Hemodynamic cortical ripples through cyclicity analysis.

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Biology

Background:

• Understanding brain function requires detailed analysis of cortical network dynamics.
• Resting-state functional magnetic resonance imaging (fMRI) provides time series data of brain region activity, often aperiodic and lacking a base frequency.
• Cyclicity analysis is a novel technique adept at uncovering temporal order in complex time series data.

Purpose of the Study:

• To extend cyclicity analysis for characterizing dynamic interactions between distant brain regions.
• To apply this extended method to Human Connectome Project fMRI data.
• To investigate the temporal ordering and lead-lag relationships in brain activity.

Main Methods:

• Utilized cyclicity analysis, a robust technique for time series analysis.
• Applied the method to resting-state and task-based fMRI data from the Human Connectome Project.
• Analyzed lead-lag relationships and temporal ordering between brain regions.

Main Results:

• Detected cortical traveling waves of activity propagating along spatial axes in resting-state scans.
• Observed consistent lead-lag relationships between specific brain regions, mirroring cortical hierarchical organization.
• Identified task-modulated bursts of strong temporal ordering in task-based fMRI, aligning with stimuli.

Conclusions:

• Cortical traveling waves exhibit temporal ordering and hierarchical organization.
• These waves may play a role in emergent cognitive functions.
• The findings provide insights into the dynamic interactions within brain networks.