WIN aims to bridge the gap between laboratory neuroscience and human health, by exploiting the capacity of neuroimaging to provide measurements that are sensitive to cellular phenomena and that can also be acquired in living humans.
Multi-scale, cross-species studies are essential if insights from neuroscience are to make a meaningful impact on human health. Precise mechanisms discovered in animal models must be related to clinical phenotypes discovered through population studies; both must be combined to improve diagnosis and treatment in individual patients. Neuroimaging offers a powerful route to connect these different scales, providing measurements that are sensitive to cellular phenomena and that can be acquired in living humans and in large populations.
This is achieved by focussing on five themes: Cross-Species Neuroimaging, Cross-Scale Relationships, Population Data Mining, Clinical Markers and Open Neuroimaging.
The ability to record comparable signals and study comparable behaviours across species allows us to address causal and mechanistic questions in animal models and translate these findings directly to humans.
The aim of this theme is to discover cross-species homologies in brain anatomy, functional specialisation and computation. Outputs will include cross-species brain atlases as well as a shared repository of cross-species behavioural paradigms.
This will not only provide fundamental new scientific knowledge but will also enable mechanistic questions that are addressed in rodent and macaque models, to be seamlessly translated to human and clinical populations.
For more information please contact Matthew.Rushworth@psy.ox.ac.uk who is coordinating this theme.
To better understand the brain and tackle brain diseases requires us to integrate between different scales of investigation. For example, to understand how we make decisions we need models of neural networks that can be tested at the single neuron level and then with whole brain recordings using MEG and fMRI. Researchers in the Centre will be developing detailed biophysical models to relate imaging data to cellular and synaptic computations.
Another aspect of this research theme is to improve our understanding of brain structure and connectivity. By deploying sophisticated machine learning techniques, we aim to link known cellular information gained from neuropathology methods, via post-mortem brain imaging to diffusion MRI of living patients.
For more information please contact Timothy.Behrens@ndcn.ox.ac.uk who is coordinating this theme.
Population Data Mining
For cross-species, cross-scale neuroimaging to have the maximum impact it is necessary to identify brain phenotypes that are relevant for human health. Our close involvement in UK Biobank, which will image 100,000 individuals and monitor their long-term health outcomes, will allow us to identify imaging markers that predict disease.
In this theme, we will exploit population data mining techniques to identify patterns in large neuroimaging datasets. Relationships learnt in populations will inform predictions at the level of the individual. For example, for a given question 'will this individual develop dementia?', we will build algorithms that use these patterns to predict clinical outcome in new individuals.
For more information please contact Stephen.Smith@ndcn.ox.ac.uk who is coordinating this theme.
Developing Clinical Markers
Although neuroimaging has delivered major advances in understanding the brain, there remains a disconnect between research developments and translation to the clinic. Combining our experience across a range of neurological and neuropsychiatric disorders, with our integrative imaging approaches, will transform our ability to define relevant clinical markers through both forward and backward translation.
Forward translation will take the fundamental neuroscience insights gained from the animal and human neuroimaging, and use them to identify and assess treatments in the clinic.
Backward translation will use insights from clinical neuroimaging studies, and aim to explain and explore these in rodent models, to give a clearer understanding of the mechanisms of disease.
Finally in this theme, we will interrogate UK Biobank data for markers of disease vulnerability and use these to identify populations for targeted intervention.
Much of this theme will involve interactions with the two NIHR Biomedical Research Centres based in Oxford, both of which include themes led by WIN researchers.
NIHR Oxford BRC, based at the John Radcliffe Hospital, with themes spanning wide areas of medicine
NIHR Oxford Health BRC, based at the Warneford Hospital, with themes focused on brain health.
'Open Science' initiatives aim to make scientific data, code and results openly accessible. This facilitates discovery and hence accelerates the translation of methods and results to the clinic.
One of the key themes of WIN is to increase the openness of our data, to be a leader in the way that data is shared, and importantly, in how tools (such as the FMRIB Software Library), pipelines and databases can be used to ensure that the data shared is useful to other researchers. However, there are some challenges that need to be overcome, practically, ethically and culturally. These are the challenges that we will be working on over the next five years.
All members of the WIN management board are committed to sharing their data openly by the end of the 5 year project, and all Centre members are encouraged to do so.
For more information please contact Clare.Mackay@psych.ox.ac.uk or Mark.Jenkinson@ndcn.ox.ac.uk who are coordinating this theme.