The objective of this proposal is to develop and theoretically evaluate a unified set of statistical, computational, and software tools to address data mining and discovery science challenges in the analysis of existing vast amounts of publicly available neuroimaging data. In particular, we propose to develop scalable and robust semiparametric solutions for high-throughput estimation of resting-state brain connectivity networks, both at the individual and population levels, with the flexibility of incorporating covariate information. The work will contribute meaningfully to the theory and methods for large-scale semiparametric graphical models and will apply these methods to the largest collections of resting-state fMRI data available. The proposed methods and theory include key directions of research for brain network estimation and mining. First, we pro- pose novel methods for subject-specific network estimation, such as would be needed for biomarker development in functional brain imaging. Secondly, we define and propose to evaluate and implement methods for studying population-level graphs, which study collections of graphs. Thirdly, we propose the use of estimated graphs in predictive modeling. Finally, all of these methods will have complementary software and web services development. Most notably, the idea of population graphs allows for the creation of functional brain network atlases. In summary, the work of this proposal will result in a unified framework for the analysis of modern neuroimaging data via graphical models. Our methods will further be agnostic to intricacies of the technology, thus making it portable across settings and applicable outside of the field of functional brain imaging. The methods will be carefully evaluated via theory, simulation and data-based application evidence.
Modern neuroimaging data are often Big, Complex, Noisy and Dependent. We propose a systematic attempt on methodological development for the largely unexplored but practically important problem of network estimation and mining based on neuroimaging data. Our proposed work represents a significant step forward over the current methodology and has the potential to be applied to analyze a wide range of scientific problems beyond brain imaging data analysis.
|Wang, Zhaoran; Liu, Han; Zhang, Tong (2014) OPTIMAL COMPUTATIONAL AND STATISTICAL RATES OF CONVERGENCE FOR SPARSE NONCONVEX LEARNING PROBLEMS. Ann Stat 42:2164-2201|
|Han, Fang; Liu, Han (2014) Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data. J Am Stat Assoc 109:275-287|