ArticleNeuroImage2011
Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis.
Article in NeuroImage, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
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.
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Who cites it
21 citing papers in PubMed, 53 citations in OpenAlex.
- Differences in functional connectivity distribution after transcranial direct-current stimulation: A connectivity density point of view.Human brain mapping · 2023Trial
- Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach.BMC medical research methodology · 2024Article
- Subtle microstructural alterations in white matter tracts involved in socio-emotional processing after very preterm birth.NeuroImage. Clinical · 2024Article
- ESTIMATION FOR EXTREME CONDITIONAL QUANTILES OF FUNCTIONAL QUANTILE REGRESSION.Statistica Sinica · 2022Article
- Extreme quantile estimation for partial functional linear regression models with heavy-tailed distributions.The Canadian journal of statistics = Revue canadienne de statistique · 2022Article
- Longitudinal dynamic functional regression.Journal of the Royal Statistical Society. Series C, Applied statistics · 2020Article
- Article
- Modeling and Prediction of Multiple Correlated Functional Outcomes.Journal of agricultural, biological, and environmental statistics · 2019Article
- Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists.Statistics surveys · 2019Article
- Parametrization of white matter manifold-like structures using principal surfaces.Journal of the American Statistical Association · 2016Article
- Variable-Domain Functional Regression for Modeling ICU Data.Journal of the American Statistical Association · 2014Article
- Spatial variability and changes of metabolite concentrations in the cortico-spinal tract in multiple sclerosis using coronal CSI.Human brain mapping · 2014Article
- Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2014Article
- ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.The annals of applied statistics · 2014Article
- Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (I2C2).Cognitive, affective & behavioral neuroscience · 2013Article
- Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes.NeuroImage · 2013Article
- Article
- VARYING COEFFICIENT MODEL FOR MODELING DIFFUSION TENSORS ALONG WHITE MATTER TRACTS.The annals of applied statistics · 2013Article
- Article
- Longitudinal Penalized Functional Regression for Cognitive Outcomes on Neuronal Tract Measurements.Journal of the Royal Statistical Society. Series C, Applied statistics · 2012Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a "penalized functional regression" procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a "tract abnormality score" for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6.4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation.
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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.