Quantitative translational neuroscience: Bridging preclinical and human neuroscience research

定量转化神经科学：连接临床前和人类神经科学研究

• 批准号: MR/Y010698/1
• 负责人: Rogier Mars
• 金额: $ 213.86万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY010698%2F1
• 关键词: Quantitative; translational; neuroscience; Bridging; preclinical

Therapies for neuropsychiatric and neurodevelopmental disorders often fail to make the transition from successful preclinical trials to real world efficacy. This is for a large part due to the lack of good animal models for these diseases, which often target uniquely human cognitive and neural processes. These problems are especially prevalent in the case of the mouse model. Mice are a popular model species, since we understand much of their genetics and are able to manipulate it. However, mouse brains are smaller and differently organized from ours. How different the brains of the mouse and human are exactly and how this influences translations of results from one species to the other remains largely unknown.Solving this problem requires new tools that allow us to directly compare the organization of the mouse and human brain. Traditionally, this is difficult to do due to the vast amounts of data required and the fact that the mouse and human brain are of such different size and shape that it's difficult to find a reference frame. We have pioneered an approach to solve this issue, which we term the 'common space approach'. In effect, we use high-throughput, whole-brain data from both species and then describe the different brains in terms of an abstract feature space consisting of common features that can be identified in both brains. For instance, we might describe the brains in terms of which genes are expressed in any given area. If we select genes that are shared by mice and humans, this will allow us to describe both brains in the same 'gene space'. This is a simple but extremely powerful way to directly compare brain organization across species.Here, we will use this approach to understand:(1) how similar each part of the mouse and human brain are. New, openly available, high quality gene expression and MRI data from both the mouse and the human allow us to compare the brains using the same type of data for the two species. Our common space approach allows us to determine how similar each part of the mouse brain is to each part of the human brain.(2) which aspects of the human brain we cannot understand based on the mouse. If we can describe the two brains in the same space, we can also assess which parts of the human brain are very distinct from the mouse, at any level of brain organization. This, in effect, shows us the limit of the mouse-human translation. (3) whether there are any systematic rules that predict whether a certain neurological or psychiatric disease can be understood using a mouse model. Though large international consortia, datasets of brain changes in a range of diseases are now available. We can combine them with our limits-of-translation data to see if there are certain predictors that indicate whether a disease can be successfully modelling in the mouse. We will then test these predictors by comparing mice models of certain conditions with human patient data from the same conditions.(4) develop a way to better relate specific mouse models of disease with specific patients. Many psychiatric disorders have not one mouse model, but many--each with a high construct validity for a very small aspect of the disease. To optimize successful translation, it would be beneficial to match specific mouse strains to specific patients. Our common space approach will allow us to do this, saving on the amount of work and the number of animals needed for translational research.This research will mostly be based at the University of Oxford, but will benefit from collaborators across the world. Large consortia have started mapping out brain changes in a range of diseases and have started to collect large amounts of human and non-human imaging data. However, to date such consortia never bridged the gap between preclinical animal research and human clinical research. This project will break those silos, building a new quantitative framework for translational neuroscience.

神经精神病学和神经发育障碍的疗法通常无法使从成功的临床前试验过渡到现实世界的功效。这是由于这些疾病缺乏良好的动物模型，这通常是针对人类独特的人类认知和神经过程。在小鼠模型的情况下，这些问题尤其普遍。小鼠是一种流行的模型物种，因为我们了解它们的许多遗传学并能够操纵它。但是，小鼠大脑与我们的大脑较小，并且与我们的大脑有所不同。小鼠和人类的大脑完全不同，这如何影响从一个物种到另一物种的结果翻译的翻译。解决此问题需要新工具，使我们能够直接比较小鼠和人脑的组织。传统上，由于所需的大量数据以及老鼠和人的大脑的大小和形状如此不同，因此很难找到参考框架，因此很难做到这一点。我们开创了一种解决此问题的方法，我们将其称为“共同空间方法”。实际上，我们使用来自两个物种的高通量，全脑数据，然后用抽象特征空间来描述不同的大脑，该抽象特征空间由共同特征组成，这些特征可以在两个大脑中识别出来。例如，我们可以用在任何给定区域中表达的基因来描述大脑。如果我们选择小鼠和人类共享的基因，这将使我们能够描述同一“基因空间”中的两个大脑。这是一种直接比较跨物种的大脑组织的简单但非常有力的方法。在这里，我们将使用这种方法来理解：（1）小鼠和人脑的每个部分的相似性。新的，公开可用的高质量基因表达和来自小鼠和人类的MRI数据使我们能够使用这两个物种的相同类型的数据比较大脑。我们的公共空间方法使我们能够确定小鼠大脑的每个部分与人脑的每个部分相似。（2）基于小鼠无法理解的人脑的哪个方面。如果我们能在同一空间中描述两个大脑，我们还可以评估人脑的哪些部分与小鼠，在任何级别的大脑组织中都非常不同。实际上，这向我们展示了小鼠人类翻译的极限。 （3）是否有任何系统的规则可以预测使用小鼠模型可以理解某种神经系统或精神病。尽管大型国际财团，但现在可以使用一系列疾病的大脑变化数据集。我们可以将它们与我们的翻译数据相结合，以查看是否有某些预测因素表明疾病是否可以在小鼠中成功进行建模。然后，我们将通过将某些疾病的小鼠模型与来自相同条件的人类患者数据进行比较来测试这些预测因子。（4）开发一种方法来更好地将特定的疾病小鼠模型与特定患者联系起来。许多精神疾病没有一个小鼠模型，而是许多人 - 构造有效性很小，对于疾病的一小部分。为了优化成功的翻译，将特定的小鼠菌株与特定患者匹配是有益的。我们的通用空间方法将使我们能够做到这一点，节省了翻译研究所需的动物数量。这项研究主要基于牛津大学，但将从世界各地的合作者中受益。大型财团已经开始绘制一系列疾病的大脑变化，并开始收集大量的人类和非人类成像数据。但是，迄今为止，这种财团从未弥合临床前动物研究与人类临床研究之间的差距。该项目将打破那些孤岛，为转化神经科学建立新的定量框架。