Evidence map›Paper›PMID 31074686›Full record

ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2020

A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis.

Carmen Tur, Francesco Grussu, Ferran Prados, Thalis Charalambous, Sara Collorone, Baris Kanber, Niamh Cawley, Daniel R Altmann, Sébastien Ourselin, Frederik Barkhof and 4 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

11 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Graph theoretical approach to brain remodeling in multiple sclerosis.Network neuroscience (Cambridge, Mass.) · 2023
    Article
  3. Review
  4. Article
  5. Review
  6. Observational
  7. Article
  8. Article
  9. Review
  10. Article
  11. 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

14 authors at 6 institutions in 2 countries.

Carmen TurQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK.ORCID 0000-0003-1849-3184
Francesco GrussuQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Centre for Medical Image Computing, Department of Computer Science, University College London (UCL), London, UK.ORCID 0000-0002-0945-3909
Ferran PradosQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK.
Thalis CharalambousQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK.ORCID 0000-0002-6919-4486
Sara ColloroneQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK.ORCID 0000-0003-1506-8983
Baris KanberQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK.
Niamh CawleyQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK.
Daniel R AltmannQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London, London, UK.
Sébastien OurselinTranslational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK/School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Frederik BarkhofQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK/Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands/National Institute for Health Research University College London Hospitals Biomedical Research Centre, London, UK.
Jonathan D ClaydenUCL Great Ormond Street Institute of Child Health, University College London (UCL), London, UK.
Ahmed T ToosyQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK.
Claudia Am Gandini Wheeler-KingshottQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy.
Olga CiccarelliQueen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/National Institute for Health Research University College London Hospitals Biomedical Research Centre, London, UK.
National Hospital for Neurology and Neurosurgery · GBUniversity College London · GBNational Institute for Health Research · GBKing's College London · GBUniversity of London · GBUniversity of Pavia · IT

Funding

Medical Research Council MR/J01107X/1Medical Research Council MR/S026088/1Wellcome Trust 203148
6 · The paper itself

Abstract

backgroundThe potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated.

objectiveTo test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols.

methodsNineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients.

resultsPatients had lower mean nodal strength (

conclusionConnectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.

Indexed as

AdultCognitive DysfunctionDiffusion Tensor ImagingFemaleGray MatterHumansMaleMiddle AgedMultiple SclerosisNerve NetRetrospective StudiesWhite Matterclinically isolated syndromeDiffusion-weighted imagingmultiple sclerosismulti-shell acquisitionsmulti-shell multi-tissue constrained spherical deconvolutiontractography

Identifiers

PMID31074686
PMCPMC7611366
OpenAlexW2944389904

What OpenQuestion holds

Textmetadata
LicenceTDM
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.