Multiscale and multimodal modelling of brain dynamics. This project aims to understand dynamics of how several brain regions work together to process information. This project will generate new knowledge in brain sciences by using state of the art computational modelling and neuroimaging methods like functional and diffusion magnetic resonance imaging and electromagnetic measurements. This project will develop technologies to compute multiscale, multimodal and directed connectivity in the brain. ....Multiscale and multimodal modelling of brain dynamics. This project aims to understand dynamics of how several brain regions work together to process information. This project will generate new knowledge in brain sciences by using state of the art computational modelling and neuroimaging methods like functional and diffusion magnetic resonance imaging and electromagnetic measurements. This project will develop technologies to compute multiscale, multimodal and directed connectivity in the brain. Expected outcomes of this project will enhance our understanding of the brain’s functional organization and dynamics. The benefits of this project will include breakthroughs in development of new neuro-technologies like brain-machine interfaces and neuroscience inspired artificial intelligence. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170100128
Funder
Australian Research Council
Funding Amount
$395,000.00
Summary
Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts ....Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts research. This project is expected to advance our understanding of information processing in the brain by providing a mechanistic approach to functional integration.Read moreRead less
Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less