Literature DB >> 28479342

Normalizing data from GABA-edited MEGA-PRESS implementations at 3 Tesla.

Ashley D Harris1, Nicolaas A J Puts2, S Andrea Wijtenburg3, Laura M Rowland4, Mark Mikkelsen5, Peter B Barker2, C John Evans6, Richard A E Edden2.   

Abstract

Standardization of results is an important milestone in the maturation of any truly quantitative methodology. For instance, a lack of measurement agreement across imaging platforms limits multisite studies, between-study comparisons based on the literature, and inferences based on and the generalizability of results. In GABA-edited MEGA-PRESS, two key sources of differences between implementations are: differences in editing efficiency of GABA and the degree of co-editing of macromolecules (MM). In this work, GABA editing efficiency κ and MM-co-editing μ constants are determined for three widely used MEGA-PRESS implementations (on the most common MRI platforms; GE, Philips, and Siemens) by phantom experiments. Implementation-specific κ,μ-corrections were then applied to two in vivo datasets, one consisted of 8 subject scanned on the three platforms and the other one subject scanned eight times on each platform. Manufacturer-specific κ and μ values were determined as: κGE=0.436, κSiemens=0.366 and κPhilips=0.394 and μGE=0.83, μSiemens=0.625 and μPhilips=0.75. Applying the κ,μ-correction on the Cr-referenced data decreased the coefficient of variation (CV) of the data for both in vivo data sets (multisubjects: uncorrected CV=13%, κ,μ-corrected CV=5%, single subject: uncorrected CV=23%, κ,μ-corrected CV=13%) but had no significant effect on mean GABA levels. For the water-referenced results, CV increased in the multisubject data (uncorrected CV=6.7%, κ,μ-corrected CV=14%) while it decreased in the single subject data (uncorrected CV=24%, κ,μ-corrected CV=21%) and manufacturer was a significant source of variance in the κ,μ-corrected data. Applying a correction for editing efficiency and macromolecule contamination decreases the variance between different manufacturers for creatine-referenced data, but other sources of variance remain.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cross-platform; Editing efficiency; GABA; MEGA-PRESS; Macromolecular co-editing; Multi-site

Mesh:

Substances:

Year:  2017        PMID: 28479342      PMCID: PMC5581667          DOI: 10.1016/j.mri.2017.04.013

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  43 in total

1.  Brain GABA editing without macromolecule contamination.

Authors:  P G Henry; C Dautry; P Hantraye; G Bloch
Journal:  Magn Reson Med       Date:  2001-03       Impact factor: 4.668

2.  The need for updates of spin system parameters, illustrated for the case of γ-aminobutyric acid.

Authors:  Roland Kreis; Christine Sandra Bolliger
Journal:  NMR Biomed       Date:  2012-05-14       Impact factor: 4.044

Review 3.  Edited 1 H magnetic resonance spectroscopy in vivo: Methods and metabolites.

Authors:  Ashley D Harris; Muhammad G Saleh; Richard A E Edden
Journal:  Magn Reson Med       Date:  2017-02-02       Impact factor: 4.668

4.  Diurnal stability of gamma-aminobutyric acid concentration in visual and sensorimotor cortex.

Authors:  Christopher John Evans; David John McGonigle; Richard Anthony Edward Edden
Journal:  J Magn Reson Imaging       Date:  2010-01       Impact factor: 4.813

5.  Spectral-editing measurements of GABA in the human brain with and without macromolecule suppression.

Authors:  Ashley D Harris; Nicolaas A J Puts; Peter B Barker; Richard A E Edden
Journal:  Magn Reson Med       Date:  2014-12-17       Impact factor: 4.668

6.  In vivo detection of acute pain-induced changes of GABA+ and Glx in the human brain by using functional 1H MEGA-PRESS MR spectroscopy.

Authors:  Marianne Cleve; Alexander Gussew; Jürgen R Reichenbach
Journal:  Neuroimage       Date:  2014-10-24       Impact factor: 6.556

7.  Creatine abnormalities in schizophrenia and bipolar disorder.

Authors:  Dost Ongür; Andrew P Prescot; J Eric Jensen; Bruce M Cohen; Perry F Renshaw
Journal:  Psychiatry Res       Date:  2009-02-23       Impact factor: 3.222

Review 8.  GABA estimation in the brains of children on the autism spectrum: measurement precision and regional cortical variation.

Authors:  W Gaetz; L Bloy; D J Wang; R G Port; L Blaskey; S E Levy; T P L Roberts
Journal:  Neuroimage       Date:  2013-05-24       Impact factor: 6.556

9.  Reproducibility of GABA measurements using 2D J-resolved magnetic resonance spectroscopy.

Authors:  Katherine Lymer; Kristin Haga; Ian Marshall; Napapon Sailasuta; Joanna Wardlaw
Journal:  Magn Reson Imaging       Date:  2006-11-30       Impact factor: 2.546

10.  Voxel Placement Precision for GABA-Edited Magnetic Resonance Spectroscopy.

Authors:  Xue Bai; Ashley D Harris; Tao Gong; Nicolaas A J Puts; Guangbin Wang; Michael Schär; Peter B Barker; Richard A E Edden
Journal:  Open J Radiol       Date:  2017-03-24
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  7 in total

1.  Multi-vendor standardized sequence for edited magnetic resonance spectroscopy.

Authors:  Muhammad G Saleh; Daniel Rimbault; Mark Mikkelsen; Georg Oeltzschner; Anna M Wang; Dengrong Jiang; Ali Alhamud; Jamie Near; Michael Schär; Ralph Noeske; James B Murdoch; Lars Ersland; Alexander R Craven; Gerard Eric Dwyer; Eli Renate Grüner; Li Pan; Sinyeob Ahn; Richard A E Edden
Journal:  Neuroimage       Date:  2019-01-22       Impact factor: 6.556

2.  Resting GABA concentration predicts inhibitory control during an auditory Go-Nogo task.

Authors:  Chia-Hsiung Cheng; David M Niddam; Shih-Chieh Hsu; Chia-Yih Liu; Shang-Yueh Tsai
Journal:  Exp Brain Res       Date:  2017-10-09       Impact factor: 1.972

3.  Spectral editing in 1 H magnetic resonance spectroscopy: Experts' consensus recommendations.

Authors:  In-Young Choi; Ovidiu C Andronesi; Peter Barker; Wolfgang Bogner; Richard A E Edden; Lana G Kaiser; Phil Lee; Małgorzata Marjańska; Melissa Terpstra; Robin A de Graaf
Journal:  NMR Biomed       Date:  2020-09-18       Impact factor: 4.044

4.  Comparison of seven modelling algorithms for γ-aminobutyric acid-edited proton magnetic resonance spectroscopy.

Authors:  Alexander R Craven; Pallab K Bhattacharyya; William T Clarke; Ulrike Dydak; Richard A E Edden; Lars Ersland; Pravat K Mandal; Mark Mikkelsen; James B Murdoch; Jamie Near; Reuben Rideaux; Deepika Shukla; Min Wang; Martin Wilson; Helge J Zöllner; Kenneth Hugdahl; Georg Oeltzschner
Journal:  NMR Biomed       Date:  2022-02-23       Impact factor: 4.478

5.  Gamma-Aminobutyric Acid Levels in the Anterior Cingulate Cortex of Perimenopausal Women With Depression: A Magnetic Resonance Spectroscopy Study.

Authors:  Dan Wang; Xuan Wang; Meng-Ting Luo; Hui Wang; Yue-Hua Li
Journal:  Front Neurosci       Date:  2019-08-20       Impact factor: 4.677

6.  Kisspeptin modulates gamma-aminobutyric acid levels in the human brain.

Authors:  Alexander N Comninos; Lisa Yang; James O'Callaghan; Edouard G Mills; Matthew B Wall; Lysia Demetriou; Victoria C Wing; Layla Thurston; Bryn M Owen; Ali Abbara; Eugenii A Rabiner; Waljit S Dhillo
Journal:  Psychoneuroendocrinology       Date:  2021-04-26       Impact factor: 4.905

Review 7.  GABA and glutamate in the preterm neonatal brain: In-vivo measurement by magnetic resonance spectroscopy.

Authors:  Sudeepta K Basu; Subechhya Pradhan; Adre J du Plessis; Yehezkel Ben-Ari; Catherine Limperopoulos
Journal:  Neuroimage       Date:  2021-05-28       Impact factor: 6.556

  7 in total

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