NeuroImage
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Error detection is essential for monitoring performance and preparing subsequent behavioral adjustments, and is associated with specific neural responses in the anterior cingulate cortex (ACC). To investigate whether different brain mechanisms subserve the processing of commission vs. accuracy errors, we recorded EEG in adult participants while they performed a novel speeded GO-NOGO aiming task ("the Shoot-NoShoot paradigm"). ⋯ Fast hits also elicited a pre-ERN but no ERN, suggesting that this pre-response monitoring component might be related to the detection of error likelihood. Although source analysis revealed similar generators in ACC for these different error-related negativities, the respective timing differed, suggesting that commission errors are detected rapidly based on internal motor representations, whereas the detection of accuracy errors in ACC relies on the additional and swift processing of external visual information.
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Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". ⋯ We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations.