ArticleNeuroImage2018
On the detection of high frequency correlations in resting state fMRI.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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
27 citing papers in PubMed.
- Inhibitory Decay and Supercritical Brain Dynamics During Sleep Deprivation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- Real-time fMRI using multi-band echo-volumar imaging with millimeter spatial resolution and sub-second temporal resolution at 3 tesla.Frontiers in neuroscience · 2025Article
- Whole-brain modular dynamics at rest predict sensorimotor learning performance.Network neuroscience (Cambridge, Mass.) · 2025Article
- Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.Network neuroscience (Cambridge, Mass.) · 2024Article
- Characterizing systemic physiological effects on the blood oxygen level dependent signal of resting-state fMRI in time-frequency space using wavelets.Human brain mapping · 2023Article
- Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.Scientific reports · 2023Article
- Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI.Nature communications · 2023Article
- Review
- Functional ultrasound detects frequency-specific acute and delayed S-ketamine effects in the healthy mouse brain.Frontiers in neuroscience · 2023Article
- The role of ipsilateral motor network in upper limb movement.Frontiers in physiology · 2023Article
- Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study.Human brain mapping · 2022Article
- Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics.NeuroImage · 2021Article
- Imaging the temporal dynamics of brain states with highly sampled fMRI.Current opinion in behavioral sciences · 2021Article
- A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis.Frontiers in human neuroscience · 2021Article
- The Role of the Cerebellum in Tremor - Evidence from Neuroimaging.Tremor and other hyperkinetic movements (New York, N.Y.) · 2021Review
- Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals.Brain connectivity · 2020Article
- Recent advances of deep learning in psychiatric disorders.Precision clinical medicine · 2020Review
- Model-based physiological noise removal in fast fMRI.NeuroImage · 2020Article
- The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.Human brain mapping · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Current studies of resting-state connectivity rely on coherent signal fluctuations at frequencies below 0.1 Hz, however, recent studies using high-speed fMRI have shown that fluctuations above 0.5 Hz may exist. This study replicates the feasibility of measuring high frequency (HF) correlations in six healthy controls and a patient with a brain tumor while analyzing non-physiological signal sources via simulation. Resting-state data were acquired using a high-speed multi-slab echo-volumar imaging pulse sequence with 136 ms temporal resolution. Bandpass frequency filtering in combination with sliding window seed-based connectivity analysis using running mean of the correlation maps was employed to map HF correlations up to 3.7 Hz. Computer simulations of Rician noise and the underlying point spread function were analyzed to estimate baseline spatial autocorrelation levels in four major networks (auditory, sensorimotor, visual, and default-mode). Using seed regions based on Brodmann areas, the auditory and default-mode networks were observed to have significant frequency band dependent HF correlations above baseline spatial autocorrelation levels. Correlations in the sensorimotor network were at trend level. The auditory network was still observed using a unilateral single voxel seed. In the patient, HF auditory correlations showed a spatial displacement near the tumor consistent with the displacement seen at low frequencies. In conclusion, our data suggest that HF connectivity in the human brain may be observable with high-speed fMRI, however, the detection sensitivity may depend on the network observed, data acquisition technique, and analysis method.
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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.