Abstract
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 language | English |
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Pages (from-to) | 794-805 |
Number of pages | 12 |
Journal | Lecture Notes in Computer Science |
Volume | 9123 |
DOIs | |
Publication status | Published - 2015 |
Event | 24th International Conference on Information Processing in Medical Imaging, IPMI 2015 - Isle of Skye, United Kingdom Duration: 28 Jun 2015 → 3 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