Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

Mark Mikkelsen, Daniel L. Rimbault, Peter B. Barker, Pallab K. Bhattacharyya, Maiken K. Brix, Pieter F. Buur, 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, Gabriele Ende, Lars Ersland, Megan A. Forbes, Fei Gao, Ian GreenhouseAshley D. Harris, Naying He, Stefanie Heba, Nigel Hoggard, Tun Wei Hsu, Jacobus F.A. Jansen, Alayar Kangarlu, Thomas Lange, R. Marc Lebel, Yan 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, Diederick Stoffers, 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

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Accurate and reliable quantification of brain metabolites measured in vivo using 1 H 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 T 1 -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.

Original languageEnglish (US)
Pages (from-to)537-548
Number of pages12
JournalNeuroImage
Volume191
DOIs
StatePublished - May 1 2019

Bibliographical note

Funding Information:
This work was supported by NIH grants R01 EB016089 , R01 EB023963 and P41 EB015909 . Data collection was supported by the Shandong Provincial Key Research and Development Plan of China ( 2016ZDJS07A16 ) and the National Natural Science Foundation of China for Young Scholars (no. 81601479 ). AJW was supported by NIA grants K01 AG050707 and R01 AG054077 and the University of Florida (UF) , the Center for Cognitive Aging and Memory (CAM) and the McKnight Brain Research Foundation (MBRF) . DAE was supported by NIH grant F31 ES028081 . ECP was supported by NIAAA grant K01 AA025306 and UF, CAM and MBRF. HJZ was supported by DFG grant SFB 974 TP B07 . IDW and NH thank The Wellcome Trust , the NIHR-Sheffield Biomedical Research Center and Mrs. J. Bigley of the University of Sheffield MRI Unit for her assistance with data acquisition. JJP was supported by NIAAA grant K23 AA020842 . KMC was supported by NIH grants R01 MH095014 and R01 NS096207 . MPS was supported by NIH grant F32 EY025121 . NAJP receives salary support from NIH grant R00 MH107719 . SPS was supported by the Research Foundation - Flanders ( G089818N ), the Excellence of Science ( EOS, 30446199 , MEMODYN) and the KU Leuven Research Fund ( C16/15/070 ). The authors acknowledge implementation contributions from a number of employees of Siemens Medical Solutions, including Dr. Keith Heberlein and Dr. Sinyeob Ahn, to the Siemens WIP sequences, which are shared with several research sites under sequence-specific agreements.

Funding Information:
This work was supported by NIH grants R01 EB016089, R01 EB023963 and P41 EB015909. Data collection was supported by the Shandong Provincial Key Research and Development Plan of China (2016ZDJS07A16) and the National Natural Science Foundation of China for Young Scholars (no. 81601479). AJW was supported by NIA grants K01 AG050707 and R01 AG054077 and the University of Florida (UF), the Center for Cognitive Aging and Memory (CAM) and the McKnight Brain Research Foundation (MBRF). DAE was supported by NIH grant F31 ES028081. ECP was supported by NIAAA grant K01 AA025306 and UF, CAM and MBRF. HJZ was supported by DFG grant SFB 974 TP B07. IDW and NH thank The Wellcome Trust, the NIHR-Sheffield Biomedical Research Center and Mrs. J. Bigley of the University of Sheffield MRI Unit for her assistance with data acquisition. JJP was supported by NIAAA grant K23 AA020842. KMC was supported by NIH grants R01 MH095014 and R01 NS096207. MPS was supported by NIH grant F32 EY025121. NAJP receives salary support from NIH grant R00 MH107719. SPS was supported by the Research Foundation - Flanders (G089818N), the Excellence of Science (EOS, 30446199, MEMODYN) and the KU Leuven Research Fund (C16/15/070). The authors acknowledge implementation contributions from a number of employees of Siemens Medical Solutions, including Dr. Keith Heberlein and Dr. Sinyeob Ahn, to the Siemens WIP sequences, which are shared with several research sites under sequence-specific agreements.

Publisher Copyright:
© 2019

Keywords

  • Editing
  • GABA
  • MEGA-PRESS
  • MRS
  • Quantification
  • Tissue correction

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