CRCNS: Waste-clearance flows in the brain measured using physics-informed neural network

CRCNS：使用物理信息神经网络测量大脑中的废物清除流量

• 批准号: 10706594
• 负责人: Douglas H Kelley
• 金额: $ 32.88万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706594
• 关键词: 3-Dimensional; Address; Alzheimer' s Disease; Artificial Intelligence; Biology; Blood Vessels; Brain; Brain imaging; Brain region; Cephalic; Cerebrospinal Fluid; Clinical; Communities; Contrast Media; Data; Educational process of instructing; Engineering; Equation; Failure; Future; Health; High School Student; Human; Image; Intercellular Fluid; Link; Liquid substance; Magnetic Resonance Imaging; Measurement; Measures; Methods; Modality; Morphologic artifacts; Mus; Nature; Neurologic; Neurosciences; Noise; Pathologic; Pathologic Processes; Physics; Postdoctoral Fellow; Rehabilitation therapy; Research Personnel; Resolution; Science; Series; Sleep; Sorting; Speed; Stroke; System; Techniques; Training; Traumatic Brain Injury; Variant; Work; contrast enhanced; data-driven model; experience; fluid flow; glymphatic flow; glymphatic function; glymphatic system; high risk; imaging modality; improvement on sleep; in vivo; in vivo imaging; invention; neural network; novel; outreach; particle; pressure; spatiotemporal; two-dimensional; two-photon; wasting

The brain’s transport system for cerebrospinal and interstitial fluid, the glymphatic system, was first described in 2012 by the Nedergaard team, operates primarily during sleep, and has been linked to pathological neurological conditions including Alzheimer’s disease, traumatic brain injury (TBI), and stroke. Obtaining quantitative measurements of glymphatic fluid velocity and pressure is crucial to understanding the function, failures, and potential rehabilitation of the glymphatic system. However, existing techniques for obtaining in vivo glymphatic velocities are limited to sparse measurements and specific regions, and pressure variation is essentially impossible to measure in vivo. We propose to quantify glymphatic flows from measurements of tracked particles and contrast agents using physics-informed neural networks (PINNs), which can infer velocity and pressure from sparse measurements and have not been used previously in neuroscience. We will adapt PINNs for three commonly-employed glymphatic imaging modalities: two-photon perivascular space imaging, transcranial whole-brain imaging, and dynamic contrast-enhanced magnetic resonance imaging (DCEMRI). For these modalities, each of which can probe different regions and scales of glymphatic flows, we will adapt the PINNs equations and artificial intelligence hyperparameters, evaluate the sensitivity of the approach to noise, spatiotemporal resolution, and imaging artifacts using synthetic data, and validate by comparing velocities inferred by PINNs to velocities from alternative techniques. Using PINNs will allow us to obtain in vivo velocity and pressure measurements of cerebrospinal fluid in previously unmeasured regions of the brain. Our collaborative team of neuroscientists, fluid dynamicists, and applied mathematicians includes the leaders who discovered the glymphatic system and invented PINNs. Moreover, we have extensive experience with all three imaging modalities and with velocity measurement (via automated particle tracking and front tracking) in glymphatic flows. This proposal seeks to reveal mechanisms by which the brain's transport system for cerebrospinal and interstitial fluid operates. Our novel velocity and pressure measurements of intracranial cerebral spinal fluid flows may demonstrate how improving sleep, the state during which the glymphatic system primarily operates, can counteract pathological processes related to glymphatic system failure including Alzheimer's disease.

大脑的脑脊髓和间质运输系统 液体，即类淋巴系统，由 Nedergaard 团队于 2012 年首次描述，主要运作 睡眠期间，并与包括阿尔茨海默氏病在内的病理性神经系统疾病有关， 获得脑外伤 (TBI) 和中风的定量测量。 压力对于理解系统的功能、故障和潜在的恢复至关重要。 然而，现有的获取体内类淋巴速度的技术仅限于。 稀疏测量和特定区域，并且压力变化基本上无法测量 我们建议通过跟踪粒子和对比度的测量来量化类淋巴流量。 使用物理信息神经网络（PINN）的智能体，可以根据以下信息推断速度和压力 稀疏测量并且之前没有在神经科学中使用过，我们将调整 PINN。 三种常用的类淋巴成像方式：双光子血管周围空间成像， 经颅全脑成像和动态对比增强磁共振成像（DCEMRI）。 对于这些模式，每种模式都可以探测类淋巴流的不同区域和规模，我们 将调整 PINN 方程和人工智能超参数，评估 使用合成数据处理噪声、时空分辨率和成像伪影的方法，并通过以下方式进行验证 将 PINN 推断的速度与使用 PINN 得出的速度进行比较将允许。 我们获得以前未测量过的脑脊液的体内速度和压力测量值 我们的合作团队由神经科学家、流体动力学家和应用专家组成。 数学家包括发现类淋巴系统和发明 PINN 的领导者。 此外，我们在所有三种成像模式和速度方面拥有丰富的经验 类淋巴流中的测量（通过自动粒子跟踪和前沿跟踪）。 该提案旨在揭示大脑的脑脊髓和神经运输系统的机制。 我们对颅内大脑脊髓的速度和压力进行了新颖的测量。 液体流动可能表明如何改善睡眠，即类淋巴系统的状态 主要发挥作用，可以抵消与类淋巴系统衰竭相关的病理过程，包括 阿尔茨海默病。

项目成果

Dysregulation of extracellular potassium distinguishes healthy ageing from neurodegeneration.

细胞外钾的失调将健康衰老与神经变性区分开来。

• 发表时间: 2024-05-03
• 作者: Ding, Fengfei;Sun, Qian;Long, Carter;Rasmussen, Rune Nguyen;Peng, Sisi;Xu, Qiwu;Kang, Ning;Song, Wei;Weikop, Pia;Goldman, Steven A;Nedergaard, Maiken
• 通讯作者: Nedergaard, Maiken