Quantitative translational neuroscience: Bridging preclinical and human neuroscience research

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.