Round 4

PI: Dr Jianbo Jiao. Co-I: Professor Ole Jensen
Centre Fellow: Dr Cai Wingfield
Artist in Residence: Alex Billingham

Understanding how the human brain works is a longstanding and challenging question. Inspired by human neural networks, artificial neural networks (ANNs) have shown remarkable progress across various fields.

The aim of this proposal is to use ANNs as a model for visual processing in the human brain. Likewise, human brain data is expected to inspire new developments of ANNs. The proposed research will investigate this topic from the perspective of visual information (i.e. daily visually dynamic data such as images and video sequences), as this is the most common way for humans to learn and understand the environment.

Specifically, participants will be shown various images and videos, and their brain activities will be recorded using brain-imaging approaches to observe how they learn and understand visual information. Meanwhile, ANNs will be trained with the same stimuli.

By comparing how human brains and ANNs learn and interpret visual information, this research will shed new light on both approaches and introduce innovative, brain-inspired designs for AI systems. On the longer term, the outcome will provide a better understanding of how the human brain process visual information.

This knowledge could be instrumental in addressing differences in visual processing associated with neurodiversity such as dyslexia and ADHD. Furthermore, our work will contribute to the ongoing debate about what distinguishes the human brain from AI, for instance in the context of appreciating aesthetics. 

PI: Dr Kyungmin An. Co-I: Dr Hyung Chang
Centre Fellow: Dr Leandro Junges
Artist in Residence: Emily Scarrott

Autism Spectrum Disorder (ASD) is prevalent, and early diagnosis is crucial to help manage the condition from childhood. Currently, ASD diagnosis is only based on behavioural information. While ASD diagnoses based on brain imaging data studies have been extensive, they are not suitable for young children who need to remain still and in specific positions during the procedure. Consequently, magnetoencephalography (MEG) is a more suitable neuroimaging technique for children, which provides a more comfortable recording environment and accurate temporal data than other types of brain imaging. However, there is a lack of ASD diagnostic research using MEG. 

In this study, we aim to use novel mathematical and computational techniques to analyse MEG data that is more appropriate for young children. Specifically, we will use network analysis and artificial intelligence to estimate how MEG data correlates with behavioural information. This research holds significant importance as it seeks to advance the understanding of brain activity in children with ASD and to investigate a method to diagnose ASD in young children using a more child-friendly neuroimaging technique.

PI: Professor Shishir Shetty. Co-Is: Professor Fabian Spill, Professor Dirk-Peter Herten & Dr Chris Weston
Centre Fellow: Dr Paul Roberts 
Artist in Residence: Felicity Inkpen

Hepatocellular cancer (HCC), the most common form of primary liver cancer, affects millions of people around the globe, with cases set to rise dramatically in the coming years. A recent breakthrough has been the approval of immunotherapy to treat HCC. Immunotherapy activates a patient’s immune system to attack their HCC tumour but is only successful in 10-15% of patients. 

For immunotherapy to be successful, it is critical that immune cells can get to the right place at the right time. Immune cells travel along our blood vessels and they are guided to their ideal location by the cells lining these blood vessels, called endothelial cells.  There is evidence that the tumour hides from the immune system by changing the behaviour of these endothelial cells.  Reversing this process should open the gates to tumour-killing immune cells and improve the effectiveness of current immune therapies. Using new microscopic imaging techniques to analyse patient samples together with complex mathematical analysis, it will show how endothelial cells affect the immune response to HCC. These results will identify targets on blood vessels for development as new treatments for HCC.

PI: Dr Luke Brown. Co-I: Gavin Rudge, Dr Siân Lowri Griffiths, Allan Reid, Dr Joht Singh Chandan, Conner McDonald and Professor Manfred Opper
Centre Fellow: Dr Catherine Drysdale
Artist in Residence: Shannel James

Predicting the neighbourhoods where psychosis is likely to occur? 

Psychosis, a Serious Mental Illness (SMI), has a significant and longstanding impact on individuals, communities, and society. Although treatments aim at improving the lives of those who develop psychosis are continually being developed, there is now greater recognition for the need of new approaches to prevent psychosis developing.  

Current models of psychosis prevention are however inefficient in reaching the majority of those who later develop psychosis for the first time (First Episode Psychosis). They are also inaccessible to certain ethnic minority communities, which further perpetuates existing inequalities in mental health care. It is clear that greater diversity is required in our approach to psychosis prevention, where accessibility, equity and inclusion are central components.  

A public health approach has been proposed as a new approach to psychosis preventative. In developing a public health approach, a tool will first need to be developed to accurately detect and predict the neighbourhood where incidents of psychosis are most common. This will ensure future preventive approaches are targeted and attuned to the communities in these neighbourhoods, making the delivery of new preventative interventions resourceful, effective, and accessible. This study aims to develop a mathematical model that uses health, social care and crime data (social determinants) to accurately predict the neighbourhoods where psychosis is most likely to occur.

PI: Dr Maria Witek. Co-I: Dr Rob Sturman, Dr Patti Nijhuis & Diar Abdlkarim
Centre Fellow: Dr Daniel Galvis
Artist in Residence: Simon Peter Green

Much of human behaviour involves coordinating body-movement to other rhythms in the world. Sometimes this requires interacting with and manipulating multiple sound-producing objects. This demonstrates that the mind is a distributed system that spans the brain, body and the environment. DJing is a paradigmatic model of such a system. It’s a complex musical skill that involves matching the tempo of one rhythm to that of another by adjusting its speed and position on the turntables. This skill is called beat-matching.  

We will analyse data collected from a variety of techniques during beat-matching, including EEG, motion-capture and audio recording, to study how these signals synchronise and build a mathematical model to understand fundamental features of this process.

Gaining a model of how the mind and body interacts with the world through synchronised behaviour can help us understand what happens when interactions break down, such as in disabled DJs. Together with stakeholders in the disability community, music technology and dance music industries, the project aims to translate the research into the development of more accessible DJ tools and environments, thus creating a more inclusive dance music culture.

PI: Dr Marie-Christine Jones. Co-I: Dr Sophie Cox, Mathieu Brunet, Dr Sarah Gordon & Dr Dwaipayan Chakrabarti
Centre Fellow: Dr Amin Rahmat
Artist in Residence: Vicky Roden

Extracellular vesicles (EVs) are very small particles (about 1/1000 of a strand of human hair) produced by human cells. They act as couriers between cells to maintain health. EVs represent an exciting tool to heal many diseases, however it is very difficult to source sufficient quantities needed for treatments.

Our cells continuously produce billions of nanoparticles, there are also concerns about the purity and quality of EVs isolated from a natural source. To overcome this problem, pharmaceutical science is being used to engineer artificial versions of the natural EVs that may be used to target specific diseases. These synthetic EVs benefit from the possibility of large-scale production and allow the industry to have control over their composition. 

To accelerate the development of synthetic EVs, this research projects aims to use a computer-based approach to create simulations of their design before laboratory development. We aim to help scientists optimise the initial parameters of their work and to make better material choices for their product to be developed faster and at a lower cost. The development of this tool support the innovation of numerous nanotherapies inspired by EVs.