Okada Tomohisa, Kuribayashi Hideto, Urushibata Yuta, Fujimoto Koji, Akasaka Thai, Seethamraju Ravi Teja, Ahn Sinyeob, Isa Tadashi
Human Brain Research Center, Tokyo, Japan.
Siemens Healthcare, Tokyo, Japan.
Heliyon. 2023 Jul 15;9(7):e18357. doi: 10.1016/j.heliyon.2023.e18357. eCollection 2023 Jul.
Macromolecules (MMs) affect the precision and accuracy of neurochemical quantification in magnetic resonance spectroscopy. A measured MM basis is increasingly used in LCModel analysis combined with a spline baseline, whose stiffness is controlled by a parameter named DKNTMN. The effects of measured MM basis and DKNTMN were investigated.
Twenty-six healthy subjects were prospectively enrolled and scanned twice using a short echo-time Stimulated Echo Acquisition Mode (STEAM) at 7-T. Using LCModel, analyses were conducted using the simulated MM basis (MMsim) with DKNTMN 0.15 and an MM basis measured inhouse (MMmeas) with DKNTMN of 0.15, 0.30, 0.60 and 1.00. Cramér-Rao lower bound (CRLB) and the concentrations of gamma-aminobutyric acid (GABA), glutamate and excitatory-inhibitory ratio (EIR), in addition to MMs were statistically analyzed. Measurement stability was evaluated using coefficient of variation (CV).
CRLBs of GABA were significantly lower when using MMsim than MMmeas; those of glutamate were 2-3. GABA concentrations were significantly higher in the analysis using MMsim than MMmeas where concentrations were significantly higher with DKNTMN of 0.15 or 0.30 than 0.60 or 1.00. Difference in glutamate concentration was not significant. EIRs showed the same difference as in GABA depending on the DKNTMN values. CVs between test-retest scans were relatively stable for glutamate but became larger as DKNTMN increased for GABA and EIR.
Neurochemical quantification depends on the parameters of the basis sets used for fitting. Analysis using MMmeas with DKNTMN of 0.30 conformed best to previous studies and is recommended.
大分子（MMs）会影响磁共振波谱中神经化学物质定量的精度和准确性。在结合样条基线的LCModel分析中，越来越多地使用测量得到的MM基础，样条基线的刚度由一个名为DKNTMN的参数控制。研究了测量得到的MM基础和DKNTMN的影响。
前瞻性招募了26名健康受试者，并在7-T场强下使用短回波时间刺激回波采集模式（STEAM）进行了两次扫描。使用LCModel，分别采用DKNTMN为0.15的模拟MM基础（MMsim）以及DKNTMN分别为0.15、0.30、0.60和1.00的内部测量MM基础（MMmeas）进行分析。除了MMs外，还对克莱姆-拉奥下界（CRLB）以及γ-氨基丁酸（GABA）、谷氨酸的浓度和兴奋抑制比（EIR）进行了统计分析。使用变异系数（CV）评估测量稳定性。
使用MMsim时GABA的CRLB显著低于使用MMmeas时的；谷氨酸的CRLB则低2-3。使用MMsim分析时GABA浓度显著高于使用MMmeas时的，其中DKNTMN为0.15或0.30时的浓度显著高于0.60或1.00时的。谷氨酸浓度差异不显著。根据DKNTMN值，EIRs显示出与GABA相同的差异。重测扫描之间谷氨酸的CV相对稳定，但GABA和EIR的CV随着DKNTMN的增加而变大。
神经化学物质定量取决于用于拟合的基集参数。使用DKNTMN为0.30的MMmeas进行分析最符合先前的研究，因此推荐使用。
