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Dr J Zhang
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Magnetoencephalography (MEG) is a brain-imaging technique that measures brain activity from 200~300 sensors near the scalp with millisecond time-resolution. Recently, multivariate pattern analysis (MVPA) for brain-imaging data has been developed, which utilises machine learning methods to distinguish between brain states. MVPA takes into account associations between data dimensions, and offers superior sensitivity than conventional univariate analysis.
However, MVPA for MEG comes with a unique challenge: the high spatiotemporal dimensionality of MEG makes difficult to choose from a large variety of multivariate methods, because the optimal method depends on the underlying structure of data. This project will address this challenge, by providing the first comprehensive comparison of various MVPA approaches on whole-brain simulated and empirical MEG data.
Whole-brain simulation (years 1-2)
We will use an advanced neuroinformatic platform to generate computer-simulated MEG data. Simulation will be based on established neural models, prior knowledge of anatomical connections, and real noise from MEG acquisitions. The simulated data will provide a realistic testbed, of which the ground truth is known. The performance of common MVPA methods will be evaluated on this testbed, under different noise strengths and signal levels (e.g., MEG signals from within the brain or from sensors). New knowledge from this research will provide guidelines for choosing optimal MVPA methods for empirical questions.
Empirical applications (years 2-3)
Outcomes from the simulation will be validated by applying appropriate MVPA methods on two MEG datasets: (1) to distinguish MEG signals from different motor actions, and (2) to distinguish MEG signals from epileptic patients and controls. We will confirm the extent to which the optimal methods, as indicated by the simulation-based research, outperforms other sub-optimal ones. Working on the existing datasets maximizes this project’s feasibility, and will demonstrates the full potential of MVPA in extracting information from MEG for investigating brain functions and disorders.
However, MVPA for MEG comes with a unique challenge: the high spatiotemporal dimensionality of MEG makes difficult to choose from a large variety of multivariate methods, because the optimal method depends on the underlying structure of data. This project will address this challenge, by providing the first comprehensive comparison of various MVPA approaches on whole-brain simulated and empirical MEG data.
Whole-brain simulation (years 1-2)
We will use an advanced neuroinformatic platform to generate computer-simulated MEG data. Simulation will be based on established neural models, prior knowledge of anatomical connections, and real noise from MEG acquisitions. The simulated data will provide a realistic testbed, of which the ground truth is known. The performance of common MVPA methods will be evaluated on this testbed, under different noise strengths and signal levels (e.g., MEG signals from within the brain or from sensors). New knowledge from this research will provide guidelines for choosing optimal MVPA methods for empirical questions.
Empirical applications (years 2-3)
Outcomes from the simulation will be validated by applying appropriate MVPA methods on two MEG datasets: (1) to distinguish MEG signals from different motor actions, and (2) to distinguish MEG signals from epileptic patients and controls. We will confirm the extent to which the optimal methods, as indicated by the simulation-based research, outperforms other sub-optimal ones. Working on the existing datasets maximizes this project’s feasibility, and will demonstrates the full potential of MVPA in extracting information from MEG for investigating brain functions and disorders.
Funding Notes
The studentships will commence in October 2017, and will cover your tuition fees (at UK/EU level) as well as a maintenance grant. In 2016-17 the maintenance grant for full-time students was £14,296 per annum. As well as tuition fees and a maintenance grant, you will receive a participant allowance of £300 per annum, and conference funding (approx. £750 per annum). You will also receive a computer and office space. You will become a member of, and have access to courses offered by the University's Graduate College.
References
Full awards (fees plus maintenance stipend) are open to UK Nationals, and EU students who can satisfy UK residency requirements. To be eligible for the full award, EU Nationals must have been in the UK for at least 3 years prior to the start of the course for which they are seeking funding, including for the purposes of full-time education.
A very high standard of applications is typically received, the successful applicant is likely to have a very good first degree (a First or Upper Second class BSc Honours or equivalent) and/or be distinguished by having relevant research experience.
You can apply online - consideration is automatic on applying for a PhD in Psychology, with an October 2017 start date (programme code RFPDPSYA).
Please use our online application service and specify in the funding section that you wish to be considered for EPSRC funding.
Please specify that you are applying for this particular project. The research project listed above is in competition with 3 projects in the School for the 2016/17 intake. The 2 projects which receives the best applicants will be awarded the funding.
A very high standard of applications is typically received, the successful applicant is likely to have a very good first degree (a First or Upper Second class BSc Honours or equivalent) and/or be distinguished by having relevant research experience.
You can apply online - consideration is automatic on applying for a PhD in Psychology, with an October 2017 start date (programme code RFPDPSYA).
Please use our online application service and specify in the funding section that you wish to be considered for EPSRC funding.
Please specify that you are applying for this particular project. The research project listed above is in competition with 3 projects in the School for the 2016/17 intake. The 2 projects which receives the best applicants will be awarded the funding.
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