Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural...

Area of Science:

• Neuroimaging
• Connectomics
• Computational Neuroscience

Background:

• Joint analysis of structural connectivity (SC) and functional connectivity (FC) is crucial for understanding brain networks.
• Existing methods predominantly rely on predefined atlases, introducing potential bias and affecting analysis outcomes.
• The choice of atlas significantly influences the results of connectome integration studies.

Purpose of the Study:

• To introduce a novel atlas-free approach, Surface-Based Connectivity Integration (SBCI), for accurate SC and FC integration.
• To enable the study of SC-FC relationships within the intra-cortical gray matter without predefined atlases.
• To derive novel measures of SC-FC coupling (SFC) for enhanced connectomics analysis.

Main Methods:

• SBCI represents SC and FC continuously on the white surface, eliminating the need for atlases.
• Continuous SC is modeled as a smoothed probability density function for better integration with FC.
• Three novel sets of SC-FC coupling (SFC) measures are derived using the SBCI framework.

Main Results:

• SBCI produces high-quality SFC measures, demonstrating superior reproducibility compared to atlas-based methods.
• The atlas-free framework shows greater predictive power in distinguishing biological sex using Human Connectome Project data.
• Novel SFC measures derived from SBCI offer enhanced insights into brain connectivity.

Conclusions:

• SBCI provides a robust and reproducible atlas-free framework for connectome integration.
• This approach advances the study of SC-FC relationships, particularly in distinguishing biological sex.
• SBCI opens new avenues for research in connectomics and neuroimaging analysis.