Evidence map›Paper›PMID 32818883›Full record

SynthesisNeuroImage. Clinical2020

Magnetic resonance imaging in multiple sclerosis animal models: A systematic review, meta-analysis, and white paper.

Benjamin V Ineichen, Pascal Sati, Tobias Granberg, Martina Absinta, Nathanael J Lee, Jennifer A Lefeuvre, Daniel S Reich

Open access · goldAbstract readMeta-AnalysisSystematic ReviewConsensus Statement
In one paragraph

Synthesis in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 27% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. 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

7 authors at 2 institutions in 2 countries.

Benjamin V IneichenTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States. Electronic address: ineichen@protonmail.ch.
Pascal SatiTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States.
Tobias GranbergDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden Division of Neuroradiology, Department of Radiology, Karolinska University Hospital, Stockholm, Sweden.
Martina AbsintaTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States.
Nathanael J LeeTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States.
Jennifer A LefeuvreTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States.
Daniel S ReichTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States.
National Institutes of Health · USKarolinska University Hospital · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) is the most important paraclinical tool for assessing drug response in multiple sclerosis (MS) clinical trials. As such, MRI has also been widely used in preclinical research to investigate drug efficacy and pathogenic aspects in MS animal models. Keeping track of all published preclinical imaging studies, and possible new therapeutic approaches, has become difficult considering the abundance of studies. Moreover, comparisons between studies are hampered by methodological differences, especially since small differences in an MRI protocol can lead to large differences in tissue contrast. We therefore provide a comprehensive qualitative overview of preclinical MRI studies in the field of neuroinflammatory and demyelinating diseases, aiming to summarize experimental setup, MRI methodology, and risk of bias. We also provide estimates of the effects of tested therapeutic interventions by a meta-analysis. Finally, to improve the standardization of preclinical experiments, we propose guidelines on technical aspects of MRI and reporting that can serve as a framework for future preclinical studies using MRI in MS animal models. By implementing these guidelines, clinical translation of findings will be facilitated, and could possibly reduce experimental animal numbers.

Indexed as

Magnetic Resonance ImagingMultiple SclerosisAnimalsHumansMiceRatsAnimal modelsGuidelinesMagnetic resonance imaging (MRI)Meta-analysisMultiple sclerosis (MS)Systematic review

Identifiers

PMID32818883
PMCPMC7451445
OpenAlexW3046896326

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.