The grand challenge of our time is to understand how “brains work and go wrong”. Advanced neuroimaging technologies, such as EEG and fMRI applied during resting states and cognitive task processing have moved neuroscience research into the era of big data, shifting the paradigm from the traditional hypothesis-based, group comparison approaches to an emerging and rapidly growing field of using Machine Learning tools for image processing, marker searching, disease diagnosis and for predicting individual differences from large-scale, multimodal neuroimaging data. Such data-driven approaches are highly promising for learning intricate latent relationships between brain and behaviour. Furthermore, the classification power of machine learning methods may help to translate large-scale brain imaging data into objective criteria for potential diagnosis and prognosis of disease in single individuals. However, the data-learned relationships are usually opaque and the feature space usually huge, making it difficult to interpret the results in terms of understanding how the brain works and what changes occur when it goes wrong.
This collaborative project aims to integrate and synergize existing expertise within HKBU to form an interdisciplinary team together with our international collaborator to build capacity for long-term development of big-data brain studies to characterize and understand continuous spectra of cognitive behavioural differences between individuals. Researchers from advanced data analytics (pattern recognition and machine/deep learning), dynamical network science (principle-based modelling) will closely work together with experts from cognitive neuroscience and individual differences. Within the project period of two years we aim to achieve objectives at three levels using large-scale datasets from open sources and from Co-Is.
Models and understanding from the selected cognitive tasks within this project will build foundation to a more comprehensive study of behaviours across many domains in the future by examining the latent variable space trained to differentiate different tasks. The project will contribute to elucidate continuous spectra of multimodal features across individuals as population-level priors used to predict risk of disorder for single individuals at the extreme boundaries of the spectra. The research work and capacity built in this project will set a foundation for future projects to develop tools and applications to help educators or professionals to conduct screening to identify individuals at risk.
This project is supported by the HKBU Research Committee, Interdisciplinary Research Clusters Matching Scheme RC-IRCMs/18-19/SCI/01.
For further information on this research topic, please contact Prof. Changsong ZHOU.