The Robustness and the Doubly-Preferential Attachment Simulation of the Consensus Connectome Dynamics of the Human Brain

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.