| Autor: |
Boster KAS; Department of Mechanical Engineering, University of Rochester, Rochester, NY 14627., Cai S; Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China., Ladrón-de-Guevara A; Center for Translational Neuromedicine and Department of Neuroscience, University of Rochester Medical Center, Rochester, NY 14627., Sun J; Department of Mechanical Engineering, University of Rochester, Rochester, NY 14627., Zheng X; Department of Mathematics, College of Information Science and Technology, Jinan University, Guangzhou 510632, China., Du T; Center for Translational Neuromedicine and Department of Neuroscience, University of Rochester Medical Center, Rochester, NY 14627.; School of Pharmacy, China Medical University, Shenyang, Liaoning 110122, China., Thomas JH; Department of Mechanical Engineering, University of Rochester, Rochester, NY 14627., Nedergaard M; Center for Translational Neuromedicine and Department of Neuroscience, University of Rochester Medical Center, Rochester, NY 14627., Karniadakis GE; Division of Applied Mathematics, Brown University, Providence, RI 02912.; School of Engineering, Brown University, Providence, RI 02912., Kelley DH; Department of Mechanical Engineering, University of Rochester, Rochester, NY 14627. |
| Abstrakt: |
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 N = 4 wild-type mice we find mean flow speed 16.33 ± 11.09 µm/s, volume flow rate 2.22 ± 1.983 × 10 3 µm 3 /s, axial pressure gradient ( - 2.75 ± 2.01)×10 -4 Pa/µm (-2.07 ± 1.51 mmHg/m), and wall shear stress (3.00 ± 1.45)×10 -3 Pa (all mean ± SE). Pressure gradients, flow rates, and resistances agree with prior predictions. AIV infers in vivo PVS flows in remarkable detail, which will improve fluid dynamic models and potentially clarify how CSF flow changes with aging, Alzheimer's disease, and small vessel disease. |
