ArticleNeuroImage2013
Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes.
Article in NeuroImage, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.Statistics and its interface · 2024Article
- Identifying regions of interest in mammogram images.Statistical methods in medical research · 2023Article
- Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.Biostatistics (Oxford, England) · 2022Article
- Article
- Localization and Diagnosis of Attention-Deficit/Hyperactivity Disorder.Healthcare (Basel, Switzerland) · 2021Article
- Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.Genetics · 2019Article
- Methods for scalar-on-function regression.International statistical review = Revue internationale de statistique · 2017Article
- Multiple testing for neuroimaging via hidden Markov random field.Biometrics · 2015Article
- WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING.The annals of applied statistics · 2015Article
- Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian "scalar-on-image" regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.
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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.