Neural models for prediction of brain activity from fMRI and MEG data sources. [Self-Funded Students Only]

The brain can essentially be regarded as a complex neural network. 

The brain has remarkable predictive capabilities. 

Inspired by models of how the brain works we are interested in for prediction of brain activity from fMRI and MEG data sources.

Independent Component Analysis (ICA) is regularly applied to fMRI and/or MEG data to identify and segment sub-networks and regions. In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation and the classic illustration of the technique is in solving the "cocktail party problem" – many speakers small number of input sources (microphones) decouple the sounds so you can hear individuals (Humans can do this well).

We plan to use ICA (and similar associated techniques such as Principal Component Analysis or PCA) to model brain activity that is recorded from fMRI and MEG sources.

We will evaluate (and subsequently refine) the model by training on one dataset and then testing on an independent dataset. 

Once we have built a satisfactory model we will use it to predict neural activity in one location in the brain given known activation in other brain regions. 

We will use Machine Learning, Deep learning and time series data analysis for this stage.

When the input or tasks demands are changed, discrepancies between predicted activity and measured activity will be used to identify regions and networks involved in processing specific stimuli, for example faces or text. As a second stage we will look to extend the technique to work across modalities – to visual input or between fMRI and MEG.

For more information about this project, please contact Prof David Marshall [Email Address Removed]

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject.  Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas. Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.

How to apply:

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below

This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics 

In order to be considered candidates must submit the following information: 

• Supporting statement 
• CV 
• In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
• Qualification certificates and Transcripts
• Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
• References x 2 
• Proof of English language (if applicable)

If you have any questions or need more information, please contact [Email Address Removed]