TY - GEN
T1 - LSTGEE
T2 - Medical Imaging 2009 - Image Processing
AU - Li, Yimei
AU - Zhu, Hongtu
AU - Chen, Yasheng
AU - An, Hongyu
AU - Gilmore, John
AU - Lin, Weili
AU - Shen, Dinggang
PY - 2009
Y1 - 2009
N2 - Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze the imaging, behavioral, and clinical data is critical for optimally exploring and interpreting the findings from those imaging studies. However, the existing imaging processing and statistical methods for analyzing imaging longitudinal measures are primarily developed for cross-sectional neuroimaging studies. The simple use of these cross-sectional tools to longitudinal imaging studies will significantly decrease the statistical power of longitudinal studies in detecting subtle changes of imaging measures and the causal role of time-dependent covariate in disease process. The main objective of this paper is to develop longitudinal statistics toolbox, called LSTGEE, for the analysis of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure based on a score test statistic and a resampling method to test linear hypotheses of unknown parameters, such as associations between brain structure and function and covariates of interest, such as IQ, age, gene, diagnostic groups, and severity of disease. We demonstrate the application of our statistical methods to the detection of the changes of the fractional anisotropy across time in a longitudinal neonate study. Particularly, our results demonstrate that the use of longitudinal statistics can dramatically increase the statistical power in detecting the changes of neuroimaging measures. The proposed approach can be applied to longitudinal data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.
AB - Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze the imaging, behavioral, and clinical data is critical for optimally exploring and interpreting the findings from those imaging studies. However, the existing imaging processing and statistical methods for analyzing imaging longitudinal measures are primarily developed for cross-sectional neuroimaging studies. The simple use of these cross-sectional tools to longitudinal imaging studies will significantly decrease the statistical power of longitudinal studies in detecting subtle changes of imaging measures and the causal role of time-dependent covariate in disease process. The main objective of this paper is to develop longitudinal statistics toolbox, called LSTGEE, for the analysis of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure based on a score test statistic and a resampling method to test linear hypotheses of unknown parameters, such as associations between brain structure and function and covariates of interest, such as IQ, age, gene, diagnostic groups, and severity of disease. We demonstrate the application of our statistical methods to the detection of the changes of the fractional anisotropy across time in a longitudinal neonate study. Particularly, our results demonstrate that the use of longitudinal statistics can dramatically increase the statistical power in detecting the changes of neuroimaging measures. The proposed approach can be applied to longitudinal data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.
KW - Covariate
KW - Generalized estimating equation
KW - Longitudinal
KW - Resampling method
KW - Score statistic
UR - http://www.scopus.com/inward/record.url?scp=71649090329&partnerID=8YFLogxK
U2 - 10.1117/12.812432
DO - 10.1117/12.812432
M3 - Conference contribution
AN - SCOPUS:71649090329
SN - 9780819475107
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2009 - Image Processing
Y2 - 8 February 2009 through 10 February 2009
ER -