Local Field Potential Microstate Analysis

Correct depth of anesthesia (DOA) assessment is a serious and widespread medical problem and an active scientific research topic. Anesthesia is known to alter the dynamics of neural networks within the brain that ultimately results in the impairment of pain perception. Nevertheless, linking the composition and dosage of various anesthetics, to a target level of anesthesia is not trivial. In this paper we explore the use of microstates as a viable tool to discriminate between anesthesia levels in intracranial recordings from mouse visual cortex. We show such symbolic analysis is able to capture DOA specific information in local field potentials (LFP). Microstates are characterized by the appearance of set of prototypical maps of activations over recording sites (electrodes) that define stereotypical patterns of activation in the recorded neural networks. Although microstates have been defined and characterized in electroencephalogram (EEG) data, our study shows for the first time that microstates can be effective when considering LFPs as well. We performed statistical analysis of average duration, time coverage, and occurrence of the microstates in order to differentiate between different DOA levels. By increasing the number of microstates, the analysis is more insightful and it is easier to discriminate between DOA levels.