Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA.
NMR Biomed. 2022 Jan;35(1):e4618. doi: 10.1002/nbm.4618. Epub 2021 Sep 23.
J-difference-edited spectroscopy is a valuable approach for the in vivo detection of γ-aminobutyric-acid (GABA) with magnetic resonance spectroscopy (MRS). A recent expert consensus article recommends linear combination modeling (LCM) of edited MRS but does not give specific details regarding implementation. This study explores different modeling strategies to adapt LCM for GABA-edited MRS. Sixty-one medial parietal lobe GABA-edited MEGA-PRESS spectra from a recent 3-T multisite study were modeled using 102 different strategies combining six different approaches to account for co-edited macromolecules (MMs), three modeling ranges, three baseline knot spacings, and the use of basis sets with or without homocarnosine. The resulting GABA and GABA+ estimates (quantified relative to total creatine), the residuals at different ranges, standard deviations and coefficients of variation (CVs), and Akaike information criteria, were used to evaluate the models' performance. Significantly different GABA+ and GABA estimates were found when a well-parameterized MM basis function was included in the model. The mean GABA estimates were significantly lower when modeling MM , while the CVs were similar. A sparser spline knot spacing led to lower variation in the GABA and GABA+ estimates, and a narrower modeling range-only including the signals of interest-did not substantially improve or degrade modeling performance. Additionally, the results suggest that LCM can separate GABA and the underlying co-edited MM . Incorporating homocarnosine into the modeling did not significantly improve variance in GABA+ estimates. In conclusion, GABA-edited MRS is most appropriately quantified by LCM with a well-parameterized co-edited MM basis function with a constraint to the nonoverlapped MM , in combination with a sparse spline knot spacing (0.55 ppm) and a modeling range of 0.5-4 ppm.
J 差值编辑波谱是一种利用磁共振波谱（MRS）对γ-氨基丁酸（GABA）进行体内检测的有效方法。最近的一篇专家共识文章建议使用线性组合建模（LCM）对编辑后的 MRS 进行建模，但没有给出具体的实施细节。本研究探索了不同的建模策略，以适应 GABA 编辑 MRS 的 LCM。最近在 3T 多中心研究中，使用 102 种不同的策略对 61 个内侧顶叶 GABA 编辑 MEGA-PRESS 光谱进行建模，这些策略结合了六种不同的方法来解释共编辑大分子（MM），三种建模范围，三种基线结间距，以及使用或不使用同型瓜氨酸的基函数集。通过比较不同策略的 GABA 和 GABA+估计值（相对于总肌酸进行定量）、不同范围内的残差、标准偏差和变异系数（CV）以及赤池信息量准则，评估了模型的性能。当模型中包含参数良好的 MM 基函数时，发现 GABA+和 GABA 的估计值存在显著差异。当对 MM 进行建模时，GABA 的平均估计值明显较低，而 CV 则相似。更稀疏的样条结间距导致 GABA 和 GABA+估计值的变化较小，仅包括感兴趣信号的较窄建模范围不会显著改善或降低建模性能。此外，结果表明 LCM 可以分离 GABA 和潜在的共编辑 MM。将同型瓜氨酸纳入模型中并没有显著改善 GABA+估计值的方差。总之，使用参数良好的共编辑 MM 基函数和非重叠 MM 的约束的 LCM 对 GABA 编辑 MRS 进行最适当的定量，同时结合稀疏样条结间距（0.55 ppm）和 0.5-4 ppm 的建模范围。
