Using MR Spectroscopy to Measure Mammalian Neurogenesis in Vivo

项目总结 自从它被发现以来，成年哺乳动物的海马神经发生--尤其是人类--引起了人们的注意。 和争议。被认为是大脑学习和记忆中心独特的内在能力 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的研究 生物体，提供了大量数据证实其功能相关性。以人为本 然而，神经发生学面临着一条艰难得多的接受之路，因为研究它的唯一手段 通过死后组织的免疫染色。因此，我们对它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中有新生神经元，但随着年龄的增长，神经元数量会下降 像阿尔茨海默氏症这样的疾病，如果没有活的和非侵入性的，它们的功能重要性被证明是难以捉摸的 测量。 正如RFA中指出的，开发一种以非侵入性方式测量体内神经形成率的方法是 非常重要的问题。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 人类将为我们提供一个重要的工具，让我们不仅了解神经发生本身，而且还 了解神经发生如何导致衰老和AD/ADRD中的损害。这项提议需要 作为它的基础，我们团队的创新是第一个也是唯一的活体磁共振波谱 (MRS)-基于神经发生的标记：一种基于脂质的信号，在1.28ppm共振。虽然我们组的首字母是 这一领域的工作已经确定并验证了这一生物标志物，存在着几个必须解决的关键差距 在它被广泛应用于人体衰老和AD/ADRD研究之前。我们在这项提案中的目标是适应 一些现有技术用于准确测量和量化1.28ppm信号以及 进一步发展基于深度机器学习的分析方法，使其能够广泛和可靠地应用 评估人类成年神经发生的水平和变化。特别是，我们将：1)从以下位置获取数据 幻影、体内和体外小鼠和人类使用的技术将允许更可靠的 定量和验证，2)将成功用于定量GABA的超级压力技术应用于 从附近的乳酸信号中分离出1.28ppm的神经源性相关信号；3)适配预处理 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)进一步开发基于时间域的处理工具，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。在每一个中 在这些领域，我们现有的解决方案是可行的，但我们相信可以改进以提供 更可靠和稳健的神经源性信号的量化。在这里，我们将正式评估新的 与现有方法相关的方法，以产生最终的通过量化获得的管道 它可以被现场使用。

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.