ArticleNeuroImage2016
STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 34 citations in OpenAlex.
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.Journal of the American Statistical Association · 2024Article
- Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes.Frontiers in neuroscience · 2024Article
- LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures.Journal of the American Statistical Association · 2023Article
- DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative.Medical image analysis · 2022Article
- A Spatio-temporal Model for Detecting the Effect of Cocaine Use Disorder on Functional Connectivity.Spatial statistics · 2021Article
- A Variational Approximation for Analyzing the Dynamics of Panel Data.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2021Article
- Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications.BMJ open ophthalmology · 2020Review
- Double-wavelet transform for multisubject task-induced functional magnetic resonance imaging data.Biometrics · 2019Article
- Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
- Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database.Scientific reports · 2018Article
- Multivariate semiparametric spatial methods for imaging data.Biostatistics (Oxford, England) · 2017Article
- Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.NeuroImage · 2017Article
- Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.