STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data.
Longitudinal neuroimaging data plays an important role in mapping the neural developmental profile of major neuropsychiatric and neurodegenerative disorders and normal brain. The development of such developmental maps is critical for the prevention, diagnosis, and treatment of many brain-related diseases. The aim of this paper is to develop a spatio-temporal Gaussian process (STGP) framework to accurately delineate the developmental trajectories of brain structure and function, while achieving better prediction by explicitly incorporating the spatial and temporal features of longitudinal neuroimaging data. Our STGP integrates a functional principal component model (FPCA) and a partition parametric space-time covariance model to capture the medium-to-large and small-to-medium spatio-temporal dependence structures, respectively. We develop a three-stage efficient estimation procedure as well as a predictive method based on a kriging technique. Two key novelties of STGP are that it can efficiently use a small number of parameters to capture complex non-stationary and non-separable spatio-temporal dependence structures and that it can accurately predict spatio-temporal changes. We illustrate STGP using simulated data sets and two real data analyses including longitudinal positron emission tomography data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and longitudinal lateral ventricle surface data from a longitudinal study of early brain development.
Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA.
Jung Won
JW
Hyun
Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA.
Yimei
Y
Li
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Chao
C
Huang
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Martin
M
Styner
Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Weili
W
Lin
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Hongtu
H
Zhu
D000368:Aged; D000544:Alzheimer Disease / Q000000981:diagnostic imaging / Q000473:pathology; D001921:Brain / Q000254:growth & development*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D059906:Neuroimaging / Q000379:methods*; D016011:Normal Distribution; D049268:Positron-Emission Tomography; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted
Functional principal component analysis; Kriging; Neuroimaging; Prediction; Spatio-temporal modeling