Mapping individual differences across brain network structure to function and behavior with connectome embedding.

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• The brain connectome, a map of anatomical connections, is typically a matrix that misses higher-order network relationships.
• Connectome embedding (CE) creates vectorized representations of brain nodes, capturing their global network context.
• Previous CE applications focused on group-averaged data, limiting exploration of individual brain variations.

Purpose of the Study:

• To extend connectome embedding (CE) for analyzing individual differences in brain networks.
• To develop a novel embedding alignment approach for individual connectome analysis.
• To investigate the relationship between structural and functional connectivity across the lifespan.

Main Methods:

• Applied a novel embedding alignment approach to connectome embedding (CE) in two lifespan datasets (NKI, Cam-CAN).
• Utilized diffusion-weighted imaging and resting-state fMRI data to model functional connectivity.
• Incorporated demographic and behavioral measures for comprehensive analysis.

Main Results:

• CE modeling of functional connectivity significantly improved structure-function mapping at both group and individual levels.
• Age-related differences in structure-function mapping were preserved and enhanced by CE.
• CE accurately predicted age and intelligence out-of-sample, outperforming traditional connectivity measures.

Conclusions:

• Connectome embedding (CE) effectively captures individual differences in brain networks by integrating anatomical and functional information.
• The novel alignment approach enables mapping individual variations in the connectome through structure-function-behavior relationships.
• CE offers a powerful tool for understanding individual variability in brain organization and its link to cognitive function.