Analysis of brain functional networks based on inner composition alignment

Abstract

The study of brain networks usually analyze the difference of the statistical data and topological structure between pathological with normal brain, or study the difference of complex networks in different physiological state. This paper presents an inner composition alignment algorithm (IOTA) to study the complexity and differences of the brain function network of different ages in beta rhythm, study the topological characteristics in younger and old by computing the characteristics through algorithm: the average path length, clustering coefficient, the average node degree and inner composition alignment algorithm coefficient.