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 #
1R01MH086633-01A1
Application #
7791479
Study Section
Special Emphasis Panel (ZRG1-HDM-G (02))
Program Officer
Freund, Michelle
Project Start
2010-03-01
Project End
2013-11-30
Budget Start
2010-03-01
Budget End
2010-11-30
Support Year
1
Fiscal Year
2010
Total Cost
$330,008
Indirect Cost
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
Kim, Janet S; Staicu, Ana-Maria; Maity, Arnab et al. (2018) Additive Function-on-Function Regression. J Comput Graph Stat 27:234-244
Zhang, Zhengwu; Descoteaux, Maxime; Zhang, Jingwen et al. (2018) Mapping population-based structural connectomes. Neuroimage 172:130-145
Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G et al. (2018) TPRM: TENSOR PARTITION REGRESSION MODELS WITH APPLICATIONS IN IMAGING BIOMARKER DETECTION. Ann Appl Stat 12:1422-1450
Li, Tengfei; Xie, Fengchang; Feng, Xiangnan et al. (2018) Functional Linear Regression Models for Nonignorable Missing Scalar Responses. Stat Sin 28:1867-1886
Tang, Man-Lai; Tang, Niansheng; Zhao, Puying et al. (2018) Efficient Robust Estimation for Linear Models with Missing Response at Random. Scand Stat Theory Appl 45:366-381
Wang, Ching-Wei; Lee, Yu-Ching; Calista, Evelyne et al. (2018) A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays. Bioinformatics 34:1767-1773
Kang, Kai; Song, Xinyuan; Hu, X Joan et al. (2018) Bayesian adaptive group lasso with semiparametric hidden Markov models. Stat Med :
Yang, Hojin; Zhu, Hongtu; Ibrahim, Joseph G (2018) MILFM: Multiple index latent factor model based on high-dimensional features. Biometrics 74:834-844
Chen, Stephanie T; Xiao, Luo; Staicu, Ana-Maria (2018) A Smoothing-based Goodness-of-Fit Test of Covariance for Functional Data. Biometrics :
Kim, Janet S; Maity, Arnab; Staicu, Ana-Maria (2018) Additive Nonlinear Functional Concurrent Model. Stat Interface 11:669-685

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