Modeling the brain as a networked system has become a popular paradigm in neuroscience. Knowledge discovery on brain network data has the potential to revolutionize the way we understand the brain and provide a basis for developing new treatments for neurological disorders. However, today knowledge discovery systems on brain networks are still extremely hard to build. Researchers must be taught in tedious details how to design the pipeline of data processing and adjust the parameter settings in different processing stages. To address this problem, the project will aim to build an end-to-end system for knowledge discovery in brain networks that learns to integrate different processing stages and self-adjust with minimal human intervention. Educational activities will include curriculum development and training of students in the areas of data science and neuroscience. Result dissemination is planned via publication in relevant peer-reviewed conferences and journals.

To make fundamental contributions to realizing this vision, it is necessary to go beyond the pipeline-based approaches, where the multiple stages of the knowledge discovery are studied in isolation. It has been shown that end-to-end systems can significantly alleviate the problem of laborious efforts in building pipelines and making adjustments. In the planned system, the four stages of the knowledge discovery on brain networks (i.e., node discovery, edge discovery, feature mining and statistical learning) will be integrated and studied together instead of being studied in isolation. This project will investigate innovative ideas on unifying different stages of knowledge discovery in brain networks: (1) collective brain network discovery, where the node discovery and edge discovery stages are deeply integrated into a unified framework; (2) label-contrasting edge discovery, which unifies edge discovery with model inference stages; (3) collective edge discovery unifies the edge discovery and statistical learning stages; (4) node-compressing sub-graph mining, node discovery stage and feature mining stage; (5) an end-to-end system across all four stages, which allows the learners in different stages to work together and form a unified learner for brain network analysis. The project will takes a next logical step in brain network research. It will build intellectual and formal connections between data mining and neuroscience. The project results have the potential for automating brain network analysis. In terms of broader impacts, the project will deliver new analytic tools that are broadly relevant to the study of the brain in cognitive neuroscience. These tools also have broad clinical applicability for early diagnosis of neurological injury, for evaluation of treatment response, etc. Educational activities will include curriculum development and training of students in the areas of data science and neuroscience.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Sylvia Spengler
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Worcester Polytechnic Institute
United States
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