A Transcriptome Community-and-Module Approach of the Human Mesoconnectome.

Area of Science:

• Computational neuroscience
• Systems biology
• Genomics

Background:

• Graph analysis offers insights into brain mesostructures by examining transcriptome compartments like communities and modules.
• Understanding the relationship between gene regulatory networks (GRNs) and brain-wide connectomes is crucial for deciphering brain function.

Purpose of the Study:

• To develop a bottom-up model connecting gene regulatory networks to brain-wise connectomes.
• To identify functional brain cell communities and modules within the brain's hierarchical organization.
• To validate the model by comparing identified brain modules with known anatomical and functional brain circuits.

Main Methods:

• Utilized the Allen Brain Atlas transcriptome database to estimate gene communities across all brain regions.
• Applied a communities method to identify functional mesostructures within the network hierarchy.
• Constructed brain-wise region modules representing the connectome based on identified gene communities.

Main Results:

• Identified specific brain cell function communities, including neuroplasticity, axonogenesis, and dendritogenesis.
• Developed brain-wise region modules that accurately represent the brain's connectome.
• Findings align with established brain circuits like the default mode network and default visual network.

Conclusions:

• The modular composition of gene regulatory networks underlies brain dynamics for both low- and higher-order functions.
• This GRN-to-connectome workflow provides a novel framework for understanding brain organization and function.
• The study supports the hypothesis that complex brain functions emerge from the modular organization of gene regulatory networks.