Generative models of the human connectome.

Area of Science:

• Neuroscience
• Computational Biology
• Network Science

Background:

• The human connectome, a map of brain connections, is crucial for cognitive function.
• The rules governing its organization (topology) are not fully understood.
• Previous models based on distance alone were insufficient.

Purpose of the Study:

• To explore generative models of the human connectome using combined geometric and topological rules.
• To identify the key factors shaping brain network topology.
• To investigate how these factors change across the human lifespan.

Main Methods:

• Systematic exploration of generative network models.
• Combining geometric constraints with various topological factors, including homophilic attachment.
• Validation against empirical human connectome data and analysis of a lifespan dataset.

Main Results:

• A model combining geometric constraints and homophilic attachment accurately reproduced key human connectome features.
• This model captured topological characteristics not explicitly included in its optimization.
• Model parameters showed progressive changes across the lifespan.

Conclusions:

• Geometric and homophilic attachment rules are critical for generating realistic human connectome topology.
• Brain wiring undergoes dynamic rebalancing of generative factors throughout life.
• This work provides insights into the developmental and aging processes of brain networks.