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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Freund, Michelle
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Princeton University
Schools of Arts and Sciences
United States
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Chén, Oliver Y; Crainiceanu, Ciprian; Ogburn, Elizabeth L et al. (2017) High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics :
Han, Fang; Liu, Han (2017) Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution. Bernoulli (Andover) 23:23-57
Zhao, Tuo; Liu, Han (2016) Accelerated Path-following Iterative Shrinkage Thresholding Algorithm with Application to Semiparametric Graph Estimation. J Comput Graph Stat 25:1272-1296
Qiu, Huitong; Han, Fang; Liu, Han et al. (2016) Joint Estimation of Multiple Graphical Models from High Dimensional Time Series. J R Stat Soc Series B Stat Methodol 78:487-504
Tan, Kean Ming; Ning, Yang; Witten, Daniela M et al. (2016) Replicates in high dimensions, with applications to latent variable graphical models. Biometrika 103:761-777
Zhao, Tianqi; Cheng, Guang; Liu, Han (2016) A PARTIALLY LINEAR FRAMEWORK FOR MASSIVE HETEROGENEOUS DATA. Ann Stat 44:1400-1437
Liu, Han; Mulvey, John; Zhao, Tianqi (2016) A semiparametric graphical modelling approach for large-scale equity selection. Quant Finance 16:1053-1067
Kang, Jian; Bowman, F DuBois; Mayberg, Helen et al. (2016) A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. Neuroimage 141:431-441
Wang, Zhaoran; Gu, Quanquan; Ning, Yang et al. (2015) High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality. Adv Neural Inf Process Syst 28:2512-2520
Qiu, Huitong; Xu, Sheng; Han, Fang et al. (2015) Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes. Proc Int Conf Mach Learn 37:1843-1851

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