Evidence map›Paper›PMID 31483562›Full record

ArticleBrain and behavior2019

Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI.

Thiago J R Rezende, Brunno M Campos, Johnny Hsu, Yue Li, Can Ceritoglu, Kwame Kutten, Marcondes C França Junior, Susumu Mori, Michael I Miller, Andreia V Faria

Abstract read
In one paragraph

Article in Brain and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
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

28 citing papers in PubMed.

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  10. Sensitivity of Advanced Magnetic Resonance Imaging to Progression over Six Months in Early Spinocerebellar Ataxia.Movement disorders : official journal of the Movement Disorder Society · 2024
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  20. A Machine Learning-Based Approach to Discrimination of Tauopathies Using [Movement disorders : official journal of the Movement Disorder Society · 2022
    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

10 authors.

Thiago J R RezendeDepartment of Neurology, University of Campinas, Campinas, Brazil.
Brunno M CamposDepartment of Neurology, University of Campinas, Campinas, Brazil.
Johnny HsuDepartment of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland.
Yue LiAnatomyWorks LLC, Baltimore, Maryland.
Can CeritogluDepartment of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland.
Kwame KuttenDepartment of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland.
Marcondes C França JuniorDepartment of Neurology, University of Campinas, Campinas, Brazil.
Susumu MoriDepartment of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland.
Michael I MillerDepartment of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland.
Andreia V FariaDepartment of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland.ORCID 0000-0002-1673-002X

Funding

Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
TR&D 4: Algorithms for functional and anatomical brain analysisP41EB015909 · NIBIB · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI VAN ZIJL, PETER CM · 2012 to 2020
$13.1M
Continued Development and Maintenance of MriStudioR01NS084957 · NINDS · JOHNS HOPKINS UNIVERSITY · PI MILLER, MICHAEL I, MORI, SUSUMU · 2013 to 2022
$3.6M
NIA NIH HHS P30 AG066507NIBIB NIH HHS P41 EB015909NINDS NIH HHS R01 NS084957
6 · The paper itself

Abstract

introductionThe increasing use of large sample sizes for population and personalized medicine requires high-throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science.

methodsWe tested-retested the reproducibility of MRICloud, a free automated method for whole-brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults.

resultsThe reproducibility was extremely high for T1-volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting-state fMRI.

conclusionIn general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure-based MRI studies.

Indexed as

AdultAlgorithmsBrainConnectomeDiffusion Tensor ImagingFemaleFunctional NeuroimagingHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsSoftwareYoung Adultautomated segmentationmultimodality brain MRIreproducibilitytest-retest

Identifiers

PMID31483562
PMCPMC6790328

What OpenQuestion holds

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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.