Evidence map›Paper›PMID 31407532›Full record

ArticleAnnals of clinical and translational neurology2019

Probing axons using multi-compartmental diffusion in multiple sclerosis.

Francesca Bagnato, Giulia Franco, Hua Li, Enrico Kaden, Fei Ye, Run Fan, Amalie Chen, Daniel C Alexander, Seth A Smith, Richard Dortch and 1 more

Open access · goldAbstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
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

15 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Updates in Multiple Sclerosis Imaging.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2025
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Assessing brain injury topographically using MR neurite orientation dispersion and density imaging in multiple sclerosis.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2021
    Article
  11. Article
  12. Article
  13. Challenges for biophysical modeling of microstructure.Journal of neuroscience methods · 2020
    Review
  14. Review
  15. Article
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

11 authors at 6 institutions in 4 countries.

Francesca BagnatoNeuroimaging Unit/Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee.ORCID 0000-0001-9544-9019
Giulia FrancoNeuroimaging Unit/Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee.
Hua LiInstitute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
Enrico KadenCentre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Fei YeDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
Run FanDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
Amalie ChenNeuroimaging Unit/Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee.
Daniel C AlexanderCentre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Seth A SmithInstitute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
Richard DortchInstitute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
Junzhong XuInstitute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
Vanderbilt University Medical Center · USUniversity College London · GBInstitute of Neuroimmunology of the Slovak Academy of Sciences · SKUniversity of Milan · ITVanderbilt University · USVA Tennessee Valley Healthcare System · US

Funding

The Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR000445 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$41.4M
Integrated Imaging of Brain Function at 7 TeslaR01EB000461 · NIBIB · VANDERBILT UNIVERSITY · PI GORE, JOHN C · 2002 to 2012
$9.4M
Replacement and Upgrade of a 3T MR Scanner for ResearchS10OD021771 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GORE, JOHN C · 2016 to 2016
$2.0M
Quantitative MRI of the Human Peripheral Nervous System In VivoK25EB013659 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI DORTCH, RICHARD · 2012 to 2016
$750k
Multi-Modality, Quantitative MRI to Assess the Human Optic Nerve in vivoK01EB009120 · NIBIB · VANDERBILT UNIVERSITY · PI SMITH, SETH A · 2009 to 2013
$663k
Engineering and Physical Sciences Research Council UK EPSRC EP/M020533/1Engineering and Physical Sciences Research Council UK EPSRC EP/N018702/1European Union Horizon 2020 EU H2020 634541-2National Multiple Sclerosis Society NMSS PP-1801-29686National Multiple Sclerosis Society NMSS RG-1501-02840NCATS NIH HHS UL1 TR000445NIBIB NIH HHS K01 EB009120NIBIB NIH HHS K25 EB013659NIBIB NIH HHS R01 EB000461NIH HHS S10 OD021771
6 · The paper itself

Abstract

objectsThe diffusion-based spherical mean technique (SMT) provides a novel model to relate multi-b-value diffusion magnetic resonance imaging (MRI) data to features of tissue microstructure. We propose the first clinical application of SMT to image the brain of patients with multiple sclerosis (MS) and investigate clinical feasibility and translation.

methodsEighteen MS patients and nine age- and sex-matched healthy controls (HCs) underwent a 3.0 Tesla scan inclusive of clinical sequences and SMT images (isotropic resolution of 2 mm). Axial diffusivity (AD), apparent axonal volume fraction (V

resultsDifferences were seen in all SMT-derived parameters between chronic black holes (cBHs) and T2-lesions (P ≤ 0.0016), in V INTERPRETATIONS: SMT-derived metrics are sensitive to pathological changes and hold potential for clinical application in MS patients.

Indexed as

Diffusion Magnetic Resonance ImagingAdultAxonsBrainFemaleHumansMaleMiddle AgedMultiple SclerosisWhite Matter

Identifiers

PMID31407532
PMCPMC6764633
OpenAlexW2968484774

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.