Keresztes László, Szögi Evelin, Varga Bálint, Grolmusz Vince
PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
Uratim Ltd., H-1118 Budapest, Hungary.
Cogn Neurodyn. 2021 Dec;15(6):949-959. doi: 10.1007/s11571-021-09687-w. Epub 2021 Jul 15.
For more than a decade now, we can discover and study thousands of cerebral connections with the application of diffusion magnetic resonance imaging (dMRI) techniques and the accompanying algorithmic workflow. While numerous connectomical results were published enlightening the relation between the braingraph and certain biological, medical, and psychological properties, it is still a great challenge to identify a small number of brain connections closely related to those conditions. In the present contribution, by applying the 1200 Subjects Release of the Human Connectome Project (HCP) and Support Vector Machines, we identify just 102 connections out of the total number of 1950 connections in the 83-vertex graphs of 1064 subjects, which-by a simple linear test-precisely, without any error determine the sex of the subject. Next, we re-scaled the weights of the edges-corresponding to the discovered fibers-to be between 0 and 1, and, very surprisingly, we were able to identify two graph edges out of these 102, such that, if their weights are both 1, then the connectome always belongs to a female subject, independently of the other edges. Similarly, we have identified 3 edges from these 102, whose weights, if two of them are 1 and one is 0, imply that the graph belongs to a male subject-again, independently of the other edges. We call the former 2 edges superfeminine and the first two of the 3 edges supermasculine edges of the human connectome. Even more interestingly, the edge, connecting the right Pars Triangularis and the right Superior Parietal areas, is one of the 2 superfeminine edges, and it is also the third edge, accompanying the two supermasculine connections if its weight is 0; therefore, it is also a "switching" edge. Identifying such edge-sets of distinction is the unprecedented result of this work.
The online version contains supplementary material available at 10.1007/s11571-021-09687-w.
十多年来,通过应用扩散磁共振成像(dMRI)技术及相关算法流程,我们能够发现并研究数千条脑连接。尽管已有众多关于连接组学的结果发表,揭示了脑图谱与某些生物学、医学和心理学特性之间的关系,但识别与这些状况密切相关的少数脑连接仍是一项巨大挑战。在本研究中,通过应用人类连接组计划(HCP)的1200名受试者数据集和支持向量机,我们从1064名受试者的83个顶点图中的1950条连接总数中仅识别出102条连接,通过一个简单的线性测试,能精确无误地确定受试者的性别。接下来,我们将与发现的纤维相对应的边的权重重新缩放至0到1之间,令人惊讶的是,我们能够从这102条边中识别出两条图边,使得如果它们的权重都为1,那么连接组总是属于女性受试者,与其他边无关。同样,我们从这102条边中识别出3条边,如果其中两条权重为1且一条为0,那么该图属于男性受试者——同样与其他边无关。我们将前两条边称为人类连接组的超女性边,后3条边中的前两条称为超男性边。更有趣的是,连接右侧三角区和右侧顶上叶区域的边是两条超女性边之一,并且如果其权重为0,它也是伴随两条超男性连接的第三条边;因此,它也是一条“切换”边。识别这样的区分边集是这项工作前所未有的成果。
在线版本包含可在10.1007/s11571-021-09687-w获取的补充材料。