Network enrichment significance testing in brain-phenotype association studies

Area of Science:

• Neuroimaging
• Genetics
• Biostatistics

Background:

• Functional brain networks are crucial for interpreting brain-phenotype associations.
• Current methods for assessing network enrichment of these associations lack rigor and make unrealistic assumptions, leading to high false positive rates.
• Existing genetic enrichment methods offer a foundation for improved neuroimaging analysis.

Purpose of the Study:

• To introduce Network Enrichment Significance Testing (NEST), a novel framework for analyzing brain-phenotype associations.
• To provide a flexible and rigorous method for testing the specificity of associations to functional networks or other brain regions.
• To address limitations in existing neuroimaging analysis techniques.

Main Methods:

• Developed Network Enrichment Significance Testing (NEST), inspired by genetic enrichment methods.
• Designed NEST as a flexible framework to test the specificity of brain-phenotype associations.
• Applied NEST to analyze structural and functional brain imaging data.

Main Results:

• NEST offers a rigorous approach to assess brain-phenotype associations within functional networks.
• The framework allows for flexible testing of regional specificity.
• Demonstrated application of NEST in a large-scale neurodevelopmental cohort study.

Conclusions:

• NEST provides a statistically sound and flexible method for network-based analysis of brain-phenotype associations.
• This approach enhances the reliability of findings in neuroimaging research.
• NEST advances the field by offering a robust tool for understanding brain-phenotype relationships in neurodevelopment.