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Computational comparative anatomy: Translating between species in neuroscience

计算比较解剖学：神经科学中物种之间的翻译

基本信息

批准号：
BB/X013227/1
负责人：
Rogier Mars
金额：
$ 25.76万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FX013227%2F1
关键词：
Computational comparative anatomy Translating between

项目摘要

The complexity of the human brain requires that we study its organization at many different levels: from the low level of the genes guiding its development, through the cells, the connections between cells, the regions of the brain, the networks of regions, up to the entire brain. Many of these levels can only be studied in experimental animals, such as rodents or monkeys, because the techniques required are not suitable for use in living humans. Neuroscience research therefore requires us to combine insights obtained in humans with those obtained in non-human animals. Unfortunately, such between-species 'translations' often fail.The reason many between-species translations are not successful is that we know surprisingly little about how our brains differ from those of the other animals we study. This is particularly striking in the case of the most often-used mammalian study subject, the mouse. Mice are popular experimental animals because they are easy to breed and keep, are clever enough to perform certain tasks, and-and this is increasingly important-there are many genetic variants that can be studied. For neuroscience to benefit society through new insights into brain function, new understandings of disease, and new treatments it is imperative that we better understand how the mouse and human brain relate to one another.In this project, we will use new insights from artificial intelligence to build a first comparative map of the mouse and human brain. We will train an artificial neural network to learn to recognise different areas of the mouse brain based on different types of data used in neuroscience, including genetic data, tissue properties, and connectivity data. The network will learn which types of data are important to identify areas and how each area can be recognised as a unique combination of genetics, tissue, and connections. Then, we will provide the network with the same types of data from the human brain. The network will then be able to determine which areas of the human brain are organized in ways that it has learned from the mouse and which areas of the human brain it cannot understand based on the mouse. In other works, the network will be able to provide us with a full 'map' of how well each part of the human brain relates to each part of the mouse brain.Armed with this network, we will be able to examine a number of outstanding questions about mouse-human brain comparisons. For instance, the prefrontal cortex of the human brain is often identified as impaired in many psychiatric diseases. But it is still a matter of fierce scientific debate whether the mouse brain has a similar type of prefrontal cortex. This raises serious issues as to how well we can study psychiatric diseases using mouse models. Our network-based approach will allow us to study such questions in a completely new way.We will also use our network to test explicitly how well some popular 'mouse models' of disease predict effects on the brains of human patients. We will take four different genetic variants of mice, each of which has been linked to a particular genetic variant in humans. We will study how the brains of these mice have changed relative to healthy controls. Then, using our model, we will predict how the brain of the human patient should look, based on what we found in the mouse model. If our model is capable of predicting how the human patients' brains look, this will provide a first quantitative validation of the mouse model for the brain.Together, our approach will allow us to establish how much we can rely on knowledge obtained from the mouse brain to achieve the ultimate goal of neuroscience: to understand the human brain.

人类大脑的复杂性要求我们在许多不同的层次上研究它的组织：从指导其发展的基因的低层次，通过细胞，细胞之间的连接，大脑的区域，区域网络，直到整个大脑。这些水平中的许多只能在实验动物中进行研究，例如啮齿动物或猴子，因为所需的技术不适合用于活体人类。因此，神经科学研究要求我们将在人类身上获得的见解与在非人类动物身上获得的见解结合起来。不幸的是，这种物种间的“翻译”经常失败。许多物种间的翻译不成功的原因是，我们对我们的大脑与我们研究的其他动物的大脑有何不同所知甚少。这在最常用的哺乳动物研究对象小鼠的情况下尤其引人注目。老鼠是受欢迎的实验动物，因为它们易于繁殖和饲养，聪明到可以完成某些任务，而且越来越重要的是，有许多遗传变异可以研究。为了让神经科学通过对大脑功能的新见解、对疾病的新理解和新治疗方法造福社会，我们必须更好地了解小鼠和人类大脑之间的关系。在这个项目中，我们将利用人工智能的新见解构建小鼠和人类大脑的第一个比较地图。我们将训练一个人工神经网络，根据神经科学中使用的不同类型的数据，包括遗传数据、组织特性和连接数据，学习识别小鼠大脑的不同区域。该网络将了解哪些类型的数据对识别区域很重要，以及如何将每个区域识别为遗传学，组织和连接的独特组合。然后，我们将向网络提供来自人类大脑的相同类型的数据。然后，该网络将能够确定人类大脑的哪些区域是以它从老鼠那里学到的方式组织的，以及人类大脑的哪些区域是它不能基于老鼠来理解的。在其他工作中，该网络将能够为我们提供一个完整的“地图”，显示人脑的每个部分与小鼠大脑的每个部分之间的关系。有了这个网络，我们将能够研究一些关于小鼠-人脑比较的悬而未决的问题。例如，人类大脑的前额叶皮层在许多精神疾病中经常被认为是受损的。但老鼠大脑是否也有类似的前额叶皮层，这仍然是一个激烈的科学争论。这就提出了一个严重的问题，即我们如何利用小鼠模型研究精神疾病。我们基于网络的方法将使我们能够以一种全新的方式研究这些问题。我们还将使用我们的网络来明确测试一些流行的疾病“小鼠模型”对人类患者大脑的影响的预测效果。我们将采用四种不同的小鼠遗传变异，每一种都与人类的特定遗传变异有关。我们将研究这些小鼠的大脑相对于健康对照组是如何变化的。然后，使用我们的模型，我们将根据我们在小鼠模型中发现的结果预测人类患者的大脑应该是什么样子。如果我们的模型能够预测人类患者的大脑外观，这将为大脑的小鼠模型提供第一个定量验证。总之，我们的方法将使我们能够确定我们可以在多大程度上依赖从小鼠大脑获得的知识来实现神经科学的最终目标：了解人类大脑。