Round 5

Principal Investigator: Andrew Quinn
Co-Investigators: Barbara Pomiechowska and Rickson Mesquita
Centre Fellow: Tommy Clausner
Artist: Andee Collard

Every day we learn and do hundreds of different behaviours (e.g. brew a cup of coffee, catch the train, buy groceries, go to a pottery class). How does the human brain make such complicated behaviour? How does the growing brain of young children learn this wide variety of behaviours?  

We do not know much about how the brain controls natural behaviour and how this changes as we get older. This is partly because brain research studies grown-ups making single movements that are well controlled but that are nothing like the real world.  

Our project will help by looking at how the brain creates movements in real-world situations in both infants and adults. We will look at brain activity and video data from babies playing and from adults speaking in public. We will write computer programmes that can detect people moving in the videos and try to predict what movements will happen next based on what the brain is doing. 

This research will help children's healthcare by helping to find conditions (e.g. autism, ADHD) earlier and by tailoring specific treatments for affected infants and children. Our results could also be used to make neuroimaging-based rehabilitation therapies for people of any age.

 

Left to right: Andee Collard, Tommy Clausner & Rickson Mesquita.

Principal Investigator: Pranav Vasanthi Bathrinarayanan
Co-Investigators: Xiaocheng Shang and Ansar Mahmood
Centre Fellow: Sandeep Shirgill
Artist: Tom Ellis

ACS is an emergency medical condition in which a high-impact trauma such as a fracture or crush injury causes pressure build-up within the limb which obstructs blood flow to the muscles of the limb. As the limb is dying slowly due to lack of oxygen and nutrients, the surgeon is attempting to diagnose the condition accurately. Due to lack of objective methods, clinicians depend upon subjective methods of diagnosis such as pain experienced by the patient upon stretching the affected limb. Such subjective assessments can be difficult even for experienced surgeons and can have serious consequences to patients such as having them to undergo a highly invasive surgery as a result of misdiagnosis or overdiagnosis. The current project aims to develop ML-driven ACS diagnostic tools that will help clinicians make informed decisions. We aim to develop ML models based on the ACS patient data that we have obtained in order to decipher the most sensitive/specific parameters that will aid in accurate diagnosis. We then aim to test the accuracy of our ML models against the current diagnostic methods. Finally, we aim to develop the ML models into a web application for wider dissemination among surgeons.

Left to right: Tom Ellis, Pranav Vasanthi Bathrinarayanan, Sandeep Shirgill & Xiaocheng Shang.

Principal Investigator: Paul Muhle-Karbe
Co-Investigators: Jianbo Jiao
Centre Fellow: Daniel Galvis
Artist: Alexandra Davenport

Working memory describes the ability to remember and manipulate information that is no longer available in the environment. It provides a flexible mental workspace that scaffolds most higher mental functions. A key feature of human working memory is its limited capacity: we can only hold on to a small amount of information at any given moment. To make the most of this precious resource, a mechanism is needed that rapidly removes memories when they are no longer needed. Remarkably, however, humans often struggle with this. Difficulties in goal-directed forgetting are particularly have been linked with the risk of developing some forms of mental illness and may also contribute to age-related memory deficits. However, the mechanisms that underpin directed forgetting remain unknown. 

To fill this gap, we plan to study how artificial neural networks from machine learning form and remove memories in the same tasks that are used to study humans. These networks provide a unique window to reveal information processing mechanisms, as the activity of every single neuron is known and can be manipulated. We will leverage this virtue to reveal how neural circuits implement directed forgetting and develop testable new predictions for aberrant memory removal in mental illness.

Left to right: Daniel Galvis, Alexandra Davenport, Paul Muhle-Karbe & Jianbo Jiao.

Principal Investigator: Ali Khatibi
Co-Investigators: Abhirup Ghosh, Jack Rogers, Rachel Upthegrove and Saiju Jacob
Centre Fellow: Yingjing Feng
Artist: Christie Swallow

 

Psychotic disorders are costly conditions, with billions spent on healthcare and over £18 billion annual economic impact in the UK. Early diagnosis improves recovery and reduces burden, but psychosis patients present diverse symptoms, characteristics, and treatment responses. Classifying patients into subgroups enables identifying tailored treatment options to improve outcomes. 

This project aims to predict which high-risk individuals will develop psychosis by analysing blood biomarkers combined with brain imaging data. We also seek to understand factors contributing to variability among psychosis patients to facilitate earlier intervention and personalised treatments. 

Our analysis integrates inflammatory biomarker measurements and brain MRI scans. Advanced statistical techniques will study connections between biomarkers and brain functioning. AI and computer science methods will create predictive models classifying patients based on biomarker levels and brain imaging features. This classification can guide understanding of which treatments are most effective for different subgroups.   

This novel approach offers a personalised, proactive strategy for early psychosis intervention by accounting for disorder heterogeneity. Integrating blood biomarkers with brain imaging has the potential to revolutionise treatment outcomes through tailored care from the earliest stages.

 

Left to right: Abhirup Ghosh, Christie Swallow & Ali Khatibi.