Advances in imaging technologies (Magnetic Resonance Imaging, for example) have significantly accelerated brain disorder studies. There is an urgent need to integrate, index and model multimodal data across a large population in order to discover a more detailed understanding about process interaction in this very complex biological system. Current state-of-the-art computational and software technologies fall short in multimodality data integration and modeling, and integrated analysis of diverse neuroimaging datasets across human subjects. The overall aim of this proposal is to develop a novel, rigorous framework for integrated modeling and analysis of multimodality neuroimaging data based on Riemannian geometry, multivariate simplex splines, and statistical learning.
Intellectual Merits This interdisciplinary research team will design a fundamental framework for advanced and integrated analysis of brain imaging data. All research activities will address the following major themes and objectives: (1) To explore new theoretic tools based on Riemannian geometry of 3-manifolds for the development of a novel Canonical Volumetric Model (CVM) which provides volumetric mapping of individual brain to a solid unit sphere with accurate matching across subjects; (2) To design hierarchical spherical trivariate simplex splines for compact representation, integration, indexing and visualization of multimodality heterogeneous imaging data with high efficiency and accuracy, which can further refine the intersubject registration through level-of-detail matching in a higher dimensional physical space based on the integration of the hierarchical spline volume with Lagrangian dynamics; (3) To design new statistical learning and mining methods to analyze simultaneously the variety of data across the broad range of spatial and temporal scales and human subjects in order to infer the dynamics of brain functions in neurological disease studies.
Broad Impacts This research will contribute to the data-intensive brain study by offering an accurate, robust, and innovative scientific approach for analytic integration, statistical modeling, and quantitative analysis of a variety of brain imaging data. The proposed computational framework has the potential to be applied across multiple areas of brain research as well as in clinical diagnosis. It is likely that this work will impact a large number of patients with neurological diseases and will provide a commonly accepted standard infrastructure for use by many other researchers. The PIs'' research endeavors will be tightly integrated with a complementary set of educational objectives, including: (1) the development of new strategies for truly multi-disciplinary science education; (2) the enhancement of the existing curricula; (3) the doctoral training of graduate researchers; (4) the implementation of mentoring activities for students from underrepresented groups.
