Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data

Conclusion: Our method increases the predictive power of the constructed FC patterns when establishing brain-behavior relationships, and gains meaningful insights into relevant biological mechanisms. Significance: The proposed CGGL offers a more powerful, and reliable method to estimate FC patterns, which can be used as fingerprints in many brain network studies.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research