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, 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.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-HDM-G (02))
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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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Wang, Xiao; Zhu, Hongtu (2017) Generalized Scalar-on-Image Regression Models via Total Variation. J Am Stat Assoc 112:1156-1168
Park, Yeonhee; Su, Zhihua; Zhu, Hongtu (2017) Groupwise envelope models for imaging genetic analysis. Biometrics 73:1243-1253
Zhu, Hongtu; Shen, Dan; Peng, Xuewei et al. (2017) MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction. J Am Stat Assoc 112:1009-1021
Li, Jialiang; Huang, Chao; Zhu, Hongtu (2017) A Functional Varying-Coefficient Single-Index Model for Functional Response Data. J Am Stat Assoc 112:1169-1181
Song, Xinyuan; Xia, Yemao; Zhu, Hongtu (2017) Hidden Markov latent variable models with multivariate longitudinal data. Biometrics 73:313-323
Hazlett, Heather Cody; Gu, Hongbin; Munsell, Brent C et al. (2017) Early brain development in infants at high risk for autism spectrum disorder. Nature 542:348-351
Xia, K; Zhang, J; Ahn, M et al. (2017) Genome-wide association analysis identifies common variants influencing infant brain volumes. Transl Psychiatry 7:e1188
Zhu, Wensheng; Yuan, Ying; Zhang, Jingwen et al. (2017) Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study. Neuroimage 146:983-1002
Bryant, Christopher; Zhu, Hongtu; Ahn, Mihye et al. (2017) LCN: a random graph mixture model for community detection in functional brain networks. Stat Interface 10:369-378
Huang, Chao; Thompson, Paul; Wang, Yalin et al. (2017) FGWAS: Functional genome wide association analysis. Neuroimage 159:107-121

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