Functional nonlinear mixed effects models for longitudinal image data

Xinchao Luo, Lixing ZHU, Linglong Kong, Hongtu Zhu*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)794-805
Number of pages12
JournalLecture Notes in Computer Science
Publication statusPublished - 2015
Event24th International Conference on Information Processing in Medical Imaging, IPMI 2015 - Isle of Skye, United Kingdom
Duration: 28 Jun 20153 Jul 2015

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Functional nonlinear mixed effects model
  • Functional response
  • Global test statistic
  • Simultaneous confidence band
  • Spatialtemporal pattern


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