ArticleFrontiers in neuroscience2019
Intrinsic Frequencies of the Resting-State fMRI Signal: The Frequency Dependence of Functional Connectivity and the Effect of Mode Mixing.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 70 citations in OpenAlex.
- Effects of single- and multiple-dose oxytocin treatment on amygdala low-frequency BOLD fluctuations and BOLD spectral dynamics in autism.Translational psychiatry · 2022Trial
- Unveiling Functional-Metabolic Synergy in the Healthy Brain: Multivariate Integration of Dynamic [Human brain mapping · 2026Article
- Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium.medRxiv : the preprint server for health sciences · 2026Article
- Network-nodal tACS induces right-lateralization of thalamocortical connectivity.Scientific reports · 2026Article
- Review
- Functional connectivity of the superior temporal sulcus at term-equivalent age: effects of gestational age and sex.Brain structure & function · 2025Article
- Functional development of the human cerebellum from birth to age five.Nature communications · 2025Article
- Frequency band analysis of nonstationary multivariate time series.Biometrics · 2025Article
- A robust approach for analyzing and mapping hierarchical brain connectome towards laminar-specific neural networks.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Exploring functional connectivity at different timescales with multivariate mode decomposition.Frontiers in neuroscience · 2025Article
- Whole-brain modular dynamics at rest predict sensorimotor learning performance.Network neuroscience (Cambridge, Mass.) · 2025Article
- Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.Human brain mapping · 2024Article
- Community detection in the human connectome: Method types, differences and their impact on inference.Human brain mapping · 2024Article
- Multiscale Functional Connectivity analysis of episodic memory reconstruction.Frontiers in cognition · 2024Article
- Frequency-dependent alterations in functional connectivity in patients with Alzheimer's Disease spectrum disorders.Frontiers in aging 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
- From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data.Human brain mapping · 2023Review
- Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths.Neuroinformatics · 2023Article
- Refugee visa insecurity disrupts the brain's default mode network.European journal of psychotraumatology · 2023Article
- Cortical depth-dependent human fMRI of resting-state networks using EPIK.Frontiers in neuroscience · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The frequency characteristics of the resting-state BOLD fMRI (rs-fMRI) signal are of increasing scientific interest, as we discover more frequency-specific biological interpretations. In this work, we use variational mode decomposition (VMD) to precisely decompose the rs-fMRI time series into its intrinsic mode functions (IMFs) in a data-driven manner. The accuracy of the VMD decomposition of constituent IMFs is verified through simulations, with higher reconstruction accuracy and much-reduced mode mixing relative to previous methods. Furthermore, we examine the relative contribution of the VMD-derived modes (frequencies) to the rs-fMRI signal as well as functional connectivity measurements. Our primary findings are: (1) The rs-fMRI signal within the 0.01-0.25 Hz range can be consistently characterized by four intrinsic frequency clusters, centered at 0.028 Hz (IMF4), 0.080 Hz (IMF3), 0.15 Hz (IMF2) and 0.22 Hz (IMF1); (2) these frequency clusters were highly reproducible, and independent of rs-fMRI data sampling rate; (3) not all frequencies were associated with equivalent network topology, in contrast to previous findings. In fact, while IMF4 is most likely associated with physiological fluctuations due to respiration and pulse, IMF3 is most likely associated with metabolic processes, and IMF2 with vasomotor activity. Both IMF3 and IMF4 could produce the brain-network topology typically observed in fMRI, whereas IMF1 and IMF2 could not. These findings provide initial evidence of feasibility in decomposing the rs-fMRI signal into its intrinsic oscillatory frequencies in a reproducible manner.
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