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2022, NeuroImage. DOI: 10.1016/j.neuroimage.2022.119550
Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data
Claudio Román , Cecilia Hernández , Miguel Figueroa , Josselin Houenou , Cyril Poupon , Jean-François Mangin , Pamela Guevara
Abstract:
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.
2022-08-31 11:02:00
#paper doi.org/10.1016/j.neuroimage.2022.119550 NeuroImage, 2022, Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data. 与已知的长联络纤维束相比,短联络纤维束具有更高的被试间变异性和更小的尺寸,因此对对短联络纤维束的研究仍是一个未完成的任务。然而,它们的描述对于理解人类大脑功能障碍和更好地描述人类大脑连接体是必不可少的。在这项工作中,作者提出了一个短联络纤维的多被试脑图谱,它是使用基于纤维束聚类的浅表层白质识别方法计算的。为了创建脑图谱,作者使用了来自HCP数据库的100名受试者的概率纤维追踪束图,并用非线性配准的方式将它们对齐。该方法从被试内的短联络纤维(30~50 mm)聚类开始。在皮层脑图谱的基础上,对来自所有受试者的簇内质心进行分割,以识别连接图谱中每个感兴趣区域的质心。为了减少计算量,将每个ROI组的质心随机分成10个子组。然后,对每个中心子组应用被试间层次聚类,然后再进行第二级聚类,为每个ROI组选择被试间最可重复的聚类。最后,根据它们连接的区域对类别进行标记,并进行聚类以创建最终的纤维束图。最终的图谱由525束沿整个大脑的浅表层短联络纤维组成,其中384束连接不同的ROI,141束连接相同ROI的部分。在三个不同的束图数据库上使用自动分割方法验证了束的可重复性。确定性和概率性追踪结果具有较高的可重现性,尤其是HCP数据中的概率性追踪。与之前的研究相比,我们的图谱具有更多的束和更大的皮层表面覆盖。
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