Department of Computer Science, Derozio Memorial College, Kolkata, 700136, India.
Department of Chemical Engineering, University of Calcutta, Kolkata, 700009, India.
Biosystems. 2022 Aug;218:104711. doi: 10.1016/j.biosystems.2022.104711. Epub 2022 May 27.
The evolutionary lineage of neuronal phenotype is notably complex even within a limited number of species. One of the approaches resides in the realm of complex network theory. The theory reduces the connectomic data into a hallmarked set of few parameters, some of which might be correlated with a suitably chosen phylogenetic marker. In this first-of-its-kind attempt, interspecific variations of two structural complexity measures (i.e., clustering coefficient and centrality) along with two independent information-theoretic measures (i.e., von Neumann entropy and multifractality) are investigated to decipher any hidden evolutionary signature considering four mammalian connectomes (i.e., felis catus, mus musculus, macaca mulatta, and homo sapiens). All network complexity measures partially corroborate with the phylogenetic order. Nevertheless, monotonicity of the measures with the chosen phylogenetic marker of genome size has been majorly violated because of the mus musculus data point. On the other hand, von Neumann entropy was found to exhibit an allometric scaling behavior with the community structure of all connectomes (p<0.0001, and R>0.95). The respective scaling exponent was noted to be monotonic with the genome size. Singularities of the real connectomes were also investigated upon carrying out a similar analysis in three equivalent synthetic network models.
神经元表型的进化谱系即使在有限的物种数量内也是非常复杂的。其中一种方法是在复杂网络理论领域。该理论将连接组学数据简化为一组标志性的少数参数，其中一些参数可能与选择合适的系统发生标记相关联。在这首次尝试中，我们研究了两种结构复杂性度量（即聚类系数和中心性）以及两种独立的信息论度量（即冯·诺依曼熵和多重分形性）的种间变异，以考虑四个哺乳动物连接组（即猫、鼠、猕猴和人）中的任何隐藏的进化特征。所有网络复杂性度量都部分与系统发生顺序相符。然而，由于鼠的数据点，这些度量与所选的基因组大小的系统发生标记的单调性主要被违反了。另一方面，冯·诺依曼熵被发现与所有连接组的群落结构表现出一种异速缩放行为（p<0.0001，R>0.95）。分别的缩放指数被注意到与基因组大小单调。在对三个等效的合成网络模型进行类似的分析时，也研究了真实连接组的奇点。
