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Project Code: CiPE-39624067

Project Description:

It is well-established that impaired cognitive function is a common occurrence reported by women experiencing (peri)menopause, with typical symptoms including forgetfulness; ‘brain-fog’; and difficulties with concentration/focus etc. Emerging evidence suggests that regular participation in physical activity and/or exercise can help to alleviate some of these symptoms. However, most of this existing research has focussed on the assessment of the general self-reported cognitive function, rather than specific functions (e.g. attention; cognitive flexibility). Understanding better the brain changes that take place through menopause and how the affected cognitive functions can be improved is a move forward to improving the quality of life of women in menopause. An important element for understanding the cognitive functions will be brought in by the computational work that will help translate the findings by simulating the proposed changes.

The proposed research program is focusing on understanding the brain changes in menopause and the effect of exercise to improving these functions. We are planning to do this using qualitative and quantitatively methodologies as well as a computational model that simulates changes in attentional functions developed in our lab. The first component of the project will involve co-production, whereby a qualitative approach (i.e., focus groups) will be used to gather insights on how best we can create an intervention that the target population will benefit from and be interested in. The outcome of the co-production will mould the proposed 12-week exercise intervention, where, as above, cognitive function will be assess pre- and post-intervention. The computational model will be used to simulate the changes in the brain function before and after the intervention.

Anticipated Findings:

Through co-production involving focus groups, the study aims to uncover the barriers and enablers related to exercise participation in perimenopausal women. Key findings may include preferences for exercise activities, optimal intensity, frequency, and duration, contributing to the design of a more effective and sustainable intervention. This would be valuable not only for the immediate project, but for future work that is considering designing interventions with the same target population. 

Insights from the intervention data would provide an understanding of which cognitive changes during perimenopause, specifically focusing on attention and flexibility, can be ameliorated with exercise. In addition to the above, the inclusion of computational modelling will create the basis to develop precision medicine approaches for women in menopause, as it could be used in the future as a tool for developing personalised treatment approaches.

Overall, the key findings are expected to contribute to the scientific knowledge of cognitive changes during (peri)menopause, the impact of exercise on cognitive function, and the practical strategies that enhance exercise participation and cognitive well-being in this population. The insights gained could inform future interventions and improve the overall quality of life for women in the perimenopausal and menopausal stages.

Contact (and Director of Studies for this project): Dr Foyzul Rahman, Foyzul.Rahman@bcu.ac.uk

More information for the scheme and application info can be found in the links below

https://www.bcu.ac.uk/student-info/types-of-study/postgraduate/staying-at-bcu/2024-studentships

https://www.bcu.ac.uk/student-info/types-of-study/postgraduate/staying-at-bcu/2024-studentships/projects/improvements-in-cognition-of-women-in-perimenopause

This funding model includes a 36 month fully funded PhD Studentship, in-line with the Research Council values, which comprises a tax-free stipend paid monthly (2024/5 – £19,237) per year and a Full Time Fee Scholarship for up to 3 years, subject to you making satisfactory progression within your PhD. 

All applicants will receive the same stipend irrespective of fee status.

Application Closing Date: 
23:59 on Tuesday 30^th April 2024 for a start date of the 2^nd September 2024.

How to Apply 

To apply, please complete the project proposal form, ensuring that you quote the project reference, and then complete the online application where you will be required to upload your proposal in place of a personal statement as a pdf document. 

You will also be required to upload two references, at least one being an academic reference, and your qualification/s of entry (Bachelor/Masters certificate/s and transcript/s). 

Project Title: Using machine learning, AI and computational modelling to predict recovery from stroke from brain-scans across a range of language and cognitive deficits. 

Project Lead: Professor Eirini Mavritsaki  Eirini.Mavritsaki@bcu.ac.uk

Reference: PreSReB

Project Description

Stroke can be caused when blood flow to the brain is interrupted, this can take place either by blockage or through a traumatic event. Several cognitive deficits can be caused by stroke ranging from paralysis or weakness on one side of the body to problems with memory, attention, vision, thinking, emotional changes and language problems. For the best possible recovery from stroke, the correct rehabilitation is vital. However, predicting the best treatment and outcomes is challenging since every patient is different, and each case of stroke is unique. Machine learning has been previously used, but it is difficult to interpret the outcomes due to complex interactions of the large data used. To overcome these challenges, we are planning to combine machine learning with AI and computational modelling. To achieve the best outcomes, we are collaborating with University of Birmingham and UCL and we have access to the Predicting Language Outcome and Recovery After Stroke (PLORAS) database with over 2000 stroke patients.

Anticipated Findings and Contribution to Knowledge

This research work aims to contribute towards the development of new approaches for predicting stroke recovery. This will not only advance research in this area, but it will also lead to development of new tools that clinicians can use to predict outcomes for rehabilitation approaches. 

In addition, this work will make significant contributions towards the advancement of research in computational neuroscience. By combining machine learning, AI and computational modelling, it will help improve our understanding of this complex field. 

More information on the project can be found using the link below

https://www.bcu.ac.uk/research/our-phds/phd-opportunities/using-machine-learning-ai-and-computational-modelling-to-predict-recovery-from-stroke-from-brain-scans-across-a-range-of-language-and-cognitive-deficits

and the application and process

https://www.bcu.ac.uk/research/our-phds/phd-opportunities