Importance of Linear Combination Modeling for Quantification of Glutathione and γ-Aminobutyric Acid Levels Using Hadamard-Edited Magnetic Resonance Spectroscopy.
Importance of Linear Combination Modeling for Quantification of Glutathione and γ-Aminobutyric Acid Levels Using Hadamard-Edited Magnetic Resonance Spectroscopy.
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DOI：
10.3389/fpsyt.2022.872403
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发表时间：
2022
影响因子：
4.7
通讯作者：
Edden, Richard
中科院分区：
文献类型：
--
作者：
Song, Yulu;Zollner, Helge J.;Hui, Steve C. N.;Hupfeld, Kathleen;Oeltzschner, Georg;Prisciandaro, James J.;Edden, Richard
关键词：
j -差异编辑1H-MR光谱需要建模来量化低浓度代谢物的信号。光谱建模主要采用两种方法：简单峰拟合和基于模拟基集的线性组合建模（LCM）。最近的共识推荐LCM作为编辑数据光谱分析的选择方法。本研究的目的是比较简单峰拟合和LCM在测试-重测数据集中的性能，假设与简单峰拟合相比，更复杂的LCM方法可以改善hadamard编辑数据的量化。使用Gannet（简单峰值拟合）和Osprey （LCM）对测试重测数据集进行重新分析。这些数据来自12名健康志愿者的背前扣带皮层，HERMES的TE = 80 ms，谷胱甘肽（GSH）的MEGA-PRESS的TE = 120 ms。计算受试者内变异系数（CVs）以量化每种代谢物估计的扫描间再现性。与简单的峰值拟合相比，LCM大大提高了HERMES GSH估计的重现性，CV为19.0-9.9%。对于MEGA-PRESS GSH数据，使用LCM和简单峰拟合的重现性相似，cv分别为7.3和8.8%。两种型号的GABA + cv分别为16.7%和15.2%。具有模拟基函数的LCM大大提高了HERMES数据中GSH定量的可重复性。
J-difference-edited 1H-MR spectra require modeling to quantify signals of low-concentration metabolites. Two main approaches are used for this spectral modeling: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data. The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach would improve quantification of Hadamard-edited data compared with simple peak fitting. A test–retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE = 80 ms for HERMES and TE = 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variation (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate. The reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0–9.9%. For MEGA-PRESS GSH data, reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3 and 8.8%. GABA + CVs from HERMES were 16.7 and 15.2%, respectively for the two models. LCM with simulated basis functions substantially improved the reproducibility of GSH quantification for HERMES data.
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影响因子：
5.7
作者：
Mikkelsen M;Barker PB;Bhattacharyya PK;Brix MK;Buur PF;Cecil KM;Chan KL;Chen DY;Craven AR;Cuypers K;Dacko M;Duncan NW;Dydak U;Edmondson DA;Ende G;Ersland L;Gao F;Greenhouse I;Harris AD;He N;Heba S;Hoggard N;Hsu TW;Jansen JFA;Kangarlu A;Lange T;Lebel RM;Li Y;Lin CE;Liou JK;Lirng JF;Liu F;Ma R;Maes C;Moreno-Ortega M;Murray SO;Noah S;Noeske R;Noseworthy MD;Oeltzschner G;Prisciandaro JJ;Puts NAJ;Roberts TPL;Sack M;Sailasuta N;Saleh MG;Schallmo MP;Simard N;Swinnen SP;Tegenthoff M;Truong P;Wang G;Wilkinson ID;Wittsack HJ;Xu H;Yan F;Zhang C;Zipunnikov V;Zöllner HJ;Edden RAE
通讯作者：
Edden RAE
影响因子：
4.4
作者：
An, Li;Zhang, Yan;Thomasson, David M.;Latour, Lawrence L.;Baker, Eva H.;Shen, Jun;Warach, Steven
通讯作者：
Warach, Steven
影响因子：
3.3
作者：
Marjańska M;McCarten JR;Hodges J;Hemmy LS;Grant A;Deelchand DK;Terpstra M
通讯作者：
Terpstra M
影响因子：
3.3
作者：
KLOSE, U
通讯作者：
KLOSE, U
影响因子：
3.7
作者：
Hupfeld, K. E.;Hyatt, H. W.;Porges, E. C.
通讯作者：
Porges, E. C.