ArticleFrontiers in neuroinformatics2011
On the Influence of Amplitude on the Connectivity between Phases.
Article in Frontiers in neuroinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Microstates and connectivity states reveal task-related network reorganization in Alzheimer's disease patients.Clinical neurophysiology practice · 2026Article
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- Molecular and neuropathological determinants of neuronal dysfunction in Alzheimer's disease.bioRxiv : the preprint server for biology · 2025Article
- Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome coupled oscillators model.Scientific reports · 2024Article
- Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease.Communications biology · 2024Article
- The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models.PloS one · 2024Article
- Recent Progress in Brain Network Models for Medical Applications: A Review.Health data science · 2024Review
- In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- Bridging functional and anatomical neural connectivity through cluster synchronization.Scientific reports · 2023Article
- Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness.NeuroImage · 2023Review
- Revealing the combined roles of Aβ and tau in Alzheimer's disease via a pathophysiological activity decoder.bioRxiv : the preprint server for biology · 2023Article
- Article
- Reaction-diffusion models in weighted and directed connectomes.PLoS computational biology · 2022Article
- Human brain structural connectivity matrices-ready for modelling.Scientific data · 2022Article
- Metastable oscillatory modes emerge from synchronization in the brain spacetime connectome.Communications physics · 2022Article
- Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Phase synchronization and measure of criticality in a network of neural mass models.Scientific reports · 2022Article
- Sleeping at the switch.eLife · 2021Article
- Article
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2 authors.
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Abstract
In recent studies, functional connectivities have been reported to display characteristics of complex networks that have been suggested to concur with those of the underlying structural, i.e., anatomical, networks. Do functional networks always agree with structural ones? In all generality, this question can be answered with "no": for instance, a fully synchronized state would imply isotropic homogeneous functional connections irrespective of the "real" underlying structure. A proper inference of structure from function and vice versa requires more than a sole focus on phase synchronization. We show that functional connectivity critically depends on amplitude variations, which implies that, in general, phase patterns should be analyzed in conjunction with the corresponding amplitude. We discuss this issue by comparing the phase synchronization patterns of interconnected Wilson-Cowan models vis-à-vis Kuramoto networks of phase oscillators. For the interconnected Wilson-Cowan models we derive analytically how connectivity between phases explicitly depends on the generating oscillators' amplitudes. In consequence, the link between neurophysiological studies and computational models always requires the incorporation of the amplitude dynamics. Supplementing synchronization characteristics by amplitude patterns, as captured by, e.g., spectral power in M/EEG recordings, will certainly aid our understanding of the relation between structural and functional organizations in neural networks at large.
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