Understanding how different brain regions work together is fundamental to the understanding of brain functions in both health and diseases. Segregation and integration is a general principle of the brain's functional architecture. The diverse range of human brain functions emerges from and is realized by the interaction of multiple concurrent neural processes, each of which is spatially distributed across specific structural substrate of brain areas. A fundamental question in cognitive neuroscience is how to robustly and faithfully reconstruct concurrent functional networks from functional magnetic resonance imaging (fMRI) data and quantitatively measure their network-level interactions. Dr. Tianming Liu of University of Georgia will develop novel computational methods to investigate how different brain regions form distinctive networks. Using the publicly available Human Connectome Project (HCP) fMRI datasets, Dr. Liu will not only develop and evaluate a novel theory of organizational architecture to explain human brain function but also produce a set of freely available software tools for analyzing brain network activity. Through this project, Dr. Liu will integrate research into educational and outreach activities. The novel educational materials generated from this project will contribute to cultivate the next generation of scientists.
Dr. Liu plans to employ innovative dictionary learning methods to sparsely represent whole-brain fMRI signals, where the time series of each over-complete basis dictionary represents the functional activities of a brain network and its corresponding reference weight vector stands for the spatial map of this brain network. This project aims to (1) identify and characterize a large number of reproducible and robust functional networks, including both task-evoked and resting state networks, across the HCP data; (2) explore to what extent these task-evoked and resting state brain networks overlap spatially with each other; (3) test the hypothesis that cognitive brain functions are realized by hybrid combinations of reciprocally localized highly-heterogamous regions and highly-specialized regions. Collectively, this project will contribute novel tools and insights for understanding intra- and inter-network interactions, which are expected to benefit a variety of cognitive neuroscience and neural engineering studies.
