ArticleImaging neuroscience (Cambridge, Mass.)2025
Modeling dynamic inflow effects in fMRI to quantify cerebrospinal fluid flow.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Quantifying Cardiac, Respiratory, and Low Frequency Components of CSF Motion From fMRI Inflow Effects.Magnetic resonance in medicine · 2026Article
- A conversation on human brain clearance.Nature communications · 2026Article
- Simultaneous Real-time Imaging of Neurofluid and Neurovascular Dynamics Using Ultrafast Flow-weighted Echo-Planar Imaging.bioRxiv : the preprint server for biology · 2026Article
- Simultaneous real-time imaging of upper cervical cerebrospinal fluid and vascular dynamics using ultrafast inflow-weighted EPI.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Quantitative evaluation of low-frequency oscillations using real-time phase-contrast MRI during drowsiness.Fluids and barriers of the CNS · 2025Article
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3 authors.
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Abstract
Cerebrospinal fluid (CSF) flow in the brain is tightly regulated and essential for brain health, and imaging techniques are needed to quantitatively establish the properties of this flow system. Flow-sensitive fMRI has recently emerged as a tool to measure large scale CSF flow dynamics with high sensitivity and temporal resolution; however, the measured signal is not quantitative. Here, we developed a dynamic model to simulate fMRI inflow signals based on time-varying flow velocities. We validated the model in both human and phantom data, and used it to identify important properties of the fMRI inflow signal that inform how the signal should be interpreted. Additionally, we developed a physics-based deep learning framework to invert the model, which enables direct estimation of velocity using fMRI inflow data. This work allows new quantitative information to be obtained from fMRI, which will enable neuroimaging researchers to take advantage of the high sensitivity, high temporal resolution, and wide availability of fMRI to obtain flow signals that are physically interpretable.
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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.