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)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
7R01MH086633-07
Application #
9145786
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Freund, Michelle
Project Start
2009-07-01
Project End
2019-06-30
Budget Start
2016-07-08
Budget End
2017-06-30
Support Year
7
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Texas MD Anderson Cancer Center
Department
Type
Hospitals
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
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
Kong, Dehan; Ibrahim, Joseph G; Lee, Eunjee et al. (2018) FLCRM: Functional linear cox regression model. Biometrics 74:109-117
Li, Tengfei; Zhou, Fan; Zhu, Ziliang et al. (2018) A Label-fusion-aided Convolutional Neural Network for Isointense Infant Brain Tissue Segmentation. Proc IEEE Int Symp Biomed Imaging 2018:692-695
Zhao, Bingxin; Ibrahim, Joseph G; Li, Yun et al. (2018) Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies. Cereb Cortex :
Jha, Shaili C; Xia, Kai; Schmitt, James Eric et al. (2018) Genetic influences on neonatal cortical thickness and surface area. Hum Brain Mapp 39:4998-5013
Risk, Benjamin B; Zhu, Hongtu (2018) Note on bias from averaging repeated measurements in heritability studies. Proc Natl Acad Sci U S A 115:E122
Yu-Feng Liu, Leo; Liu, Yufeng; Zhu, Hongtu et al. (2018) SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data. Neuroimage 175:230-245
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

Showing the most recent 10 out of 122 publications