Evidence map›Paper›PMID 32112479›Full record

ArticleMagnetic resonance in medicine2020

Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data.

Benjamin T Newman, Thijs Dhollander, Kristen A Reynier, Matthew B Panzer, T Jason Druzgal

Open access · greenAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.4field-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

23 citing papers in PubMed, 39 citations in OpenAlex.

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  10. Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025
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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

5 authors at 3 institutions in 2 countries.

Benjamin T NewmanDepartment of Radiology and Medical Imaging, School of Medicine, University of Virginia, Charlottesville, Virginia.ORCID 0000-0002-0668-2853
Thijs DhollanderDevelopmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
Kristen A ReynierCenter for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, Virginia.
Matthew B PanzerCenter for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, Virginia.
T Jason DruzgalDepartment of Radiology and Medical Imaging, School of Medicine, University of Virginia, Charlottesville, Virginia.ORCID 0000-0001-7240-9487
University of Virginia · USUniversity of Virginia Medical Center · USMurdoch Children's Research Institute · AU

Funding

Neuroimaging Informatics Tools and Resources Clearinghouse Outreach, Infrastructure, and Content MaintenanceU24EB023398 · NIBIB · TCG, INC. · PI KENNEDY, DAVID NELSON, PREUSS, NINA · 2016 to 2018
$1.7M
NITRC Image Repository: A Commercial Solution to Advancing Data Sharing in NeuroscienceR44NS074540 · NINDS · TCG, INC. · PI BUCCIGROSSI, ROBERT, KENNEDY, DAVID NELSON · 2015 to 2017
$1.1M
NITRC Image Repository: A Commercial Solution to Advancing Data Sharing in NeurosR43NS074540 · NINDS · TCG, INC. · PI PREUSS, NINA · 2011 to 2011
$150k
NIBIB NIH HHS U24 EB023398NINDS NIH HHS R43 NS074540NINDS NIH HHS R44 NS074540
6 · The paper itself

Abstract

purposeSeveral recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated.

methodsThis study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters.

resultsThe CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort.

conclusionThe three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

Indexed as

Diffusion Magnetic Resonance ImagingWhite MatterBrainGray MatterHumansReproducibility of ResultsdiffusionmicrostructureMRIreliabilityspherical deconvolution

Identifiers

PMID32112479
PMCPMC7329572
OpenAlexW3008448137

What OpenQuestion holds

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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.