Approved research

Graph Neural Networks (GNNs) in Understanding Brain Connectivity and Socioeconomic Factors

Lay summary

Aims: This project aims to uncover how poverty and other socioeconomic factors affect the brain's structure and functioning. Using cutting-edge technology known as Graph Neural Networks (GNNs), the research will delve into the complex connections within the brain, integrating these with socioeconomic data to provide a more comprehensive understanding of brain health.

Scientific Rationale: The human brain is a complex network, with its health influenced not just by biological factors but also by the environment we live in, including our socioeconomic status. Traditional methods of analyzing brain data often struggle to fully capture this complexity. GNNs, which excel in analyzing networked data, offer a promising solution. By applying GNNs to brain data, this research seeks to illuminate the intricate ways in which our social and economic environments interact with our brain's functioning.

Project Duration: 4 years

Public Health Impact: The findings from this research have the potential to make significant contributions to public health. By better understanding the relationship between socioeconomic factors and brain health, we can inform public policy, potentially leading to more effective strategies for addressing wealth disparities. This is particularly relevant for communities impacted by poverty, as the research could pave the way for targeted interventions and support. Ultimately, this project aims to not only advance scientific knowledge but also to make a real difference in how we approach poverty in various socioeconomic contexts, making it a matter of public interest and benefit.