Understanding brain dynamics: merging experiments and models. PhD in Mathematics (Funded) Ref: 3058

About the award

Supervisors

Dr. Marc Goodfellow, College of Engineering, Mathematics and Physical Sciences, University of Exeter

Prof. George Augustine, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore

About the Programme:

The University of Exeter (UoE) and Nanyang Technological University (NTU), Singapore are offering six fully funded postgraduate studentships to undertake collaborative research projects at the two institutions, leading to PhD degrees (split-site) to be conferred either by the UoE or NTU.

Students pursuing these postgraduate research projects will benefit from the unique opportunity to conduct their research at both institutions.  Students will be registered at one or other institution, where they will be based for the majority of their time, but will spend at least 12 and not more than 18 months at the partner institution over the duration of the programme.  The frequency and length of stays at each institution will be agreed with successful candidates prior to offers being made.

All six projects are advertised concurrently at both institutions and three will be allocated to each institution after the deadline has passed, based on a collaborative decision made between the UoE and NTU.  The final decision on the successful applicant for each project will be made by the institution hosting the project.  Project allocation will be based on the applicant’s best fit to a project, following a review of applications submitted to each institution.  Applications to undertake the projects at the UoE and NTU are open to all nationalities.

The programme start dates are August 2019 for NTU and September 2019 for UoE

The home institution will determine the regulations that will apply to the successful applicant.  The student’s main supervisor will be based at the home institution.

Project Description:

Healthy brain function is mediated by the coordination of neuronal activity - both locally and across different brain regions - giving rise to large-scale brain dynamics. These dynamics are measured using a variety of techniques, for example magneto-/electro-encephalography or functional MRI in humans, or by fluorescence-based imaging of voltage- or calcium-sensitive indicators in animal models in vivo. Uncovering the nature and mechanisms of large-scale brain dynamics at rest, or during sensory processing, remains a fundamental challenge in neuroscience. In addition to basic insight, improving our understanding of healthy brain dynamics will help us elucidate reasons why abnormal dynamics occur, for example in neurological or neuropsychiatric disorders.

Since brain dynamics emerge and fluctuate in a complex system comprised of many interacting, dynamic components, it is crucial to use mathematical models to assimilate information and to make sense of experimental data. We have very well developed and experimentally validated models and theories for single neurons, but the same cannot be said for models of brain regions. The latter have arisen either from purely theoretical considerations or from results of decades-old experiments, in which electrical stimulation and electrode recordings were used to probe the behaviour of circumscribed regions of tissue. Thus, despite showing promise in applications in health and disease, our models and understanding of large-scale brain dynamics remain rudimentary. However, the last 2 decades have seen significant advances in techniques to probe and observe brain circuits. For example, we can now manipulate and record from specific neuronal populations in vivo with high temporal and spatial resolution, using optogenetics and fluorescent reporters. Thus we are now in a position to improve our basic understanding of brain function by constructing and validating new large-scale theories and models in combination with cutting-edge experimental measurements to test these models.

This studentship will develop a novel program of interdisciplinary research across in vivo (mouse) experimentation and mathematical modelling. The overall aim is to construct and validate mathematical models of large-scale brain dynamics that are able to explain the spontaneous activity of the rodent brain in vivo. The student will train in optogenetic technologies and in vivo imaging, as well as mathematical model development, multi-variate time series analysis and parameter fitting tools, thus placing them at the forefront of interdisciplinary neuroscience. In a first step, targeted optogenetic stimuli will be combined with voltage sensitive dye imaging in awake mice to probe the response of brain tissue to excitatory and inhibitory afferent stimuli. Neural mass models will be fit to these data, using machine learning approaches (for example random forests). This information will be compiled into a predictive model of cortical dynamics and tested against experimental recordings of spontaneous activity. Importantly, we will test models constructed at different spatial resolutions; uncovering the best fitting model will add novel information regarding the optimal spatial scale at which brain operates. We will extend this analysis to different brain states, including processing of whisker stimuli, so that we can elucidate basic mechanism of information processing at different spatial scales.

Project-specific queries should be directed to Dr Marc Goodfellow, m.goodfellow@exeter.ac.uk

Entry requirements

Successful applicants will need a good first degree (preferably 1st Class Honours and at least an upper Second class honours, or international equivalent) in a relevant field.  Applicants with a Lower Second Class honours degree may be considered if they also have a Master's degree in a relevant field.

Candidates applying to the University of Exeter for whom English is not their first language will also need to satisfy our English language entry requirements, prior to commencing the programme.

How to apply

Please read all of the information below on how to apply to the UoE prior to submitting an application for this project.

Note enquiries about NTU's application process should be emailed to gradprog_LKCMedicine@ntu.edu.sg

In order to apply for funding for this studentship, you must click the 'How to Apply' button on this page.

In the application process you will be required to upload several documents as detailed below;
• CV
• Cover Letter
• 2 References
• Transcripts
• IELTS or equivalent (if from a non-English speaking country)

Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”

The closing date for applications is 31 January 2019.  Interviews are anticipated to be held February 2019, date to be confirmed.

If you have any general enquiries about the application process please email pgrenquiries@exeter.ac.uk

Please quote reference 3058 on your application and in any correspondence about this studentship.

Reference information
References should be submitted to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

It is your responsibility to ensure that your two referees email their references to pgrenquiries@exeter.ac.uk, as we will not make requests for references directly; you must arrange for them to be submitted by 31 January 2019.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

Data sharing
During the application process, the University of Exeter may need to make certain disclosures of your personal data to Nanyang Technological University to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

  •     The selection panel and/or management board or equivalent of the relevant programme, which is
         likely to include staff from NTU.
  •     Administrative staff at NTU related to the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.

 

Summary

Application deadline:31st January 2019
Value:3 year studentship funding covering tuition fees and an annual maintenance allowance equivalent to research council rates. The maintenance allowance for 2018/19 is 14,777 per annum.
Duration of award:per year
Contact: PGR Enquiries pgrenquiries@exeter.ac.uk