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Big GABA II

Authors :
Alayar Kangarlu
Jacobus F.A. Jansen
Feng Liu
Helge J. Zöllner
Koen Cuypers
David Yen Ting Chen
Muhammad G. Saleh
Sean Noah
Scott O. Murray
David A. Edmondson
Ralph Noeske
Adam J. Woods
Georg Oeltzschner
Fei Gao
Lars Ersland
Richard A.E. Edden
Ian Greenhouse
Peter B. Barker
Mark Mikkelsen
Joanna R. Long
Chien-Yuan E. Lin
Thomas Lange
Naying He
Yan Li
Peter Truong
Ruoyun Ma
Nicolaas A.J. Puts
Niall W. Duncan
Michael Dacko
R. Marc Lebel
Hans-Jörg Wittsack
Guangbin Wang
Kimberly L. Chan
Celine Maes
Martin Tegenthoff
Pallab K. Bhattacharyya
Kim M. Cecil
Diederick Stoffers
Jy-Kang Liou
Gabriele Ende
Michael D. Noseworthy
Pieter F. Buur
Jiing-Feng Lirng
Alexander R. Craven
Stephan P. Swinnen
Michael-Paul Schallmo
Megan A. Forbes
Marta Moreno-Ortega
Stefanie Heba
Chencheng Zhang
James J. Prisciandaro
Iain D. Wilkinson
Markus Sack
Vadim Zipunnikov
Eric C. Porges
Timothy P.L. Roberts
Ulrike Dydak
Tun-Wei Hsu
Nicholas Simard
Ashley D. Harris
Daniel L. Rimbault
Fuhua Yan
Maiken K. Brix
Napapon Sailasuta
Nigel Hoggard
Hongmin Xu
Signal Processing Systems
MUMC+: DA BV Klinisch Fysicus (9)
RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience
Beeldvorming
Spinoza Centre for Neuroimaging
Netherlands Institute for Neuroscience (NIN)
Source :
Neuroimage, 191, 537-548. Academic Press Inc., Neuroimage, 191, 537-548. Elsevier Science, NeuroImage, Neuroimage, NeuroImage, 191, 537-548. Academic Press
Publication Year :
2019

Abstract

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. ispartof: NEUROIMAGE vol:191 pages:537-548 ispartof: location:United States status: published

Details

Language :
English
ISSN :
10538119
Volume :
191
Database :
OpenAIRE
Journal :
Neuroimage
Accession number :
edsair.doi.dedup.....00f5f16bd36d28f8867e485caa965214