Chung Sohae, Fieremans Els, Novikov Dmitry S, Lui Yvonne W
Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States.
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States.
Brain Struct Funct. 2024 Dec 13;230(1):1. doi: 10.1007/s00429-024-02872-7.
The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as Hofer and Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas known to consist of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC known to consist of a greater number of large diameter fibers. Based on the degree of change in ƒ along the callosum, seven callosal subregions were consistently delineated for each individual. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.
胼胝体（CC）是最重要的半球间白质（WM）结构，由几个在解剖学和功能上不同的WM束组成。由于胼胝体在传统结构成像中显得相对均匀，解析这些束是一项挑战。常用的胼胝体分割方法，如霍弗和弗拉姆方案，依赖于严格的几何准则来分离亚结构，而这些准则仅限于考虑个体差异。在这里，我们提出了一种基于轴突水分数（ƒ）的新型个体特异性且基于微观结构信息的胼胝体分割方法，轴突水分数是一种反映轴突直径和密度的扩散度量。我们使用多壳扩散磁共振成像研究了人类连接体项目数据集中的30名健康受试者。生物物理参数ƒ是从特定区域的WM建模中得出的。通过识别胼胝体上ƒ发生凹陷变化的拐点来描绘胼胝体亚区域。我们观察到，在已知由大量小直径纤维组成的前部和后部区域，ƒ相对较高；而在已知由大量大直径纤维组成的胼胝体后部区域，ƒ较低。基于ƒ沿胼胝体的变化程度，为每个个体一致地描绘出七个胼胝体亚区域。因此，该方法以个体特异性的方式提供基于微观结构信息的胼胝体分割，从而允许对胼胝体进行更准确的分析。
