Department of Computer Science and Technology, Shandong University, Shandong, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hills, North Carolina.
Hum Brain Mapp. 2019 Sep;40(13):3881-3899. doi: 10.1002/hbm.24637. Epub 2019 May 20.
Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface-based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data-driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic "growth patterns" of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age-specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.
在基于表面的神经影像学分析中，在皮质表面图谱上定义具有解剖学和功能意义的分区图非常重要。传统的皮质分区图通常是基于成人表面图谱中的解剖学皮质折叠标志定义的。然而，它们不适合用于胎儿大脑研究，因为成人和胎儿的大脑大小、形状和性质存在巨大差异。为了解决这个问题，我们提出了一种新的基于群体的方法，用于根据皮质属性（例如表面积）的动态“生长模式”将胎儿皮质表面图谱划分为不同的区域。我们的动机是，皮质属性的生长模式表明了微观结构的快速变化，而微观结构决定了皮质的分子和功能原理。因此，生长模式非常适合定义发育、结构和功能中的不同皮质区域。为了全面捕捉顶点之间皮质生长模式的相似性，我们构建了两个互补的相似性矩阵。一个直接基于顶点的生长轨迹，另一个基于顶点生长轨迹与一组参考点之间的相关轮廓。然后，我们将这两个相似性矩阵非线性地融合成一个单一的矩阵，这比简单地平均它们可以更好地捕捉它们的共同和互补信息。最后，基于这个融合的相似性矩阵，我们进行谱聚类，将胎儿皮质表面图谱划分为不同的区域。通过将我们的方法应用于 26 至 29 孕周的 25 个正常胎儿，我们构建了基于皮质生长模式的具有生物学意义的分区图的特定年龄的胎儿皮质表面图谱。重要的是，我们生成的分区图揭示了胎儿皮质表面发育的空间连续、层次和双侧相对对称的模式。
