Using MR Spectroscopy to Measure Mammalian Neurogenesis in Vivo

使用磁共振波谱测量哺乳动物体内神经发生

基本信息

批准号：
10627832
负责人：
MIRJANA MALETIC-SAVATIC
金额：
$ 77.29万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10627832
关键词：
Address Adolescent Adopted Adult Age Aging Agreement Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Animal Model Area Attention Autopsy Behavioral Biological Markers Brain Bromodeoxyuridine Cells Clinical Data Detection Disease Drops Electroconvulsive Therapy Excision Foundations Goals Hippocampus Human Image Impairment In Vitro Learning Ligands Lipids Machine Learning Magnetic Resonance Spectroscopy Major Depressive Disorder Measurement Measures Memory Mental Depression Methods Modeling Monounsaturated Fatty Acids Moods Mus Natural regeneration Network-based Newly Diagnosed Noise Nuclear Receptors Oleic Acids Pathway Analysis Pattern Recognition Performance Pharmaceutical Preparations Process Proliferating Protocols documentation Reproducibility Resistance Rodent Scanning Sensitivity and Specificity Signal Transduction Site Specificity Spectrometry Statistical Data Interpretation Techniques Time Tissues Validation Wild Type Mouse Work adult neurogenesis age effect aged analytical method biomarker validation computerized data processing convolutional neural network data acquisition deep learning detection method environmental enrichment for laboratory animals gamma-Aminobutyric Acid improved in vivo in vivo magnetic resonance spectroscopy innovation interest learning strategy model organism nerve stem cell neural neural network neurogenesis newborn neuron novel novel strategies repaired response stem stem cell self renewal stem cells tool

项目摘要

PROJECT SUMMARY Since its discovery, adult mammalian hippocampal neurogenesis—particularly human—has attracted attention and controversy. Envisioned as a unique, intrinsic capacity of the brain center for learning and memory and mood control to repair and regenerate, over the past three decades it has been scrutinized in model organisms, which provided a wealth of data confirming its functional relevance. The case for human neurogenesis, however, has faced a much harder road to acceptance because the only means to study it has been by immunostaining of the postmortem tissue. It is thus of no surprise that we know very little about it. While most agree that adult human hippocampus harbors newborn neurons that decline with age and with diseases such as Alzheimer’s, their functional importance has proved elusive without a live and non-invasive measure. As noted in the RFA, developing a means of measuring neurogenesis rates in vivo in a non-invasive manner is of critical interest. Doing so in a technique that can be readily used in not only animal models, but also in humans would give us a vital tool in our effort to not only understand neurogenesis per se, but to also understand how neurogenesis may lead to impairments found in aging and in AD/ADRD. This proposal takes as its foundation the innovation by our group of the first and only in vivo magnetic resonance spectroscopy (MRS)-based marker of neurogenesis: a lipid-based signal resonating at 1.28 ppm. While our group’s initial work in this area has identified and validated this biomarker, several critical gaps exist that must be addressed before it can be widely adopted in human studies of aging and AD/ADRD. Our goal in this proposal is to adapt a number of existing techniques to the accurate measurement and quantification of the 1.28ppm signal and to further develop analytical methods based on deep machine learning so that it can be broadly and reliably used to assess levels of and changes in human adult neurogenesis. In particular, we will: 1) Acquire data from phantoms, in vivo and ex vivo mice, and humans using techniques that will allow for more reliable quantification and validation, 2) Adapt the MEGA-PRESS technique successfully used to quantify GABA to isolate the neurogenic-associated signal at 1.28ppm from the nearby lactate signal; 3) Adapt pre-processing tools we have developed in related studies that enhance signal-to-noise in MRS signals from the hippocampus; 4) Further develop time-domain based processing tools to isolate the neurogenic signal from overlapping components; and 5) Further develop a neural-network based approach to detect and quantify it. In each of these areas, we have existing solutions that are functional, but we believe can be improved upon to provide more reliable and robust quantification of the neurogenic signal. Here, we will formally evaluate the new approaches relative to the existing approaches to produce a final acquisition-through-quantification pipeline that can be used by the field.

项目概要自发现以来，成年哺乳动物——尤其是人类——的海马神经发生引起了人们的关注和争议。被设想为大脑中心的学习和记忆的独特的、内在的能力。情绪控制来修复和再生，在过去的三十年里，它已经在模型中得到了仔细研究生物体，提供了大量数据证实其与人类的功能相关性。然而，神经发生却面临着一条更加艰难的接受之路，因为研究它的唯一手段已经因此，我们对此知之甚少也就不足为奇了。虽然大多数人都认为成年人的海马体含有新生神经元，但这些神经元会随着年龄的增长而衰退对于阿尔茨海默病等疾病，如果没有活体和非侵入性的治疗，其功能重要性已被证明是难以捉摸的措施。正如 RFA 中所指出的，开发一种以非侵入性方式测量体内神经发生率的方法是这样做的技术不仅可以很容易地用于动物模型，而且还可以用于动物模型。人类将为我们提供一个重要的工具，不仅可以帮助我们理解神经发生本身，还可以帮助我们理解神经发生。了解神经发生如何导致衰老和 AD/ADRD 中的损伤。作为其基础，我们团队的创新是第一个也是唯一的体内磁共振波谱基于 MRS 的神经发生标记：基于脂质的信号在 1.28 ppm 处共振，而我们小组的初始信号。该领域的工作已确定并验证了该生物标志物，但存在一些必须解决的关键差距在其广泛应用于衰老和 AD/ADRD 的人类研究之前，我们的目标是适应。多种现有技术可准确测量和量化 1.28ppm 信号，并进一步开发基于深度机器学习的分析方法，使其能够广泛可靠地使用为了评估人类成人神经发生的水平和变化，我们将： 1) 获取数据。模型、体内和离体小鼠以及人类使用的技术将允许更可靠的量化和验证，2) 采用成功用于量化 GABA 的 MEGA-PRESS 技术从附近的乳酸信号中分离出 1.28ppm 的神经源性相关信号 3) 调整预处理；我们在相关研究中开发的工具可以增强海马 MRS 信号的信噪比； 4）进一步开发基于时域的处理工具，以将神经源信号与重叠隔离开来 5) 进一步开发基于神经网络的方法来检测和量化每个组件。在这些领域，我们现有的解决方案是有效的，但我们相信可以改进以提供在这里，我们将正式评估新的神经源信号。与现有方法相关的方法，以产生最终的通过量化获取的管道可以被现场使用。