ArticleNeuroImage2022
Spectral graph theory of brain oscillations--Revisited and improved.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
24 citing papers in PubMed.
- HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals.bioRxiv : the preprint server for biology · 2026Article
- High-dimensional inference for functional regression with an application to the Alzheimer's disease magnetoencephalography study.Biostatistics (Oxford, England) · 2025Article
- Selective vulnerability and resilience to Alzheimer's disease tauopathy as a function of genes and the connectome.Brain : a journal of neurology · 2025Article
- Evolution of the Rich Club Properties in Mouse, Macaque, and Human Brain Networks: A Study of Functional Integration, Segregation, and Balance.Neuroscience bulletin · 2025Article
- Spectral Biomarkers of Functional Brain Network Alteration in Alzheimer's Disease.Current Alzheimer research · 2025Observational
- Structure-function coupling in macroscale human brain networks.Nature reviews. Neuroscience · 2024Review
- Integrative, segregative, and degenerate harmonics of the structural connectome.Communications biology · 2024Article
- Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.PloS one · 2024Article
- Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model.Imaging neuroscience (Cambridge, Mass.) · 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
- 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
- Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes.Nature communications · 2023Article
- Bayesian inference of a spectral graph model for brain oscillations.NeuroImage · 2023Article
- Multi-modal and multi-model interrogation of large-scale functional brain networks.NeuroImage · 2023Article
- Article
- Bayesian Inference of a Spectral Graph Model for Brain Oscillations.bioRxiv : the preprint server for biology · 2023Article
- Distributed harmonic patterns of structure-function dependence orchestrate human consciousness.Communications biology · 2023Article
- Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.Network neuroscience (Cambridge, Mass.) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.
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Registered trials
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.