Characterizing how brain regions activate, collaborate, and interact in cognition empowers us with advanced approaches to help humans make the right decisions on high stress jobs, prevent drug abuse, and treat neurological disorders. This project will study cognitive control in terms of the uncertainty representation, namely, how brains execute the same cognitive task with different levels of uncertainty. Based on theory and algorithms in topology data analysis, the project will analyze brain functional MRI images using novel topological descriptors, which directly model global interactions between brain regions in a principled manner. These descriptors will be used in novel learning models to discover brain activity patterns that are crucial for uncertainty representation. The outcome of the project will include (1) new knowledge in uncertainty representation, e.g., fine-scale activity patterns and interactions between brain regions correlated to the uncertainty level; (2) new topological analysis tools for brain imaging study. This project will bring research and educational opportunities to graduate and undergraduate students from both computer science and neuroscience. The PIs will also mentor students from underrepresented groups and high school students through the CUNY College Now program.
This project will create new computational topology algorithms to extract rich information from the intrinsic structure of data. Novel machine learning methods will be created in order to leverage the topological structures for not only prediction, but also knowledge discovery. A novel interactive data exploration platform based on topological features will be developed for brain imaging study. These techniques and software will be validated on task-evoked fMRI data to produce quantitative assessments of accuracy and to characterize advantages and limitations of these approaches. Domain experts will validate the quality of the approach in validating scientific hypotheses and data exploration.
