Revisiting Alpha Resting State Dynamics Underlying Hallucinatory Vulnerability: Insights from Hidden Semi-Markov Modeling

Abstract

Abstract Resting state (RS) brain activity is inherently non-stationary. Hidden Semi-Markov Models (HsMM) can characterize the continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics. Recent explorations suggest that EEG brain state dynamics in the alpha frequency link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aims to replicate these findings to elucidate robust neural correlates of hallucinatory vulnerability. Specifically, we aimed to investigate the reproducibility of HsMM states across different data sets and within-data set variants as well as the replicability of the association between alpha brain state dynamics and HP. We found that most brain states are reproducible in different data sets, confirming that the HsMM characterized robust and generalizable EEG RS dynamics. Brain state topographies and temporal dynamics of different within-data set variants showed substantial similarities and were robust against reduced data length and number of electrodes. However, the association with HP was not directly reproducible across data sets. These results indicate that the sensitivity of brain state dynamics to capture individual variability in HP may depend on the data recording characteristics and individual variability in RS cognition, such as mind wandering. We suggest that the order in which eyes-open and eyes-closed RS data are acquired directly influences an individual’s attentional state and generation of spontaneous thoughts, and thereby might mediate the link to hallucinatory vulnerability.