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  Model-predictive control of brain plasticity for optimal non-invasive brain stimulation


   Faculty of Biology, Medicine and Health

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  Dr N Trujillo-Barreto, Dr Caroline Lea-Carnall  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Neuroplasticity is the mechanism that underpins the brain’s ability to learn or recovery from traumatic events, but also its deterioration underlies several Neurodegenerative diseases and mental illnesses. It is an umbrella term that encompasses multiple processes occurring at different spatial and temporal scales (1, 2). The most commonly discussed type of plasticity is long-term potentiation (LTP) which is defined as an increase in efficacy between synapses of two neurons. Experiments designed to measure LTP are often done in cell slices or cultures using patch-clamp techniques. However, we often want to promote (and measure) plasticity in humans in-vivo  but it is not clear how changes in synaptic efficacy manifest in macroscopic non-invasive imaging measurements such as magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), or electroencephalography (EEG) that are available for use in humans.

This project aims to link our detailed cellular-level knowledge of plasticity processes with systems-level observations in humans and is focussed on answering the following 2 questions:

1. How can we modulate plasticity non-invasively in the brain?

2. How can we measure plasticity in vivo?

To answer these questions you will combine mathematical/computational modelling of biophysically realistic large-scale neural networks that exhibit plasticity with cutting edge neuroimaging techniques to test the model predictions. The model will be adapted so that its output can be compared to the three major imaging techniques described above and you will determine how changes in connectivity between and within brain regions in the model predict changes in our imaging metrics. There will be a particular focus on developing an EEG marker of plasticity as this currently does not exist. Finally, you will test the model predictions in an imaging study combining brain stimulation, EEG, MRI and MRS in a proof-of-concept experiment in humans.

This will provide us with a deeper understanding of how to modulate and measure plasticity that can be deployed in the future for medical applications of brain stimulation such as in Parkinson’s disease, depression and pain control. 

Training/techniques to be provided

Mathematical modelling

Computational modelling

Neural networks

EEG - electroencephalography

MRI – magnetic resonance imaging

MRS – magnetic resonance spectroscopy

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area/subject. Candidates with previous maths/physics experience are particularly encouraged to apply.

How to Apply

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 the PhD Neuroscience.

Equality, Diversity & Inclusion

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/

Biological Sciences (4) Computer Science (8) Mathematics (25) Medicine (26) Physics (29)

References

1. Mateos-Aparicio P and Rodríguez-Moreno A (2019) The Impact of Studying Brain Plasticity. Front Cell Neurosci 13:66. doi: 10.3389/fncel.2019.00066.
2. Zenke, F., & Gerstner, W. (2017). Hebbian plasticity requires compensatory processes on multiple timescales. Trans R Soc B, 372. doi:10.1098/rstb.2016.0259.

 About the Project