Electroencephalography Signatures for Hepatic Encephalopathy in Cirrhosis Patients Treated with Proton Pump Inhibitors: An Exploratory Pilot Study

A neural mass model for disturbance of alpha rhythm in the minimal hepatic encephalopathy

One of the early markers of minimal hepatic encephalopathy (MHE) is the disruption of alpha rhythm observed in electroencephalogram (EEG) signals. However, the underlying mechanisms responsible for this occurrence remain poorly understood. To address this gap, we develop a novel biophysical model MHE-AWD-NCM, encompassing the communication dynamics between a cortical neuron population (CNP) and an astrocyte population (AP), aimed at investigating the relationship between alpha wave disturbance (AWD) and mechanistical principles, specifically concerning astrocyte-neuronal communication in the context of MHE. In addition, we introduce the concepts of peak power density and peak frequency within the alpha band as quantitative measures of AWD. Our model faithfully reproduces the characteristic EEG phenomenology during MHE and shows how impairments of communication between CNP and AP could promote AWD. The results suggest that the disruptions in feedback neurotransmission from AP to CNP, along with the inhibition of GABA uptake by AP from the extracellular space, contribute to the observed AWD. Moreover, we found that the variation of external excitatory stimuli on CNP may play a key role in AWD in MHE. Finally, the sensitivity analysis is also performed to assess the relative significance of above factors in influencing AWD. Our findings align with the physiological observations and provide a more comprehensive understanding of the complex interplay of astrocyte-neuronal communication that underlies the AWD observed in MHE, which potentially may help to explore the targeted therapeutic interventions for the early stage of hepatic encephalopathy.

PainVision-based evaluation of brain potentials: a novel approach for quantitative pain assessment

Introduction: The complex and multidimensional nature of pain poses a major challenge in clinical pain assessments. In this study, we aimed to evaluate a novel approach combining quantitative sensory testing (QST) with event-related potential measurements for assessment of experimental pain in healthy individuals. Methods: QST was performed with a commercial device (PainVision, PS-2100), and numeric rating scale (NRS) scores after exposure to different sensory stimuli were reported by the participants. Resting-state electroencephalography (EEG) was simultaneously performed to capture the cortical responses to peripheral stimulation. Results: Pain scores increased with the intensity of stimuli, with mean NRS scores of 2.7 ± 1.0 after mild stimuli and 5.6 ± 1.0 after moderate stimuli. A reproducible, significant P2-N2 complex was evoked by both mild and moderately painful stimuli, but not by non-painful stimuli. The latency of pain-related potentials was not significantly different between stimuli. The amplitudes of both P2 and N2 components significantly increased when intense nociception was applied, and the increments mainly originated from theta oscillations. Conclusion: The combination of QST with EEG was feasible for subjective and objective pain assessment. Distinct patterns of brain potentials were associated with the phenotype of the peripheral stimuli (e.g., noxious versus. innoxious, high versus. low pain intensity).