Abstract：
The nucleus basalis of Meynert (nbM) is the major source of cortical acetylcholine (ACh) and has been related to cognitive processes and to neurological disorders. However, spatially delineating the human nbM in MRI studies remains challenging. Due to the absence of a functional localiser for the human nbM, studies to date have localised it using nearby neuroanatomical landmarks or using probabilistic atlases. To understand the feasibility of MRI of the nbM we set our four goals; our first goal was to review current human nbM region-of-interest (ROI) selection protocols used in MRI studies, which we found have reported highly variable nbM volume estimates. Our next goal was to quantify and discuss the limitations of existing atlas-based volumetry of nbM. We found that the identified ROI volume depends heavily on the atlas used and on the probabilistic threshold set. In addition, we found large disparities even for data/studies using the same atlas and threshold. To test whether spatial resolution contributes to volume variability, as our third goal, we developed a novel nbM mask based on the normalized BigBrain dataset. We found that as long as the spatial resolution of the target data was 1.3 mm isotropic or above, our novel nbM mask offered realistic and stable volume estimates. Finally, as our last goal we tried to discern nbM using publicly available and novel high resolution structural MRI ex vivo MRI datasets. We find that, using an optimised 9.4T quantitative T2⁎ ex vivo dataset, the nbM can be visualised using MRI. We conclude caution is needed when applying the current methods of mapping nbM, especially for high resolution MRI data. Direct imaging of the nbM appears feasible and would eliminate the problems we identify, although further development is required to allow such imaging using standard (f)MRI scanning.
摘要：
Meynert的基底核（nbM）是皮质乙酰胆碱（ACh）的主要来源，与认知过程和神经系统疾病有关。然而,在MRI研究中空间描绘人类nbM仍然具有挑战性。由于缺乏人类nbM的功能定位器，迄今为止的研究已经使用附近的神经解剖学标志或使用概率地图集对其进行了定位。为了了解nbMMRI的可行性，我们设定了四个目标；我们的第一个目标是回顾MRI研究中使用的当前人类nbM感兴趣区域（ROI）选择方案，我们发现，报告了高度可变的NBM体积估计值。我们的下一个目标是量化和讨论现有的基于图集的nbM容量的局限性。我们发现所识别的ROI体积在很大程度上取决于所使用的图谱和概率阈值集。此外,即使对于使用相同图集和阈值的数据/研究，我们也发现了巨大的差异。要测试空间分辨率是否有助于体积变异性，作为我们的第三个目标，我们基于归一化的BigBrain数据集开发了一种新的nbM掩码。我们发现，只要目标数据的空间分辨率为1.3mm各向同性或以上，我们的新nbM面具提供了现实和稳定的体积估计。最后,作为我们的最后一个目标，我们试图识别nbM使用公开可用和新颖的高分辨率结构MRI离体MRI数据集。我们发现,使用优化的9.4T定量T2离体数据集，可以使用MRI可视化nbM。我们得出结论，在应用当前的映射nbM的方法时需要谨慎，尤其是高分辨率MRI数据。nbM的直接成像似乎是可行的，可以消除我们发现的问题，尽管需要进一步开发以允许使用标准（f）MRI扫描进行此类成像。
