ArticleNMR in biomedicine2021
In silico GABA+ MEGA-PRESS: Effects of signal-to-noise ratio and linewidth on modeling the 3 ppm GABA+ resonance.
Article in NMR in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- Fatigue in major depression is linked to depression severity, not sleep duration or anterior cingulate cortex neurochemistry.Journal of affective disorders · 2026Article
- Article
- The relationship between visual acuity loss and GABAergic inhibition in amblyopia.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Comparison of seven modelling algorithms for γ-aminobutyric acid-edited proton magnetic resonance spectroscopy.NMR in biomedicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
To investigate the GABA+ modeling accuracy of MEGA-PRESS GABA+-edited MRS data with various spectral quality scenarios, the influence of varying signal-to-noise ratio (SNR) and linewidth on the model estimates was quantified. MEGA-PRESS data from 46 volunteers were averaged to generate a template MEGA-PRESS spectrum, which was modeled and quantified to generate a GABA+ level ground truth. This spectrum was then manipulated by adding 427 combinations of varying artificial noise levels and line broadening, mimicking variations in GABA+ SNR and B
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Registered trials
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.