Uncovering the invariant structural organization of the human connectome

TL;DR

This study identifies invariant structural patterns in human connectomes, introduces a link-specific parameter for rescaling weights, and improves structure-function mapping by creating a representative brain network.

Contribution

It reveals a link-specific parameter that captures variability and invariance in human brain connectivity, enabling the creation of a generic, representative connectome.

Findings

Link occurrence frequency correlates with average link weight.

A single parameter can explain link variability across individuals.

Rescaled connectomes improve structure-function correspondence.

Abstract

In order to understand the complex cognitive functions of the human brain, it is essential to study the structural connectome, i.e., the wiring of different brain regions to each other through axonal pathways. However, the high degree of plasticity and cross-population variability in human brains makes it difficult to relate structure to function, motivating a search for invariant patterns in the connectivity. At the same time, variability within a population can provide information about generative mechanisms. In this paper we analyze the connection topology and link-weight distribution of human structural connectomes obtained from a database comprising 196 subjects. By demonstrating a correspondence between the occurrence frequency of individual links and their average weight across the population, we show that the process by which the brain is wired is not independent of the process by which the link weights of the connectome are determined. Furthermore, using the specific distribution of the weights associated with each link over the entire population, we show that a single parameter that is specific to a link can account for its frequency of occurrence, as well as, the variation in its weight across different subjects. This parameter provides a basis for ``rescaling'' the link weights in each connectome, allowing us to obtain a generic network representative of the human brain, distinct from a simple average over the connectomes. We obtain functional connectomes by implementing a neural mass model on each of the vertices of the corresponding structural connectomes. By comparing with the empirical functional brain networks, we demonstrate that the rescaling procedure yields a closer structure-function correspondence. Finally, we show that the representative network can be decomposed into a basal component that is stable across the population and a highly variable superstructure.