Cluster-based analysis for characterizing dynamic functional connectivity.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Resting-state functional connectivity (FC) exhibits non-stationarity across brain regions over time.
• Dynamic FC analysis often assumes constant spatial network extents, limiting understanding of brain flexibility.
• Characterizing temporal variability in brain networks is crucial for a comprehensive understanding of brain function.

Purpose of the Study:

• To propose a simple and efficient framework for clustering FC variability in the resting rat brain.
• To identify brain regions with consistent connectivity versus those with dynamic FC patterns.
• To address limitations in current dynamic FC analysis by not assuming constant network spatial extents.

Main Methods:

• A novel clustering framework was developed to analyze FC variability in resting-state functional magnetic resonance imaging (rsfMRI) data from rats.
• Voxel size was increased and spatial resolution reduced to analyze whole-brain FC variability.
• Sliding window correlation was employed to compute dynamic FC patterns with a sensorimotor cortex seed voxel.
• K-means clustering with binary transformed feature vectors was used to group spatially similar FC patterns.

Main Results:

• The clustering framework successfully identified brain regions with stable and variable functional connectivity.
• It revealed specific areas consistently connected to the seed voxel and those exhibiting dynamic FC.
• The method effectively detected the extent and nature of FC variability across the whole resting brain.
• Analysis demonstrated that adjacent voxels could be averaged without significant information loss, supporting the methodology.

Conclusions:

• The proposed framework offers an efficient method for analyzing non-stationary FC in the resting brain.
• It provides a more nuanced understanding of brain network dynamics by distinguishing stable from variable connectivity.
• This approach enhances the analysis of dynamic functional connectivity, moving beyond assumptions of static network structures.