Generative models of the human connectome

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.

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Cited by papers we hold 9

• Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review · 2022
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• Genetic influences on hub connectivity of the human connectome · 2021
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• Distance-dependent consensus thresholds for generating group-representative structural brain networks · 2019
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• Gradients of structure–function tethering across neocortex · 2019
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• The impact of epilepsy surgery on the structural connectome and its relation to outcome · 2018
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• The modular organization of human anatomical brain networks: Accounting for the cost of wiring · 2017
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• An open resource for transdiagnostic research in pediatric mental health and learning disorders · 2017
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• Optimally controlling the human connectome: the role of network topology · 2016
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• Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month · 2015
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