Universidad de Concepción, Department of Electrical Engineering, Faculty of Engineering, Concepción, Chile.
Universidad de Concepción, Department of Computer Science, Faculty of Engineering, Concepción, Chile; Center for Biotechnology and Bioengineering (CeBiB), Chile.
Neuroimage. 2022 Nov 15;262:119550. doi: 10.1016/j.neuroimage.2022.119550. Epub 2022 Aug 6.
The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.
由于短联合纤维在不同个体间的变异性较高，且与已知的长联合束相比，其尺寸更小，因此对短联合纤维的研究仍然是一项尚未完成的任务。然而，对短联合纤维的描述对于理解人类大脑功能障碍和更好地描绘人类大脑连接组至关重要。在这项工作中，我们提出了一种基于纤维聚类的浅层白质束识别方法，使用多主体短联合纤维图谱。为了创建图谱，我们使用了来自 HCP 数据库的一百个主体的概率追踪，通过非线性配准进行对齐。该方法首先对短纤维（30-85 毫米）进行主体内聚类。基于皮质图谱，将所有主体的主体内聚类中心进行分割，以识别连接图谱中每个感兴趣区域（ROI）的中心。为了降低计算负荷，将每个 ROI 组的中心随机分为十个子组。然后，对每个中心子组应用主体间层次聚类，接着对每个 ROI 组进行第二轮聚类，以选择跨主体最具再现性的聚类。最后，根据它们连接的区域对聚类进行标记，并对聚类进行分组以创建最终的束图谱。生成的图谱由整个大脑中 525 束浅层短联合纤维组成，其中 384 束连接不同 ROI 的对，141 束连接同一 ROI 的部分。使用三个不同的轨迹数据库的自动分割验证了束的可重复性。确定性和概率追踪数据的结果显示出高度的可重复性，尤其是在 HCP 数据的概率追踪中。与以前的工作相比，我们的图谱具有更多的束和更大的皮质表面覆盖率。
