Evidence map›Paper›PMID 26295042›Full record

ArticleBioMed research international2015

Multicontrast MRI Quantification of Focal Inflammation and Degeneration in Multiple Sclerosis.

Guillaume Bonnier, Alexis Roche, David Romascano, Samanta Simioni, Djalel Eddine Meskaldji, David Rotzinger, Ying-Chia Lin, Gloria Menegaz, Myriam Schluep, Renaud Du Pasquier and 5 more

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.2field-weighted citation impact, top 9% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

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  13. In Vivo Imaging of Human Neuroinflammation.ACS chemical neuroscience · 2016
    Review
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

15 authors at 6 institutions in 3 countries.

Guillaume BonnierAdvanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Department of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
Alexis RocheAdvanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
David RomascanoAdvanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Samanta SimioniDepartment of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
Djalel Eddine MeskaldjiDepartment of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland ; Medical Image Processing Laboratory (MIPLAB), Institute of Bioengineering, EPFL, 1015 Lausanne, Switzerland.
David RotzingerDepartment of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
Ying-Chia LinDepartment of Computer Science, University of Verona, 37134 Verona, Italy.
Gloria MenegazDepartment of Computer Science, University of Verona, 37134 Verona, Italy.
Myriam SchluepDepartment of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
Renaud Du PasquierDepartment of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
Tilman Johannes SumpfBiomedizinische NMR Forschungs GmbH, Max Planck Institute for Biophysical Chemistry, 37077 Goettingen, Germany.
Jens FrahmBiomedizinische NMR Forschungs GmbH, Max Planck Institute for Biophysical Chemistry, 37077 Goettingen, Germany.
Jean-Philippe ThiranLTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Gunnar KruegerAdvanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Healthcare Sector IM&WS S, Siemens Schweiz AG, 1020 Renens, Switzerland.
Cristina GranzieraAdvanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Department of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland ; Laboratoire de Recherche en Neuroimagerie, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
University Hospital of Lausanne · CHMax Planck Institute for Biophysical Chemistry · DESiemens (Switzerland) · CHUniversity of Verona · ITÉcole Polytechnique Fédérale de Lausanne · CHUniversity of Geneva · CH

Funding

Swiss National Science Foundation 131914
6 · The paper itself

Abstract

introductionLocal microstructural pathology in multiple sclerosis patients might influence their clinical performance. This study applied multicontrast MRI to quantify inflammation and neurodegeneration in MS lesions. We explored the impact of MRI-based lesion pathology in cognition and disability.

methods36 relapsing-remitting MS subjects and 18 healthy controls underwent neurological, cognitive, behavioural examinations and 3 T MRI including (i) fluid attenuated inversion recovery, double inversion recovery, and magnetization-prepared gradient echo for lesion count; (ii) T1, T2, and T2(*) relaxometry and magnetisation transfer imaging for lesion tissue characterization. Lesions were classified according to the extent of inflammation/neurodegeneration. A generalized linear model assessed the contribution of lesion groups to clinical performances.

resultsFour lesion groups were identified and characterized by (1) absence of significant alterations, (2) prevalent inflammation, (3) concomitant inflammation and microdegeneration, and (4) prevalent tissue loss. Groups 1, 3, 4 correlated with general disability (Adj-R (2) = 0.6; P = 0.0005), executive function (Adj-R (2) = 0.5; P = 0.004), verbal memory (Adj-R (2) = 0.4; P = 0.02), and attention (Adj-R (2) = 0.5; P = 0.002).

conclusionMulticontrast MRI provides a new approach to infer in vivo histopathology of plaques. Our results support evidence that neurodegeneration is the major determinant of patients' disability and cognitive dysfunction.

Indexed as

Contrast MediaMagnetic Resonance ImagingAdultCerebellumFemaleHumansInflammationLinear ModelsMaleMultiple SclerosisMultiple Sclerosis, Relapsing-RemittingMultivariate AnalysisNerve DegenerationContrast Media

Identifiers

PMID26295042
PMCPMC4532805
OpenAlexW2097549361

What OpenQuestion holds

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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.