The individuality of single-frame functional brain connectivity

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Network Science

Background:

• Brain network fingerprinting and precision mapping reveal highly individualized brain networks.
• Growing interest in dynamic (second-to-second) brain network changes within scan sessions.
• Traditional static network analyses emphasize individual differences, while dynamic network studies often use group-level approaches.

Purpose of the Study:

• To explore the feasibility of functional connectivity fingerprinting at single-frame temporal resolution.
• To investigate the extent to which individual brain network dynamics can be identified from brief time windows.
• To assess the role of individual variability in dynamic brain network patterns.

Main Methods:

• Estimation of functional connectivity at individual brain volumes using phase coherence.
• Classification of participant identity based on single-volume connectivity data.
• Utilizing varying levels of atlas parcellation to assess identification accuracy.
• Comparing task identification within-subjects versus between-subjects.

Main Results:

• Participant identity can be classified from single brain volumes with sufficient data.
• Higher atlas parcellation levels improve the accuracy of participant identification.
• Tasks are identified more readily within individuals than across different individuals.
• Single-volume connectivity patterns are significantly influenced by participant identity.

Conclusions:

• Functional connectivity fingerprinting is feasible at the single-volume level, demonstrating high temporal resolution.
• Individual variability is a key factor driving observed single-volume connectivity patterns in dynamic brain networks.
• Task-related neural activity shows greater within-subject consistency at the single-volume level than between-subject consistency.