Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

TL;DR

This study uses complex network theory and novel analytical methods to uncover the robust hierarchical modular structure of the mouse brain's functional connectivity from resting-state fMRI data.

Contribution

It introduces a new percolation analysis and applies the Minimal Spanning Forest technique to reveal hierarchical modules and their internal correlation strengths.

Findings

Hierarchical modular structure is consistent across subjects.

The structure is not explained by simple correlation distributions.

Minimal Spanning Forest efficiently identifies and ranks brain modules.

Abstract

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.