Evidence map›Paper›PMID 29090480›Full record

ArticleMagnetic resonance in medicine2018

Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle.

David B Berry, Benjamin Regner, Vitaly Galinsky, Samuel R Ward, Lawrence R Frank

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed
–field-weighted citation impact
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

65 citing papers in PubMed.

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5 more citing papers are in PubMed but not listed here.

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

5 authors.

David B BerryDepartment of Bioengineering, University of California San Diego, La Jolla, California, USA.
Benjamin RegnerInstitute of Engineering in Medicine, San Diego, California, USA.
Vitaly GalinskyInstitute of Engineering in Medicine, San Diego, California, USA.
Samuel R WardDepartment of Bioengineering, University of California San Diego, La Jolla, California, USA.
Lawrence R FrankDepartment of Radiology, University of California San Diego, La Jolla, California, USA.

Funding

fMRI Cognition in Youth at Risk for Alcoholism-Diversity SupplementR01AA013419 · NIAAA · VETERANS MEDICAL RESEARCH FDN/SAN DIEGO · PI TAPERT, SUSAN F · 2002 to 2017
$7.1M
New Quantitative Neuroimaging Metrics of Structural and Functional Connectivity of the Locus Coeruleus as a Novel Biomarker of Alzheimer's Disease Pathogenesis and ProgressionR01AG054049 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BONDI, MARK W, FRANK, LAWRENCE R · 2017 to 2021
$4.1M
Non Invasive Measurements of Muscle Microstructures Assessed by Diffusion Tensor ImagingR01AR070830 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI David Barnes Berry, LAWRENCE R FRANK · 2017 to 2026
$4.0M
The contributions of neural control and mechanosensation to rehabilitation induced spine muscle recoveryR01HD088437 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Bahar Shahidi, Samuel Richard Ward · 2017 to 2026
$2.9M
Diffusion Imaging in Gray MatterR01MH096100 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FRANK, LAWRENCE R · 2012 to 2016
$1.9M
The Physiological Basis of Rotator Cuff Muscle RehabilitationR01HD073180 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WARD, SAMUEL RICHARD · 2013 to 2017
$1.5M
NIAAA NIH HHS R01 AA013419NIAMS NIH HHS R01 AR070830NIA NIH HHS R01 AG054049NICHD NIH HHS R01 HD073180NICHD NIH HHS R01 HD088437NIMH NIH HHS R01 MH096100
6 · The paper itself

Abstract

purposeTo establish a series of relationships defining how muscle microstructure and diffusion tensor imaging (DTI) are related.

methodsThe relationship among key microstructural features of skeletal muscle (fiber size, fibrosis, edema, and permeability) and the diffusion tensor were systematically simulated over physiologically relevant dimensions individually, and in combination, using a numerical simulation application. Stepwise multiple regression was used to identify which microstructural features of muscle significantly predict the diffusion tensor using single-echo and multi-echo DTI pulse sequences. Simulations were also performed in models with histology-informed geometry to investigate the relationship between fiber size and the diffusion tensor in models with real muscle geometry.

resultsFiber size is the strongest predictor of λ2, λ3, mean diffusivity, and fractional anisotropy in skeletal muscle, accounting for approximately 40% of the variance in the diffusion model when calculated with single-echo DTI. This increased to approximately 70% when diffusion measures were calculated from the short T

conclusionsAs the normal fiber size of a human muscle fiber is 40 to 60 μm, this suggests that DTI is a sensitive tool to monitor muscle atrophy, but may be limited in measurements of muscle with larger fibers. Magn Reson Med 80:317-329, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Indexed as

Diffusion Tensor ImagingAnimalsAnisotropyComputer SimulationDiffusion Magnetic Resonance ImagingHumansLinear ModelsModels, TheoreticalMonte Carlo MethodMuscle Fibers, SkeletalMuscle, SkeletalMuscular AtrophyNonlinear DynamicsNormal DistributionRatsdiffusionDTImulti-echo DTImuscle microstructuresimulationskeletal muscle

Identifiers

PMID29090480
PMCPMC5876103

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Registered trials

None linked

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.