A measure for brain complexity: relating functional segregation and integration in the nervous system.

TLDR

In higher vertebrate brains, local areas exhibit functional segregation that sharply contrasts with their global integration during perception and behavior. The paper introduces neural complexity (CN), a measure that captures the interplay between functional segregation and integration in the brain. CN is defined by estimating the average deviation from statistical independence across subsets of increasing size—using independence of small subsets to quantify segregation and deviations in large subsets to quantify integration—and is applied in simulations of cortical areas to study how neuroanatomical principles constrain brain dynamics. CN is high when segregation and integration coexist, low when components are fully independent or fully dependent, and cortical connectivity patterns characterized by dense, locally organized, patchy, and reciprocal connections yield high CN, indicating the measure could be useful for analyzing complexity in other biological systems.

Abstract

In brains of higher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global integration during perception and behavior. In this paper, we introduce a measure, called neural complexity (CN), that captures the interplay between these two fundamental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of significant deviations from independence of large subsets. CN is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. CN is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity organizing cells into neuronal groups, patchiness in the connectivity among neuronal groups, and prevalent reciprocal connections, are associated with high values of CN. The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.