Computational comparative anatomy: Translating between species in neuroscience

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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.