ArticleProceedings of the National Academy of Sciences of the United States of America2023
Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Unveiling the glymphatic system's impact on neurodegenerative diseases: a comprehensive bibliometric analysis (2012-2024).Frontiers in aging neuroscience · 2025Pooled it
- High frame ratebioRxiv : the preprint server for biology · 2026Article
- Neurovascular Signaling in the Perivascular Space: Biophysical Principles and Disruption in Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Review
- In-silico solute transport via perivascular networks in the human intracranial space.Nature communications · 2026Article
- MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.Science advances · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Robust MR-AIV: A Systematic Study of Robustness Improvement and Sensitivity Analysis of MR-AIV.bioRxiv : the preprint server for biology · 2026Article
- Quantifying cerebrospinal fluid flow in pial perivascular spaces of rats.Fluids and barriers of the CNS · 2026Article
- Learning in PINNs: Phase transition, diffusion equilibrium, and generalization.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Human cerebrospinal fluid net flow enhanced by respiration during the awake state.Nature communications · 2025Article
- Review
- Glymphatic Clearance in the Optic Nerve: A Multidomain Electro-Osmostic Model.Entropy (Basel, Switzerland) · 2025Article
- KKANs: Ku̇rková-Kolmogorov-Arnold networks and their learning dynamics.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Article
- Dynamics of brain valves: ostensible rectification mechanisms for cerebrospinal fluid flow.Journal of the Royal Society, Interface · 2025Article
- MR-AIV revealsbioRxiv : the preprint server for biology · 2025Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- Perivascular cerebrospinal fluid inflow matches interstitial fluid efflux in anesthetized rats.iScience · 2025Article
- AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks.Science advances · 2025Article
- Advection and diffusion in perivascular and extracellular spaces in the brain.Journal of the Royal Society, Interface · 2025Article
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance and nutrient delivery, as well as edema in pathological conditions such as stroke. However, existing in vivo techniques are limited to sparse velocity measurements in pial perivascular spaces (PVSs) or low-resolution measurements from brain-wide imaging. Additionally, volume flow rate, pressure, and shear stress variation in PVSs are essentially impossible to measure in vivo. Here, we show that artificial intelligence velocimetry (AIV) can integrate sparse velocity measurements with physics-informed neural networks to quantify CSF flow in PVSs. With AIV, we infer three-dimensional (3D), high-resolution velocity, pressure, and shear stress. Validation comes from training with 70% of PTV measurements and demonstrating close agreement with the remaining 30%. A sensitivity analysis on the AIV inputs shows that the uncertainty in AIV inferred quantities due to uncertainties in the PVS boundary locations inherent to in vivo imaging is less than 30%, and the uncertainty from the neural net initialization is less than 1%. In PVSs of
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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.