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Erschienen in:

2015 | OriginalPaper | Buchkapitel

Functional Nonlinear Mixed Effects Models for Longitudinal Image Data

verfasst von : Xinchao Luo, Lixing Zhu, Linglong Kong, Hongtu Zhu

Erschienen in: Information Processing in Medical Imaging

Verlag: Springer International Publishing

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Abstract

Motivated by studying large-scale longitudinal image data, we propose a novel functional nonlinear mixed effects modeling (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 conduct Monte 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.

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Vorheriges Kapitel Joint 6D k-q Space Compressed Sensing for Accelerated High Angular Resolution Diffusion MRI

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Titel: Functional Nonlinear Mixed Effects Models for Longitudinal Image Data
verfasst von: Xinchao Luo
Lixing Zhu
Linglong Kong
Hongtu Zhu
Verlag: Springer International Publishing
Buch: Information Processing in Medical Imaging
Print ISBN: 978-3-319-19991-7

Electronic ISBN: 978-3-319-19992-4

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-19992-4_63

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