Evidence map›Paper›PMID 29802635›Full record

ArticleMagnetic resonance in medicine2018

Reproducibility of 2D GluCEST in healthy human volunteers at 7 T.

Ravi Prakash Reddy Nanga, Catherine DeBrosse, Dushyant Kumar, David Roalf, Brendan McGeehan, Kevin D'Aquilla, Arijitt Borthakur, Hari Hariharan, Damodara Reddy, Mark Elliott and 3 more

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
–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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Transformer Enabled Half Z‑spectrum Sampling BChemical & biomedical imaging · 2026
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  6. In vivoMagnetic resonance in medicine · 2024
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  7. Mapping hippocampal glutamate in healthy aging withFrontiers in aging neuroscience · 2024
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  11. Repeatability of BMagnetic resonance in medicine · 2022
    Article
  12. IntegratingNeuroImage · 2022
    Article
  13. Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Accelerating GluCEST imaging using deep learning for BMagnetic resonance in medicine · 2020
    Article
  19. Article
  20. 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.

Ravi Prakash Reddy NangaDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Catherine DeBrosseDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Dushyant KumarDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
David RoalfDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Brendan McGeehanDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Kevin D'AquillaDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Arijitt BorthakurDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Hari HariharanDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Damodara ReddyDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Mark ElliottDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
John A DetreDepartment of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Cynthia Neill EppersonDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Ravinder ReddyDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.

Funding

Training and DisseminationP41EB015893 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI REDDY, RAVINDER · 2012 to 2019
$11.3M
Multimodal Imaging of Progesterone/Neurosteroid Effects in Nicotine AddictionR01DA037289 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI EPPERSON, C. NEILL · 2015 to 2019
$3.3M
A Novel Method for Glutamate ImagingR01NS087516 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI REDDY, RAVINDER · 2014 to 2018
$1.7M
NIBIB NIH HHS P41 EB015893NIDA NIH HHS R01 DA037289NINDS NIH HHS R01 NS087516
6 · The paper itself

Abstract

purposeTo investigate the reproducibility of gray and white matter glutamate contrast of a brain slice among a small group of healthy volunteers by using the 2D single-slice glutamate CEST (GluCEST) imaging technique.

methodsSix healthy volunteers were scanned multiple times for within-day and between-day reproducibility. One more volunteer was scanned for within-day reproducibility at 7T MRI. Glutamate CEST contrast measurements were calculated for within subjects and among the subjects and the coefficient of variations are reported.

resultsThe GluCEST measurements were highly reproducible in the gray and white matter area of the brain slice, whether it was within-day or between-day with a coefficient of variation of less than 5%.

conclusionThis preliminary study in a small group of healthy volunteers shows a high degree of reproducibility of GluCEST MRI in brain and holds promise for implementation in studying age-dependent changes in the brain.

Indexed as

AdultAgedBrainFemaleGlutamic AcidHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsGlutamic AcidGluCESTGluCEST reproducibilityglutamate

Identifiers

PMID29802635
PMCPMC6107408

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.