Understanding the brain has been called science’s final frontier due to its astonishing complexity. It provides people with their unmatched cognitive capabilities and is the source of some of our most debilitating disabilities. A widespread view among brain scientists is that brains are essentially large complex networks, with each node and edge of the network itself being a complex object. Thus, to understand ourselves and to fight disorders including depression (the world’s leading burden of disease) and suicide (a current epidemic), the scientist on this project will develop mathematical and statistical tools to study networks and apply them to brain networks. This will provide a deeper understanding of human brains and contribute to preventing dysfunction and restoring function. All the tools they develop will also be integrated into a course, and all of the science, including papers, code, and educational materials, will be generated in the open source, so that everyone in society will have access to this content.
The investigator will establish foundational theory and methods for analyzing populations of attributed connectomes. Their approach, “connectal coding,†will enable brain scientists to (1) infer latent structure from individual connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships between connectomes and multivariate phenotypes. The methods they develop will naturally overcome the challenges inherent in connectomics: high-dimensional non-Euclidean data with multi-level nonlinear interactions. Their procedures will extend the current state-of-the-art in terms of theoretical guarantees, computational scalability, empirical performance, and interpretability of results. The efficacy of the methods will be demonstrated on datasets spanning experimental modalities, scales, and taxa, in collaboration with domain experts who acquired the data. They will also generate educational resources to complement their methods, data, and code to democratize connectomics. All project results will be available at https://neurodata.io/graspy
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
