Many large-scale 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. Compared to cross-sectional imaging studies, the longitudinal imaging studies can identify subtle anatomical and functional changes and the causal role of time-dependent covariate (e.g., exposure) in disease process. However, analysis of longitudinal imaging data has been hindered by the lack of advanced image processing and statistical tools for analyzing complex and correlated imaging data along with behavioral and clinical data. Relatively, cross-sectional image processing and statistical tools have been developed and used, but they are in general not optimal in power. In response to PAR-06-411, the primary goal of this project is to develop new statistical tools and to evaluate these statistical tools and 4D image processing for analysis of imaging data, in combination with behavioral and clinical information obtained from 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 longitudinal datasets and the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset for early detection of Alzheimer's Disease (AD), 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 longitudinal 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 longitudinal 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 one large neuroimaging study on Alzheimer's diseases. New statistical methods are developed and applied to detect morphological differences of cortical and subcortical structures across time between Alzheimer patients and healthy subjects.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21AG033387-02
Application #
7771769
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Hsiao, John
Project Start
2009-03-01
Project End
2012-02-28
Budget Start
2010-03-15
Budget End
2012-02-28
Support Year
2
Fiscal Year
2010
Total Cost
$150,341
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
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Huang, Meiyan; Nichols, Thomas; Huang, Chao et al. (2015) FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data. Neuroimage 118:613-27
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