Detection of functional brain network reconfiguration during task-driven cognitive states

Area of Science:

• Neuroimaging
• Network Science
• Computational Neuroscience

Background:

• Network science provides tools to analyze complex interactions in neuroimaging data.
• Understanding dynamic changes in functional connectivity at short timescales is crucial but not well understood.
• Varying time window sizes is a method to study functional connectivity dynamics across different timescales.

Purpose of the Study:

• To investigate the impact of time window size on functional connectivity dynamics.
• To identify how different time window sizes affect the observed network flexibility.
• To reveal core and periphery brain regions based on their dynamic network flexibility during cognitive tasks.

Main Methods:

• Functional connectivity was assessed using resting-state fMRI data from 82 participants.
• Regional BOLD time series were divided into variable-sized time windows.
• Multilayer community detection and network flexibility calculations were employed to analyze temporal dynamics.

Main Results:

• Large and small time windows showed narrow ranges of network flexibility.
• Medium time windows (75-100s) exhibited a broad range of network flexibility values.
• Core regions (visual, attention areas) showed low flexibility, while periphery regions (subcortical, temporal) showed high flexibility.

Conclusions:

• Time window length significantly influences observed network dynamics during task performance.
• Medium time windows are effective for distinguishing core and periphery brain regions.
• This study highlights organizational principles of brain functional connectivity beyond static approaches.