ArticleNeuroimage. Reports2022
Empirical estimation of the eigenmodes of macroscale cortical dynamics: Reconciling neural field eigenmodes and resting-state networks.
Article in Neuroimage. Reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Pronounced Nonlinear Traits at Epileptic Seizure Onset and Offset.Brain topography · 2026Article
- Modeling the influences of non-local connectomic projections on geometrically constrained cortical dynamics.PLoS computational biology · 2026Article
- Forward-Projected Cortical Eigenmodes Provide an Efficient Sensor-Space Representation of Resting-State EEG.Brain topography · 2026Article
- A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Mode-based morphometry: A multiscale approach to mapping human neuroanatomy.Human brain mapping · 2024Article
- Geometric constraints on human brain function.Nature · 2023Article
- Mode-based morphometry: A multiscale approach to mapping human neuroanatomy.bioRxiv : the preprint server for biology · 2023Article
- Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI.Nature communications · 2023Article
- The mesoanatomy of the cortex, minimization of free energy, and generative cognition.Frontiers in computational neuroscience · 2023Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Neural field theories successfully describe normal, awake mesoscale to macroscale brain activity using linearized partial differential equations that involve a Laplacian operator. These neural field theories therefore predict spatial brain activity patterns that are simple, smooth eigenfunctions of the Laplacian operator that satisfy the Helmholtz equation and extend across the entire cortex. However, complex, localized spatial patterns such as resting-state networks have been proposed from data analysis of brain activity measurements, primarily fMRI. Using Human Connectome Project data, the Helmholtz eigenmode prediction of neural field theory is shown to be consistent with eigenvectors of both fMRI and MEG covariance matrices. It is explained why these Helmholtz equation eigenmodes have been difficult to observe due to their similar spectra, effects of covariant input stimuli to the brain, and measurement noise. This implies that complex spatial patterns like resting-state networks may be the result of complex input to the brain but simple intrinsic brain dynamics, rather than simple stimuli and complex brain dynamics, as assumed in many existing models.
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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.