The specificity and robustness of long-distance connections in weighted, interareal connectomes

TL;DR

This study investigates the role of long-distance connections in brain networks, finding they mainly diversify regional inputs and outputs, support robustness, and are essential for complex neural dynamics, rather than simply reducing topological distance.

Contribution

The paper challenges the view that long-distance connections primarily reduce topological distance, showing they instead promote diversity, robustness, and complex dynamics in brain networks.

Findings

Long-distance connections minimally reduce average topological distance.

Long-distance neighbors differ significantly in connectivity profiles.

Removing long-distance connections decreases functional diversity.

Abstract

Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of these long-distance connections is not known, the leading hypothesis is that they act to reduce the topological distance between brain areas and facilitate efficient interareal communication. However, this hypothesis implies a non-specificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas' inputs and outputs, thereby promoting complex dynamics. Through analysis of five interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas' long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between regional inputs and outputs. Next, we show that -- in isolation -- areas' long-distance connectivity profiles exhibit non-random levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson-Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections, a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.