ArticleIEEE journal of selected topics in signal processing2016
Graph Frequency Analysis of Brain Signals.
Article in IEEE journal of selected topics in signal processing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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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
- Exploring Neurofunctional Phase Transition Patterns in Autism Spectrum Disorder via Thermodynamics Parameters.Entropy (Basel, Switzerland) · 2026Article
- A Dimensionality Reduction Approach for Motor Imagery Brain-Computer Interface Using Functional Clustering and Graph Signal Processing.Journal of medical signals and sensors · 2026Article
- DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform.Genome biology · 2025Article
- Graph Fourier transform for spatial omics representation and analyses of complex organs.Nature communications · 2024Article
- Integrative, segregative, and degenerate harmonics of the structural connectome.Communications biology · 2024Article
- Decomposing cortical activity through neuronal tracing connectome-eigenmodes in marmosets.Nature communications · 2024Article
- A hybrid CNN-SVM model for enhanced autism diagnosis.PloS one · 2024Article
- Manifold Approximating Graph Interpolation of Cardiac Local Activation Time.IEEE transactions on bio-medical engineering · 2022Article
- A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging.Frontiers in neuroscience · 2022Article
- Functional brain activity constrained by structural connectivity reveals cohort-specific features for serum neurofilament light chain.Communications medicine · 2022Article
- Functional network estimation using multigraph learning with application to brain maturation study.Human brain mapping · 2021Article
- Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data.IEEE transactions on bio-medical engineering · 2021Article
- Learning Common Harmonic Waves on Stiefel Manifold - A New Mathematical Approach for Brain Network Analyses.IEEE transactions on medical imaging · 2021Article
- Gradients of connectivity as graph Fourier bases of brain activity.Network neuroscience (Cambridge, Mass.) · 2021Article
- Examining brain maturation during adolescence using graph Laplacian learning based Fourier transform.Journal of neuroscience methods · 2020Article
- Stationary time-vertex signal processing.EURASIP journal on advances in signal processing · 2019Article
- Brain imaging of locomotion in neurological conditions.Neurophysiologie clinique = Clinical neurophysiology · 2018Review
- Modeling and interpreting mesoscale network dynamics.NeuroImage · 2018Review
- Brain state flexibility accompanies motor-skill acquisition.NeuroImage · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different levels of task familiarity.
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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.