Time Course of Brain Network Reconfiguration Supporting Inhibitory Control

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for of brain networks, the relatively slow temporal course of fMRI limits the ability to The latter is crucial for developing a mechanistic understanding of how brain networks dynamically shift to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality and random-forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total n = 333, both genders) who performed a color-word Stroop task.Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. Granger causality analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of SFG over both dACC and IFG, dACC control ofr MFG, and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta, and posterior beta tracking the onset of behavioral response.Present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is up-regulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample (n = 96) and replication sample (n = 237) utilizing effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological pro