ArticleNeuroImage2021
Emergence of canonical functional networks from the structural connectome.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Near-infrared light-induced transcranial photobiomodulation enhances the visual pathway's function in healthy volunteers.Journal of biomedical optics · 2025Trial
- HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals.bioRxiv : the preprint server for biology · 2026Article
- Network Diffusion Modeling in Computational Neuroscience.Advances in experimental medicine and biology · 2026Article
- Structural and effective brain connectivity in focal epilepsy.Neuroimage. Reports · 2025Article
- Connectome harmonic decomposition tracks the presence of disconnected consciousness during ketamine-induced unresponsiveness.British journal of anaesthesia · 2025Article
- 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
- Simulation-based inference of developmental EEG maturation with the spectral graph model.Communications physics · 2024Article
- Differences in brain connectivity between older adults practicing Tai Chi and Water Aerobics: a case-control study.Frontiers in integrative neuroscience · 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
- Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- 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
- Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI.Nature communications · 2023Article
- Distributed harmonic patterns of structure-function dependence orchestrate human consciousness.Communications biology · 2023Article
- The role of the salience network in cognitive and affective deficits.Frontiers in human neuroscience · 2023Review
- Empirical estimation of the eigenmodes of macroscale cortical dynamics: Reconciling neural field eigenmodes and resting-state networks.Neuroimage. Reports · 2022Article
- Spectral graph theory of brain oscillations--Revisited and improved.NeuroImage · 2022Article
- A Riemannian Revisiting of Structure-Function Mapping Based on Eigenmodes.Frontiers in neuroimaging · 2022Article
- Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging.Frontiers in neuroscience · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its "structural connectome". By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.
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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.