Evidence map›Paper›PMID 32318692›Full record

ArticleBiostatistics (Oxford, England)2022

Matrix decomposition for modeling lesion development processes in multiple sclerosis.

Menghan Hu, Ciprian Crainiceanu, Matthew K Schindler, Blake Dewey, Daniel S Reich, Russell T Shinohara, Ani Eloyan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Menghan HuDepartment of Biostatistics, Brown University, Providence, RI 02903, USA.
Ciprian CrainiceanuDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Matthew K SchindlerTranslational Neuroradiology Section, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
Blake DeweyTranslational Neuroradiology Section, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA and Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, USA.
Daniel S ReichTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
Russell T ShinoharaDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA and Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Ani EloyanDepartment of Biostatistics, Brown University, Providence, RI 02903, USA.

Funding

Multimodal MRI in Multiple SclerosisZIANS003119 · NINDS · NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE · PI REICH, DANIEL · 2010 to 2025
$53.4M
The neural basis of sequential control in human and non-human primatesP20GM103645 · NIGMS · BROWN UNIVERSITY · PI WORDEN, MICHAEL S · 2013 to 2022
$22.9M
Spatiotemporal Modeling of MRI Brain Lesion Trajectories of Biomarker DiscoveryR21NS093349 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI ELOYAN, ANI, SHINOHARA, RUSSELL TAKESHI · 2016 to 2017
$454k
NIGMS NIH HHS P20 GM103645NINDS NIH HHS R21 NS093349
6 · The paper itself

Abstract

Our main goal is to study and quantify the evolution of multiple sclerosis lesions observed longitudinally over many years in multi-sequence structural magnetic resonance imaging (sMRI). To achieve that, we propose a class of functional models for capturing the temporal dynamics and spatial distribution of the voxel-specific intensity trajectories in all sMRI sequences. To accommodate the hierarchical data structure (observations nested within voxels, which are nested within lesions, which, in turn, are nested within study participants), we use structured functional principal component analysis. We propose and evaluate the finite sample properties of hypothesis tests of therapeutic intervention effects on lesion evolution while accounting for the multilevel structure of the data. Using this novel testing strategy, we found statistically significant differences in lesion evolution between treatment groups.

Indexed as

Multiple SclerosisBrainHumansMagnetic Resonance ImagingPrincipal Component AnalysisAnalysis of varianceFunctional dataFunctional principal component analysisHierarchical dataHypothesis testingMagnetic resonance imaging

Identifiers

PMID32318692
PMCPMC9118558

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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.