Development of Statistical Shape Model techniques for understanding rodent vision and behaviours.

PhD Research Project   ·   Self Funded

Details

Understanding rodent behaviour is a critical step in basic and preclinical research on conditions such as ocular diseases, Alzheimer’s disease, anxiety and depression. Treatment efficacy is determined by changes in rodent behaviour. To quantify those changes represents an ongoing challenge. Software currently used in academia and industry can only perform low throughput analyses that enable to determine the position of the animals but not what the animal is doing.

In the last few years substantial advances have been made in improving accuracy and resolution of body tracking (e.g. [1]) and behavioural classification (e.g.[2]). Building on some of these results we recently developed a computational technique based on a Statistical Shape Model to reconstruct 3D body poses in freely moving animals [3]. The successful PhD applicant will further develop the mouse Statistical Shape Model and use it to classify the variety of unconstrained mouse behaviours.

Neuronal correlates of such behaviours will also be investigated by simultaneous recordings in freely moving animals. Building on our experience in freely moving recordings [4] and on the high throughput neuropixel system [5] the successful candidate will study the impact of body movements, quantified with a Statistical Shape Model, on visual processing.

1. Mathis 2018, Nat Neurosci, PMID: 30127430
2. Wiltschko 2015, Neuron, PMID: 26687221
3. Storchi 2020, Current Biology, PMID: 33007242
4. Storchi 2017, Neuron, PMID: 28103478
5. Jun 2017, Nature, PMID: 29120427

Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in engineering/physics/computer science/mathematics. Candidates with strong interest and previous experience in vision, computer vision or system neuroscience are encouraged to apply. Good coding skills (MATLAB/Python) are an important requisite.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select PhD Bioinformatics.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Standard Band fee. Details of our different fee bands can be found on our website (https://www.bmh.manchester.ac.uk/study/research/fees/). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/).

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/

References

1. Storchi R., Milosavljevic N., Allen A., Zippo A., Agnihotri A., Cootes T. and Lucas R. A “High-Dimensional Quantification of Mouse Defensive Behaviors Reveals Enhanced Diversity and Stimulus Specificity.” Current Biology, 2020.
2. A.K.Davison, C.Lindner, D.C.Perry, W.Luo and T.F.Cootes, “Landmark Localisation in Radiographs using Weighted Heatmap Displacement Voting”. In Proc. Computational Methods and Clinical Applications in Musculoskeletal Imaging. P.73-85, 2018
3.  Storchi R., Bedford R.A., Martial F.P., Allen A.E., Wynne J., Montemurro M.A., Petersen R.S. & Lucas R.J. “Modulation of Fast Narrowband Oscillations in the Mouse Retina and dLGN According to Background Light Intensity.” Neuron, 2017.

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