Group independent component analysis reveals consistent resting-state networks across multiple sessions

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Systems Neuroscience

Background:

• Resting-state functional magnetic resonance imaging (fMRI) is crucial for understanding brain function.
• Assessing the reliability of brain networks over time is essential for clinical applications.
• Group independent component analysis (gICA) is a powerful tool for identifying brain networks.

Purpose of the Study:

• To evaluate the across-session consistency of brain networks identified by gICA.
• To determine if physiologically relevant brain networks are stable over a 16-day period.
• To assess the utility of resting-state fMRI networks for longitudinal studies.

Main Methods:

• Resting-state fMRI data from 14 healthy subjects were analyzed across 5 sessions over 16 days.
• Data underwent multi-step Principal Component Analysis (PCA) for reduction and aggregation.
• Group independent component analysis (gICA) was applied, with consistency assessed via back-reconstruction methods.

Main Results:

• gICA identified 55 spatially independent brain maps, with artifactual maps removed.
• Biologically relevant networks, including sensory, motor, and default-mode networks, were identified.
• All analysis methods demonstrated remarkable across-session consistency for the identified components.

Conclusions:

• Resting-state brain networks identified by gICA are highly consistent across multiple sessions over several weeks.
• Physiologically relevant networks exhibit the greatest consistency, supporting their reliability.
• These consistent networks hold promise for monitoring longitudinal treatment-related changes in future studies.