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Big GABA II

Publication year 2019
Published in NeuroImage
Authors P.F. Buur, D. Stoffers, Mark Mikkelsen, Daniel L Rimbault, Peter B Barker, Pallab K Bhattacharyya, Maiken K Brix, Kim M Cecil, Kimberly L Chan, David Y-T Chen, Alexander R Craven, Koen Cuypers, Michael Dacko, Niall W Duncan, Ulrike Dydak, David A Edmondson, G. Ende, Lars Ersland, Megan A Forbes, Fei Gao, Ian Greenhouse, Ashley D Harris, Naying He, Stefanie Heba, Nigel Hoggard, Tun-Wei Hsu, Jacobus F A Jansen, Alayar Kangarlu, Thomas Lange, R Marc Lebel, Yan-Mei Li, Chien-Yuan E Lin, Jy-Kang Liou, Jiing-Feng Lirng, Feng Liu, Joanna R Long, Ruoyun Ma, Celine Maes, Marta Moreno-Ortega, Scott O Murray, Sean Noah, Ralph Noeske, Michael D Noseworthy, Georg Oeltzschner, Eric C Porges, James J Prisciandaro, Nicolaas A J Puts, Timothy P L Roberts, Markus Sack, Napapon Sailasuta, Muhammad G Saleh, Michael-Paul Schallmo, Nicholas Simard, Stephan P Swinnen, Martin Tegenthoff, Peter Truong, Guangbin Wang, Iain D Wilkinson, Hans-Jörg Wittsack, Adam J Woods, Hongmin Xu, Fuhua Yan, Chencheng Zhang, Vadim Zipunnikov, Helge J Zöllner, Richard A E Edden,
The order of authors may deviate from the original publication due to temporary technical issues.

Accurate and reliable quantification of brain metabolites measured in vivo using 1H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T1-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels.

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