ArticleThe annals of applied statistics2014
ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.
Article in The annals of applied statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Prefrontal Neurovascular Coupling Abnormalities Associated With Cognitive Performance in Multiple Sclerosis.CNS neuroscience & therapeutics · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- A SPATIALLY VARYING HIERARCHICAL RANDOM EFFECTS MODEL FOR LONGITUDINAL MACULAR STRUCTURAL DATA IN GLAUCOMA PATIENTS.The annals of applied statistics · 2024Article
- Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior.Journal of the American Statistical Association · 2024Article
- GPU Accelerated Estimation of a Shared Random Effect Joint Model for Dynamic Prediction.Computational statistics & data analysis · 2022Article
- Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework.NeuroImage · 2021Article
- Lesion probability mapping in MS patients using a regression network on MR fingerprinting.BMC medical imaging · 2021Article
- Tuber Locations Associated with Infantile Spasms Map to a Common Brain Network.Annals of neurology · 2021Article
- Mapping migraine to a common brain network.Brain : a journal of neurology · 2020Article
- MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry.Brain imaging and behavior · 2019Article
- Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.NeuroImage · 2019Article
- Spatially Adaptive Varying Correlation Analysis for Multimodal Neuroimaging Data.IEEE transactions on medical imaging · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically "mass univariate" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from
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.