Self-organization of quantum entropy in evolutionary studies in mammalian brain networks

Abstract

Characterization of complex brain connectomic datasets and feature extraction thereof is progressively being attempted over the last decade. Intra-class evolutionary layout of such networks is regarded as one of the upcoming research domains of high importance. However, appropriate evolutionary profile generation requires thorough exploration of classical as well as non-classical graph theoretic properties. In this study, scaling parameter associated to graph community-wise distribution of quantum von Neumann entropy was found to be unambiguously correlated (𝑅 2 ≈ 0.99) to two phylogenetic markers (long non-coding genes and gene transcripts). Segmental connectivity datasets of different brain regions pertaining to six mammalian species were considered for this purpose. Furthermore, two classical network properties (clustering coefficient and closeness centrality) were demonstrated to fail in generating such an intra-mammalian evolutionary profile. Outcomes of this investigation justifies the efficacy of complex graph theoretic property in development of quantitative brain connectivity locus according to evolutionary selection.