Time Course of Brain Network Reconfiguration Supporting Inhibitory Control

Area of Science:

• Cognitive Neuroscience
• Neuroimaging
• Brain Network Dynamics

Background:

• Hemodynamic neuroimaging (fMRI) has identified brain structures involved in inhibitory control.
• However, fMRI's temporal resolution limits understanding of network operation dynamics.
• Characterizing dynamic network shifts is crucial for a mechanistic understanding of inhibitory control.

Purpose of the Study:

• To investigate the dynamic operation of brain networks supporting inhibitory control using EEG.
• To characterize the temporal dynamics and information flow within these networks.
• To identify key brain regions and frequency bands involved in implementing inhibitory control.

Main Methods:

• Applied spectrally resolved Granger causality (GC) and random forest machine learning to human EEG data.
• Utilized a color-word Stroop task in two large adult samples (N=333).
• Performed time-frequency analysis and EEG source analysis.

Main Results:

• Theta and alpha/beta power changes correlated with inhibitory control recruitment and slower responses.
• Granger causality revealed asymmetric information flow: SFG influenced dACC and IFG; dACC controlled MFG; frontal-parietal exchanges occurred.
• Predictive analytics identified SFG theta and posterior beta as key predictors of inhibitory control demands and response onset.

Conclusions:

• Inhibitory control involves dynamic routing processes, with upregulated target responses via theta-mediated connectivity in PFC nodes.
• Beta-mediated effective connectivity is associated with motor preparation during inhibitory control.
• This study provides mechanistic insights into inhibitory control, advancing models through detailed temporal dynamics analysis.