ArticleNeuroImage. Clinical2016
The application of a mathematical model linking structural and functional connectomes in severe brain injury.
Article in NeuroImage. Clinical, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Krakencoder: a unified brain connectome translation and fusion tool.Nature methods · 2025Article
- Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases.PLoS computational biology · 2025Review
- Structure-function coupling in highly sampled individual brains.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Disrupted topological organization of functional brain networks in traumatic axonal injury.Brain imaging and behavior · 2024Article
- Spectral graph model for fMRI: A biophysical, connectivity-based generative model for the analysis of frequency-resolved resting-state fMRI.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Eigenmode-based approach reveals a decline in brain structure-function liberality across the human lifespan.Communications biology · 2023Article
- Brain network communication: concepts, models and applications.Nature reviews. Neuroscience · 2023Review
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- Distributed harmonic patterns of structure-function dependence orchestrate human consciousness.Communications biology · 2023Article
- Structural-functional connectivity bandwidth predicts processing speed in mild traumatic brain Injury: A multiplex network analysis.NeuroImage. Clinical · 2023Article
- Exploring personalized structural connectomics for moderate to severe traumatic brain injury.Network neuroscience (Cambridge, Mass.) · 2023Article
- Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity.Network neuroscience (Cambridge, Mass.) · 2023Article
- Article
- Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness.Communications biology · 2022Article
- Article
- Heritability and interindividual variability of regional structure-function coupling.Nature communications · 2021Article
- Quantification of hemodynamics of cerebral arteriovenous malformations after stereotactic radiosurgery using 4D flow magnetic resonance imaging.Journal of magnetic resonance imaging : JMRI · 2021Observational
- Network topology changes in chronic mild traumatic brain injury (mTBI).NeuroImage. Clinical · 2021Article
- Consciousness and complexity: a consilience of evidence.Neuroscience of consciousness · 2021Review
- Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis.Frontiers in neuroscience · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
Following severe injuries that result in disorders of consciousness, recovery can occur over many months or years post-injury. While post-injury synaptogenesis, axonal sprouting and functional reorganization are known to occur, the network-level processes underlying recovery are poorly understood. Here, we test a network-level functional rerouting hypothesis in recovery of patients with disorders of consciousness following severe brain injury. This hypothesis states that the brain recovers from injury by restoring normal functional connections via alternate structural pathways that circumvent impaired white matter connections. The so-called network diffusion model, which relates an individual's structural and functional connectomes by assuming that functional activation diffuses along structural pathways, is used here to capture this functional rerouting. We jointly examined functional and structural connectomes extracted from MRIs of 12 healthy and 16 brain-injured subjects. Connectome properties were quantified via graph theoretic measures and network diffusion model parameters. While a few graph metrics showed groupwise differences, they did not correlate with patients' level of consciousness as measured by the Coma Recovery Scale - Revised. There was, however, a strong and significant partial Pearson's correlation (accounting for age and years post-injury) between level of consciousness and network diffusion model propagation time (r = 0.76, p < 0.05, corrected), i.e. the time functional activation spends traversing the structural network. We concluded that functional rerouting via alternate (and less efficient) pathways leads to increases in network diffusion model propagation time. Simulations of injury and recovery in healthy connectomes confirmed these results. This work establishes the feasibility for using the network diffusion model to capture network-level mechanisms in recovery of consciousness after severe brain injury.
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