoradiology · 2023Article
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Authors and funding
14 authors.
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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 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.