Large-scale medical imaging studies have collected a rich set of ultra-high dimensional imaging data, behavioral data, and clinical data in order to better understand the progress of neuropsychiatric disorders, neurological dis- orders and stroke, normal brain development, diagnosis of colorectal cancer, osteoarthritis, and prostate cancer, among many others. However, the development of statistical and computational methods for the joint analysis of imaging and clinical data has fallen seriously behind the technological advances. Three common and important themes of these image data are (T1) ultra-high dimensional functional data with a multi-dimensional tensor struc- ture, (T2) complex geometric structures of human organs, and (T3) complex spatial correlation structures. To meet this critical and important challenge, we will establish a comprehensive statistical framework by addressing three methodological problems. First, there are few efficient and fast methods on modeling high-dimensional imaging data as piecewise smooth functions, while accounting for themes (T2) and (T3). Second, there are few efficient methods on the use of ultra-high dimensional tensor data to predict cognitive development and high- dimensional imaging data, while accounting for the themes (T1)-(T3). Third, little has been done on the analysis of imaging data from longitudinal twin studies. We will establish a comprehensive statistical framework to address these methodological problems. Specifically, we will develop a class of hierarchical functional process models, a class of functional tensor prediction process models, and a class of functional structural equation process mod- els. Scientifically, these new statistical methods are motivated by the analysis of a longitudinal neuroimaging database on early brain development in high-risk children from the Conte study. Our new methods can dramatically increase scientists'ability to better address important scientific questions associated with many imaging studies, particularly those for the Conte study. As these tools are being developed, they will be evaluated and refined through extensive Monte Carlo simulations and the Conte database. Companion software, which will pro- vide much needed analytic tools for the joint analysis of imaging and clinical data, will be disseminated to imaging researchers through ://www.nitrc.org/ and ://www.bios.unc.edu/research/bias.The proposed methodology will have wide applications in neuropsychiatric and neurodegenerative diseases, neurological disorders and stroke, and osteoarthritis, among others.

Public Health Relevance

The project proposes to analyze imaging, behavioral, and clinical data from a longitudinal neuroimaging database on early brain development in high-risk children from the Conte study. New statistical methods are developed and verified by using extensive simulation studies and the Conte dataset.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
2R01MH086633-05A1
Application #
8759104
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Freund, Michelle
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Lin, Ja-An; Zhu, Hongtu; Mihye, Ahn et al. (2014) Functional-mixed effects models for candidate genetic mapping in imaging genetic studies. Genet Epidemiol 38:680-91
Zhu, Hongtu; Fan, Jianqing; Kong, Linglong (2014) Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities. J Am Stat Assoc 109:1084-1098
Tang, Niansheng; Zhao, Puying; Zhu, Hongtu (2014) Empirical Likelihood for Estimating Equations with Nonignorably Missing Data. Stat Sin 24:723-747
Zhu, Hongtu; Khondker, Zakaria; Lu, Zhaohua et al. (2014) Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers. J Am Stat Assoc 109:997-990
Yuan, Ying; Gilmore, John H; Geng, Xiujuan et al. (2014) FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data. Neuroimage 84:753-64
Hyun, Jung Won; Li, Yimei; Gilmore, John H et al. (2014) SGPP: spatial Gaussian predictive process models for neuroimaging data. Neuroimage 89:70-80
Bompard, Lucile; Xu, Shun; Styner, Martin et al. (2014) Multivariate longitudinal shape analysis of human lateral ventricles during the first twenty-four months of life. PLoS One 9:e108306
Chen, Yasheng; Zhu, Hongtu; An, Hongyu et al. (2014) More insights into early brain development through statistical analyses of eigen-structural elements of diffusion tensor imaging using multivariate adaptive regression splines. Brain Struct Funct 219:551-69
Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng (2014) Bayesian Sensitivity Analysis of Statistical Models with Missing Data. Stat Sin 24:871-896
Shi, Yundi; Short, Sarah J; Knickmeyer, Rebecca C et al. (2013) Diffusion tensor imaging-based characterization of brain neurodevelopment in primates. Cereb Cortex 23:36-48

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