Electroencephalography (EEG) measures the brain’s electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects ( n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts’ brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).

Understanding the neural processes that govern the human gut-brain connection has been challenging due to the inaccessibility of the body′s interior. In this study, we aimed to identify neural responses to gastrointestinal sensation (i.e., the neural basis of ′gut feelings′) in healthy humans using a minimally invasive mechanosensory probe. Combining electroencephalography and electrogastrography with signal detection theory measures, we quantified brain, stomach, and perceptual (button-press) responses following the ingestion of a vibrating capsule. The relationship between vibration strength and perceptual sensitivity was evaluated using two stimulation conditions (normal and enhanced). Most individuals successfully perceived capsule stimulation in both conditions, as evidenced by above chance accuracy scores. Perceptual accuracy improved significantly during the enhanced relative to normal stimulation, which was associated with faster reaction time and reduced reaction time variability. Stomach stimulation induced responses in a cluster of parieto-occipital leads near the midline via a late positive potential emerging 300-600 milliseconds after stimulation onset. Moreover, these ′gastric evoked potentials′ showed dose-dependent increases in amplitude and were significantly correlated with perceptual accuracy. Our findings are consistent with recent neurogastric and optogenetic studies demonstrating a role for posteromedial cortices in gastrointestinal interoception and body dissociation and highlight a unique form of enterically-focused sensory monitoring within the human brain. Overall, these results show that this minimally invasive approach could serve as a useful tool for understanding gut-brain interactions in healthy and clinical populations.