TY - GEN
T1 - Functional Nonlinear Mixed Effects Models for Longitudinal Image Data
AU - Luo, Xinchao
AU - Zhu, Lixing
AU - Kong, Linglong
AU - Zhu, Hongtu
N1 - Funding Information:
Lixing Zhu was supported by a grant from the University Grants Council of Hong Kong, China. Hongtu Zhu was partially supported by NIH grants MH086633, RR025747, and MH092335 and NSF grants SES-1357666 and DMS-1407655.
PY - 2015/6/22
Y1 - 2015/6/22
N2 - Motivated by studying large-scale longitudinal image data, we propose a novel functional nonlinearmixed effectsmodeling (FNMEM) framework to model the nonlinear spatial-temporal growth patterns of brain structure and function and their association with covariates of interest (e.g., time or diagnostic status). Our FNMEM explicitly quantifies a random nonlinear association map of individual trajectories. We develop an efficient estimation method to estimate the nonlinear growth function and the covariance operator of the spatial-temporal process. We propose a global test and a simultaneous confidence band for some specific growth patterns.We conductMonte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply FNMEM to investigate the spatial-temporal dynamics of white-matter fiber skeletons in a national database for autism research. Our FNMEM may provide a valuable tool for charting the developmental trajectories of various neuropsychiatric and neurodegenerative disorders.
AB - Motivated by studying large-scale longitudinal image data, we propose a novel functional nonlinearmixed effectsmodeling (FNMEM) framework to model the nonlinear spatial-temporal growth patterns of brain structure and function and their association with covariates of interest (e.g., time or diagnostic status). Our FNMEM explicitly quantifies a random nonlinear association map of individual trajectories. We develop an efficient estimation method to estimate the nonlinear growth function and the covariance operator of the spatial-temporal process. We propose a global test and a simultaneous confidence band for some specific growth patterns.We conductMonte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply FNMEM to investigate the spatial-temporal dynamics of white-matter fiber skeletons in a national database for autism research. Our FNMEM may provide a valuable tool for charting the developmental trajectories of various neuropsychiatric and neurodegenerative disorders.
KW - Functional nonlinear mixed effects model
KW - Functional response
KW - Global test statistic
KW - Simultaneous confidence band
KW - Spatialtemporal pattern
UR - http://www.scopus.com/inward/record.url?scp=84965186508&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19992-4_63
DO - 10.1007/978-3-319-19992-4_63
M3 - Conference proceeding
C2 - 26213453
AN - SCOPUS:84965186508
SN - 9783319199917
T3 - Lecture Notes in Computer Science
SP - 794
EP - 805
BT - Information Processing in Medical Imaging
A2 - Ourselin, Sebastien
A2 - Alexander, Daniel C.
A2 - Westin, Carl-Fredrik
A2 - Cardoso, M. Jorge
PB - Springer
CY - Cham
T2 - 24th International Conference on Information Processing in Medical Imaging, IPMI 2015
Y2 - 28 June 2015 through 3 July 2015
ER -