Topological gene expression networks recapitulate brain anatomy and function.

Area of Science:

• Neuroscience
• Computational Biology
• Genetics

Background:

• Understanding gene expression's role in human behavior is a key neuroscience challenge.
• Gene co-expression networks offer insights into genetic regulation of brain function.

Purpose of the Study:

• To present and validate a novel pipeline for analyzing gene co-expression data using topological simplification.
• To explore the relationship between genetic pathways, brain anatomy, and function.

Main Methods:

• Utilized Mapper, a topological data analysis tool, to construct gene co-expression networks.
• Validated the pipeline using the Allen Human Brain Atlas and resting-state fMRI data.
• Analyzed a dopamine-related gene set to identify network structures.

Main Results:

• The Mapper pipeline successfully reproduced known correlations between gene co-expression and brain activity.
• Co-expression networks of dopamine-related genes mirrored the known dopaminergic pathway anatomy.
• The method demonstrated the utility of network-based descriptions in neuroscience.

Conclusions:

• Network-based analysis of gene co-expression provides a powerful approach to studying brain function.
• This pipeline can help elucidate genetic contributions to brain function and disease.
• The findings support the integration of topological data analysis in neurogenetics research.