ArticleThe annals of applied statistics
Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis.
Article in The annals of applied statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 44 citations in OpenAlex.
- Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models.Statistics in medicine · 2026Article
- Central Posterior Envelopes for Bayesian Functional Principal Component Analysis.Journal of data science : JDS · 2023Article
- Longitudinal Canonical Correlation Analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Fast Multilevel Functional Principal Component Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Matrix decomposition for modeling lesion development processes in multiple sclerosis.Biostatistics (Oxford, England) · 2022Article
- Fast Univariate Inference for Longitudinal Functional Models.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.Statistics and its interface · 2022Article
- Convergent brain microstructure across multiple genetic models of schizophrenia and autism spectrum disorder: A feasibility study.Magnetic resonance imaging · 2020Article
- Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma Data.Journal of the American Statistical Association · 2019Article
- FMEM: Functional Mixed Effects Models for Longitudinal Functional Responses.Statistica Sinica · 2019Article
- Logistic regression error-in-covariate models for longitudinal high-dimensional covariates.Stat · 2019Article
- Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students.Frontiers in neuroscience · 2019Article
- Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress.Developmental cognitive neuroscience · 2018Review
- Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.NeuroImage · 2017Article
- Making use of longitudinal information in pattern recognition.Human brain mapping · 2016Article
- Fast Covariance Estimation for High-dimensional Functional Data.Statistics and computing · 2016Article
- Parametrization of white matter manifold-like structures using principal surfaces.Journal of the American Statistical Association · 2016Article
- Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach.Biostatistics (Oxford, England) · 2015Article
- Statistical modeling of time-dependent fMRI activation effects.Human brain mapping · 2015Article
- Statistical image analysis of longitudinal RAVENS images.Frontiers in neuroscience · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
We develop a flexible framework for modeling high-dimensional imaging data observed longitudinally. The approach decomposes the observed variability of repeatedly measured high-dimensional observations into three additive components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the dynamic irreversible deformation over multiple realizations, and a subject-visit specific imaging deviation that quantifies exchangeable effects between visits. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis of diffusion tensor imaging (DTI) data of the corpus callosum of multiple sclerosis (MS) subjects. The study includes 176 subjects observed at 466 visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 30,000 voxels.
Indexed as
Identifiers
What OpenQuestion holds
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.