Many large-scale cross-sectional and longitudinal imaging studies have been or are being widely conducted to better understand the progress of neurodegenerative and neuropsychiatric disorders or the normal brain development. However, analysis of both cross-sectional and longitudinal imaging data has been hindered by the lack of advanced image processing and statistical tools for analyzing complex and correlated imaging data (e.g., diffusion tensor, deformation tensor, medial shape representation) in curved space along with behavioral and clinical data in Euclidean space. In response to PAR-07-070, the primary goal of this project is to develop new statistical tools and to evaluate these statistical tools for analysis of imaging data in curved space, in combination with behavioral and clinical information in Euclidean space obtained from both cross-sectional and longitudinal studies. As these tools are developed, they will be evaluated and refined through extensive Monte Carlo simulations and data analysis. Also, the efficacy of the tools developed under this grant will be tested by both simulated cross-sectional and longitudinal datasets and the two datasets including a longitudinal MRI study of schizophrenia and a longitudinal MRI study of autism, respectively. Moreover, the companion software for all developed statistical tools, once validated, will be disseminated to imaging researchers through www.nitrc.org/, as we did for our brain image registration algorithm called HAMMER. This analysis software will provide much needed imaging tools for analyzing complex, correlated imaging data in biomedical, behavioral, and social sciences. Thus, it is applicable to a variety of neuroimaging studies, e.g., on major neurodegenerative diseases, neuropsychiatric disorders, substance use disorders, and brain development.

Public Health Relevance

The project proposes to analyze imaging, behavioral, and clinical data from two large neuroimaging studies of schizophrenia and autism. New statistical methods are developed and applied to detect morphological differences of cortical and subcortical structures across time between schizophrenia and autism patients and healthy subjects.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH086633-04
Application #
8391226
Study Section
Special Emphasis Panel (ZRG1-HDM-G (02))
Program Officer
Freund, Michelle
Project Start
2010-03-01
Project End
2014-11-30
Budget Start
2012-12-01
Budget End
2014-11-30
Support Year
4
Fiscal Year
2013
Total Cost
$313,376
Indirect Cost
$99,536
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
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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