PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
Institute for Computer Science and Control, H-1111 Budapest, Hungary.
PLoS One. 2019 Apr 16;14(4):e0215473. doi: 10.1371/journal.pone.0215473. eCollection 2019.
Here we show a method of directing the edges of the connectomes, prepared from HARDI datasets from the human brain. Before the present work, no high-definition directed braingraphs were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the "Consensus Connectome Dynamics", described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed connectomes publicly available at the site http://braingraph.org. We also show the robustness of our edge directing method in four independently chosen connectome datasets: we have found that 86% of the edges, which were present in all four datasets, get the same directions in all datasets; therefore the direction method is robust. While our new edge-directing method still needs more empirical validation, we think that our present contribution opens up new possibilities in the analysis of the high-definition human connectome.
在这里,我们展示了一种从人类大脑的 HARDI 数据集制备连接组学边缘的方法。在本工作之前,没有发表过任何高清晰度的有向脑图,因为使用的追踪方法无法为发现的神经束分配方向。之前关于功能连接组学的工作应用了低分辨率的功能 MRI 检测统计因果关系,为通常有几十个顶点的连接组学分配方向。我们的方法基于我们研究小组之前描述的“共识连接组动力学”现象。在本研究中,我们将该方法应用于从公开的人类连接组计划发布中计算的 423 个脑图,每个脑图都有 1015 个顶点,我们还在 http://braingraph.org 网站上公开了有向连接组学。我们还展示了我们的边缘定向方法在四个独立选择的连接组数据集的稳健性:我们发现,在所有四个数据集都存在的 86%的边缘,在所有数据集都具有相同的方向;因此,该定向方法是稳健的。虽然我们的新边缘定向方法仍需要更多的经验验证,但我们认为我们目前的贡献为分析高清晰度的人类连接组学开辟了新的可能性。