ArticleNetwork neuroscience (Cambridge, Mass.)2021
Gradients of connectivity as graph Fourier bases of brain activity.
Article in Network neuroscience (Cambridge, Mass.), 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.
- Spectral organization of individualized connectome harmonics across brain structure, function and cognition.bioRxiv : the preprint server for biology · 2026Article
- Structure-function dynamics in healthy cognitive aging: a graph signal processing approach.Brain structure & function · 2026Article
- N,N-dimethyltryptamine effects on connectome harmonics, subjective experience and comparative psychedelic experiences.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Large-scale comparative analysis reveals top graph signal processing features for subject identification.bioRxiv : the preprint server for biology · 2025Article
- Neural dynamics of semantic control underlying generative storytelling.Communications biology · 2025Article
- Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis.Human brain mapping · 2025Article
- Low-dimensional controllability of brain networks.PLoS computational biology · 2025Article
- Structure-function coupling and decoupling during movie watching and resting state: Novel insights bridging EEG and structural imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Comparing and scaling fMRI features for brain-behavior prediction.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Sex-specific topological structure associated with dementia via latent space estimation.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Comparing structure-function relationships in brain networks using EEG and fNIRS.Scientific reports · 2024Article
- Structure-function coupling in macroscale human brain networks.Nature reviews. Neuroscience · 2024Review
- Integrative, segregative, and degenerate harmonics of the structural connectome.Communications biology · 2024Article
- Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.Nature communications · 2024Article
- Time-resolved coupling between connectome harmonics and subjective experience under the psychedelic DMT.bioRxiv : the preprint server for biology · 2024Article
- Decomposing cortical activity through neuronal tracing connectome-eigenmodes in marmosets.Nature communications · 2024Article
- Local orchestration of distributed functional patterns supporting loss and restoration of consciousness in the primate brain.Nature communications · 2024Article
- Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Methods for decoding cortical gradients of functional connectivity.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Distributed harmonic patterns of structure-function dependence orchestrate human consciousness.Communications biology · 2023Article
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5 authors.
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Abstract
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.
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