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Brain connectivity and networks as the basis of human hemispheric language dominance

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  • Full or part time
    Dr Simon Keller
    Dr Peter Taylor
    Dr L Bonnett
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

This exciting PhD studentship will focus on the application of in-vivo neuroimaging techniques to determine the structural basis of brain hemispheric language dominance (HLD) in healthy people. Up to 90% of right-handed and 75% of left-handed people have left cerebral dominance for language. The reasons why we have such lateralised brain function are not known. There have been limited insights into the anatomical basis of HLD through quantification of macroscopic brain features using MRI techniques (e.g. gyral asymmetry, cortical volume and thickness). Brain function and human cognition is likely to be governed, at least in part, by the microstructural environment of the brain, brain connectivity and brain networks. The main aim of this project will be to determine whether structural and functional brain connectivity and networks are related to, and can predict, the side of HLD.

This project will use data acquired from the Human Connectome Project (https://www.humanconnectome.org/). People with left, bilateral and right HLD will be identified based on task-based language functional MRI data. Structural and functional connectomes (network matrices) will be computed from diffusion tensor imaging and resting-state functional MRI data, respectively, and investigated with respect to HLD using classifier techniques. Behavioural data will also be obtained to investigate the potential relationships between side of HLD and cognition.

You will have at least a 2.1 in a relevant undergraduate degree programme (e.g. Biological Sciences, Anatomy, Psychology, Computer Science, Engineering). Experience of neuroimaging research in context of a postgraduate degree course is desirable. Full training will be provided in relevant image analysis, network science and predictive modelling. This studentship will be based primarily at the University of Liverpool and will include training visits to the University of Newcastle.

HOW TO APPLY
Applications should be made by emailing [Email Address Removed] with a CV (including contact details of at least two academic (or other relevant) referees), and a covering letter – clearly stating your first choice project, and optionally 2nd and 3rd ranked projects, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University. Applications not meeting these criteria will be rejected.
In addition to the CV and covering letter, please email a completed copy of the Additional Details Form (Word document) to [Email Address Removed]. A blank copy of this form can be found at: https://www.nld-dtp.org.uk/how-apply.
Informal enquiries may be made to: [Email Address Removed]

Funding Notes

This is a 4 year BBSRC studentship under the Newcastle-Liverpool-Durham DTP. The successful applicant will receive research costs, tuition fees and stipend (£15,009 for 2019-20). The PhD will start in October 2020. Applicants should have, or be expecting to receive, a 2.1 Hons degree (or equivalent) in a relevant subject. EU candidates must have been resident in the UK for 3 years in order to receive full support. Please note, there are 2 stages to the application process.

References

A guide to presenting clinical prediction models. British Medical Journal 2019 365:1737

A voxel-based asymmetry study of the relationship between hemispheric asymmetry and language dominance in Wada tested patients. Human Brain Mapping 2018 39(7):3032- 3045

The impact of epilepsy surgery on the structural connectome and its relation to outcome. NeuroImage: Clinical 2018 18:202-214

Preoperative automated white matter fibre quantification predicts postoperative seizure outcome in refractory TLE. Brain 2017 140:68-82

Dynamic classification using credible intervals in longitudinal discriminant analysis. Statistics in Medicine 2017 36(24):3858-3874

Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain 2016 140: 319-332

Predicting surgery targets in temporal lobe epilepsy through structural connectome based simulations. PLoS Computational Biology 2015 11(12):e1004642.

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. Neuroimage 2015 118: 219-230

Can the language dominant hemisphere be predicted by brain anatomy? J Cog Neurosci 2011 23:2013-2029

Seizure Recurrence After Antiepileptic Drug Withdrawal and the Implications for Driving: Further results from the MRC Antiepileptic Drug Withdrawal Study and a Systematic Review. Journal of Neurology, Neurosurgery and Psychiatry 2011 82:1328-1333



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