Electrical and Computer Engineering, Vanderbilt University, Nashville, USA.
Vanderbilt University Institute of Imaging, Nashville, USA.
Brain Struct Funct. 2022 Jul;227(6):2191-2207. doi: 10.1007/s00429-022-02518-6. Epub 2022 Jun 7.
Efficient communication across fields of research is challenging, especially when they are at opposite ends of the physical and digital spectrum. Neuroanatomy and neuroimaging may seem close to each other. When neuroimaging studies try to isolate structures of interest, according to a specific anatomical definition, a variety of challenges emerge. It is a non-trivial task to convert the neuroanatomical knowledge to instructions and rules to be executed in neuroimaging software. In the process called "virtual dissection" used to isolate coherent white matter structure in tractography, each white matter pathway has its own set of landmarks (regions of interest) used as inclusion and exclusion criteria. The ability to segment and study these pathways is critical for scientific progress, yet, variability may depend on region placement, and be influenced by the person positioning the region (i.e., a rater). When raters' variability is taken into account, the impact made by each region of interest becomes even more difficult to interpret. A delicate balance between anatomical validity, impact on the virtual dissection and raters' reproducibility emerge. In this work, we investigate this balance by leveraging manual delineation data of a group of raters from a previous study to quantify which set of landmarks and criteria contribute most to variability in virtual dissection. To supplement our analysis, the variability of each pathway with a region-by-region exploration was performed. We present a detailed exploration and description of each region, the causes of variability and its impacts. Finally, we provide a brief overview of the lessons learned from our previous virtual dissection projects and propose recommendations for future virtual dissection protocols as well as perspectives to reach better community agreement when it comes to anatomical definitions of white matter pathways.
跨领域的高效沟通具有挑战性，尤其是当它们处于物理和数字领域的两端时。神经解剖学和神经影像学似乎彼此接近。当神经影像学研究试图根据特定的解剖定义来分离感兴趣的结构时，就会出现各种挑战。将神经解剖学知识转换为指令和规则并在神经影像学软件中执行是一项非平凡的任务。在用于在轨迹图中分离连贯的白质结构的“虚拟解剖”过程中，每条白质通路都有自己的一组地标（感兴趣区域）作为包含和排除标准。分割和研究这些通路的能力对科学进步至关重要，但可变性可能取决于区域位置，并受到定位区域的人的影响（即评分者）。当考虑到评分者的可变性时，每个感兴趣区域的影响就更难解释了。在解剖有效性、对虚拟解剖的影响和评分者的可重复性之间需要达到微妙的平衡。在这项工作中，我们通过利用之前研究中一组评分者的手动勾画数据来研究这种平衡，以确定哪些地标和标准对虚拟解剖中的变异性贡献最大。为了补充我们的分析，我们对每个区域的每条通路进行了变异性的区域探索。我们详细地探索和描述了每个区域、变异性的原因及其影响。最后，我们提供了从之前的虚拟解剖项目中吸取的经验教训的简要概述，并提出了未来虚拟解剖协议的建议，以及在涉及白质通路的解剖定义方面达成更好的社区共识的观点。
