Using MR Spectroscopy to Measure Mammalian Neurogenesis in Vivo

项目摘要 自从发现以来，成年哺乳动物海马神经发生--特别是人类--引起了人们的关注 和争议。被认为是大脑学习和记忆中心的一种独特的内在能力， 情绪控制修复和再生，在过去的三十年里，它一直在模型中仔细研究 生物，提供了丰富的数据，证实其功能相关性。人类案例 然而，神经发生面临着一条更难接受的道路，因为研究它的唯一方法 是通过死后组织的免疫染色发现的因此，我们对它知之甚少也就不足为奇了。 虽然大多数人认为成年人海马体中的新生神经元会随着年龄的增长而下降， 老年痴呆症等疾病，其功能的重要性已被证明是难以捉摸的，没有一个活的和非侵入性的 measure. 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， 关键利益。这样做的技术不仅可以很容易地用于动物模型，而且可以用于 人类将为我们提供一个重要的工具，不仅有助于我们理解神经发生本身， 了解神经发生如何导致衰老和AD/ADRD中发现的损伤。这项提议需要 作为它的基础，我们的第一个也是唯一一个体内磁共振光谱的创新 基于MRS的神经发生标记物：在1.28 ppm处共振的基于脂质的信号。虽然我们的团队最初 该领域的工作已经确定并验证了这种生物标志物，但存在一些必须解决的关键差距 才能被广泛用于人类衰老和AD/ADRD研究。我们在这个提案中的目标是 许多现有技术可以精确测量和量化1.28ppm信号， 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 评估人类成年神经发生的水平和变化。具体而言，我们将：1）从 使用将允许更可靠的生物测定的技术， 定量和验证，2）采用MEGA-PRESS技术成功地用于定量GABA， 从附近的乳酸信号中分离出1.28ppm处的神经源性相关信号; 3）适应预处理 我们在相关研究中开发的工具可以增强来自海马体的MRS信号的信噪比; 4)进一步开发基于时域的处理工具，以将神经源性信号从重叠中分离出来 5）进一步开发基于神经网络的方法来检测和量化它。 在这些领域，我们有功能性的现有解决方案，但我们相信可以进行改进， 神经源性信号的更可靠和鲁棒的量化。在这里，我们将正式评估新的 相对于现有方法的方法，以产生最终的通过量化获取管道 可供现场使用。

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.