ArticleNetwork neuroscience (Cambridge, Mass.)2023
Stability and dynamics of a spectral graph model of brain oscillations.
Article in Network neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Simulation-based inference of developmental EEG maturation with the spectral graph model.Communications physics · 2024Article
- Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.Research square · 2023Article
- Bayesian inference of a spectral graph model for brain oscillations.NeuroImage · 2023Article
- Bayesian Inference of a Spectral Graph Model for Brain Oscillations.bioRxiv : the preprint server for biology · 2023Article
- Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging.Frontiers in neuroscience · 2022Review
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Authors and funding
3 authors.
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Abstract
We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.
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