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.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
1R01MH102339-01
Application #
8611397
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Freund, Michelle
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Princeton University
Department
Miscellaneous
Type
Schools of Arts and Sciences
DUNS #
City
Princeton
State
NJ
Country
United States
Zip Code
08543
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
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 141:431-41
Kolar, Mladen; Liu, Han (2015) Optimal Feature Selection in High-Dimensional Discriminant Analysis. IEEE Trans Inf Theory 61:1063-1083
Fan, Jianqing; Ke, Zheng Tracy; Liu, Han et al. (2015) QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION. Ann Stat 43:1498-1534
Zhao, Tuo; Liu, Han (2014) Calibrated Precision Matrix Estimation for High-Dimensional Elliptical Distributions. IEEE Trans Inf Theory 60:7874-7887
Zhao, Tuo; Yu, Mo; Wang, Yiming et al. (2014) Accelerated Mini-batch Randomized Block Coordinate Descent Method. Adv Neural Inf Process Syst 27:5614
Chen, Chao; Liu, Han; Metaxas, Dimitris N et al. (2014) Mode Estimation for High Dimensional Discrete Tree Graphical Models. Adv Neural Inf Process Syst 27:5533
Liu, Han; Wang, Lie; Zhao, Tuo (2014) Multivariate Regression with Calibration. Adv Neural Inf Process Syst 27:5630
Kolar, Mladen; Liu, Han; Xing, Eric P (2014) Graph Estimation From Multi-Attribute Data. J Mach Learn Res 15:1713-1750
Liu, Han; Wang, Lie; Zhao, Tuo (2014) Sparse Covariance Matrix Estimation With Eigenvalue Constraints. J Comput Graph Stat 23:439-459

Showing the most recent 10 out of 21 publications