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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.

人脑的复杂性要求我们在许多不同的层面上研究它的组织：从指导大脑发育的低水平基因，到细胞，细胞之间的联系，大脑的区域，区域网络，直到整个大脑。其中许多水平只能在实验动物(如啮齿动物或猴子)中进行研究，因为所需的技术不适合在活人身上使用。因此，神经科学研究要求我们将在人类身上获得的洞察力与在非人类动物身上获得的洞察力结合起来。不幸的是，这种物种间的“转换”常常失败。许多物种间的转换之所以不成功，是因为我们对自己的大脑与我们研究的其他动物的大脑有何不同，知之甚少。在最常用的哺乳动物研究对象--老鼠身上，这一点尤其引人注目。老鼠是受欢迎的实验动物，因为它们易于繁殖和饲养，足够聪明，可以执行某些任务，而且--这一点越来越重要--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的治疗方法来造福社会，我们必须更好地了解老鼠和人脑是如何相互联系的。在这个项目中，我们将使用人工智能的新见解来构建第一张老鼠和人脑的比较图。我们将训练人工神经网络，根据神经科学中使用的不同类型的数据，包括遗传数据、组织属性和连接数据，学习识别小鼠大脑的不同区域。该网络将学习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、组织和连接的独特组合。然后，我们将向网络提供来自人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习到的方式组织的，以及它不能理解基于老鼠的人类大脑的哪些区域。在其他工作中，该网络将能够为我们提供人类大脑的每一部分与老鼠大脑的每一部分有多好的联系的完整“地图”。有了这个网络，我们将能够研究一些关于老鼠和人类大脑比较的悬而未决的问题。例如，在许多精神疾病中，人类大脑的前额叶皮质经常被认为是受损的。但老鼠的大脑是否有类似类型的前额叶皮质，仍然是一个激烈的科学辩论问题。这就提出了一个严重的问题，即我们可以在多大程度上利用老鼠模型来研究精神疾病。我们的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。我们还将利用我们的网络来明确测试一些流行的“老鼠模型”对人类患者大脑的预测效果。我们将采用四种不同的老鼠基因变体，每一种都与人类的一种特定基因变体相关联。我们将研究与健康对照组相比，这些老鼠的大脑发生了怎样的变化。然后，使用我们的模型，我们将根据我们在老鼠模型中的发现，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大脑的样子，这将为大脑提供第一个老鼠模型的定量验证。此外，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老鼠大脑获得的知识来实现神经科学的最终目标：了解人脑。