Dynamic Evolution of Prefrontal Neural Activity in Depression: A Bayesian Probability Model

Depression is a common emotional disorder in modern society that causes growing burdens globally. This mental disorder has been frequently confirmed to be closely related to abnormalities in the prefrontal cortex (PFC). However, it remains to be fully investigated whether the neural activity in the PFC has regular quasi-steady spatiotemporal structures, and whether dynamic patterns of these structures are associated with prefrontal dysfunction in depression. To further uncover such neural correlates, we extended the traditional electroencephalography (EEG) microstates to a novel localized level and developed a variational Bayesian probabilistic generative model to decode such localized microstate patterns from few-channel prefrontal EEG signals. We validated the method with a publicly available multichannel EEG dataset obtained from 165 healthy individuals and a three-channel prefrontal EEG dataset (43 depressed and 43 healthy). The approach was finally used to examine dynamic evolution of prefrontal neural activities in depression. Our results demonstrate that the localized prefrontal microstates exhibit high cross-dataset reproducibility and were characterized by finer spatiotemporal patterns independent from traditional whole-brain microstates. The results further revealed significant emotional task-specific abnormalities in localized prefrontal microstates between the depressed and the healthy individuals, including more frequent occurrences and shorter durations in high-power bilaterally asymmetric microstates, as well as less organized low-power symmetric microstates. Our extended concept of the localized microstates and associated analytical methods provides a novel theoretical framework for elucidating prefrontal neural dynamics and also lays a theoretical foundation for uncovering prefrontal functional abnormalities in depression and developing auxiliary diagnostic tools with prefrontal few-channel EEG data.