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 :// and :// 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.

National Institute of Health (NIH)
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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University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
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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
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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
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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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