Generative models of the human connectome

TL;DR

This paper explores generative models combining geometric and topological rules to replicate the human connectome's structure, revealing how these factors evolve with age.

Contribution

It introduces a novel family of generative models that better match human connectome features by integrating geometric and homophilic wiring rules.

Findings

Models closely match many connectome topological features.

Model parameters change with age, indicating developmental rebalancing.

Synthetic networks replicate features not explicitly optimized in the models.

Abstract

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.