Evidence map›Paper›PMID 35347460›Full record

ArticleInsights into imaging2022

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging.

Chris W J van der Weijden, Milena S Pitombeira, Yudith R A Haveman, Carlos A Sanchez-Catasus, Kenia R Campanholo, Guilherme D Kolinger, Carolina M Rimkus, Carlos A Buchpiguel, Rudi A J O Dierckx, Remco J Renken and 3 more

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

Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Lesion-Based Disconnectome Explains Cognitive Outcomes in Multiple Sclerosis.Journal of neuroimaging : official journal of the American Society of Neuroimaging
    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

13 authors at 2 institutions in 3 countries.

Chris W J van der WeijdenDepartment of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands.
Milena S PitombeiraDepartment of Neurology, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
Yudith R A HavemanDepartment of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands.
Carlos A Sanchez-CatasusDepartment of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands.
Kenia R CampanholoDepartment of Radiology and Oncology, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
Guilherme D KolingerDepartment of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands.
Carolina M RimkusDepartment of Radiology and Oncology, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
Carlos A BuchpiguelDepartment of Radiology and Oncology, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
Rudi A J O DierckxDepartment of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands.
Remco J RenkenDepartment of Neuroscience, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, The Netherlands.
Jan F MeilofDepartment of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, The Netherlands.
Erik F J de VriesDepartment of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands. e.f.j.de.vries@umcg.nl.ORCID http://orcid.org/0000-0002-6915-1590
Daniele de Paula FariaDepartment of Radiology and Oncology, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
University Medical Center Groningen · NLHospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo · BR

Funding

general electric (GE) Healthcare 12496139131Nederlandse organisatie voor gezondheidsonderzoek en zorginnovatie (ZonMW) PTO-95105010Stichting MS Research PTO-95105010
6 · The paper itself

Abstract

backgroundGraph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying "lesion filling" by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling.

methodsThe study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling.

resultsArtefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients.

conclusionLesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads.

Indexed as

Demyelinating diseasesGraph theoretical network analysisLesion fillingMultiple sclerosisNeurodegenerative diseases

Identifiers

PMID35347460
PMCPMC8960512
OpenAlexW4220868624

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.