# Generative models of the human connectome

arXiv: https://arxiv.org/abs/1506.06795 · HTML: https://mlsanity.com/paper/1506.06795/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2015.

## Built on by (most cited first, 5 of 5)

- [Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis](https://mlsanity.com/paper/2506.11062/) (2025)
- [Improving the Level of Autism Discrimination through GraphRNN Link Prediction](https://mlsanity.com/paper/2202.09538/) (2022)
- [Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks](https://mlsanity.com/paper/2410.11064/) (2024)
- [Neuro-inspired Ensemble-to-Ensemble Communication Primitives for Sparse and Efficient ANNs](https://mlsanity.com/paper/2508.14140/) (2025)
- [Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing](https://mlsanity.com/paper/2609.06093/) (2026)

## Abstract (arXiv)

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.