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Abstract：
Consensus Connectome Dynamics (CCD) is a remarkable phenomenon of the human connectomes (braingraphs) that was discovered by continuously decreasing the minimum confidence-parameter at the graphical interface of the Budapest Reference Connectome Server, which depicts the cerebral connections of n = 418 subjects with a frequency-parameter k: For any k = 1, 2, …, n one can view the graph of the edges that are present in at least k connectomes. If parameter k is decreased one-by-one from k = n through k = 1 then more and more edges appear in the graph, since the inclusion condition is relaxed. The surprising observation is that the appearance of the edges is far from random: it resembles a growing, complex structure. We hypothesize that this growing structure copies the axonal development of the human brain. Here we show the robustness of the CCD phenomenon: it is almost independent of the particular choice of the set of underlying connectomes. This result shows that the CCD phenomenon is most likely a biological property of the human brain and not just a property of the data sets examined. We also present a simulation that well-describes the growth of the CCD structure: in our random graph model a doubly-preferential attachment distribution is found to mimic the CCD.
摘要：
ConsensusConnectomeDynamics（CCD）是人类连接体（脑图）的显着现象，它是通过不断降低布达佩斯参考Connectome服务器图形界面上的最小置信度参数而发现的。它描述了n=418名受试者的大脑连接，频率参数为k：对于任何k=1，2，...，可以查看至少k个连接体中存在的边的图。如果参数k从k=n到k=1逐个减小，则在图中出现越来越多的边，由于纳入条件放宽。令人惊讶的观察是，边缘的外观远非随机的：它类似于生长，复杂的结构。我们假设这种生长的结构复制了人脑的轴突发育。在这里，我们展示了CCD现象的鲁棒性：它几乎与基础连接组的特定选择无关。该结果表明，CCD现象很可能是人脑的生物学特性，而不仅仅是所检查的数据集的特性。我们还提供了一个模拟，可以很好地描述CCD结构的增长：在我们的随机图模型中，发现了双重优先附着分布来模仿CCD。
