Modelling mouse auditory response dynamics along a continuum of consciousness using a deep recurrent neural network.

Abstract

Objective.Understanding neurophysiological changes that accompany transitions between anaesthetized and conscious states is a key objective of anesthesiology and consciousness science. This study aimed to characterize the dynamics of auditory-evoked potential morphology in mice along a continuum of consciousness.Approach.Epidural field potentials were recorded from above the primary auditory cortices of two groups of laboratory mice: urethane-anaesthetized (A,n= 14) and conscious (C,n= 17). Both groups received auditory stimulation in the form of a repeated pure-tone stimulus, before and after receiving 10 mg kg-1i.p. ketamine (AK and CK). Evoked responses were then ordered by ascending sample entropy into AK, A, CK, and C, considered to reflect physiological correlates of awareness. These data were used to train a recurrent neural network (RNN) with an input parameter encoding state. Model outputs were compared with grand-average event-related potential (ERP) waveforms. Subsequently, the state parameter was varied to simulate changes in the ERP that occur during transitions between states, and relationships with dominant peak amplitudes were quantified.Main results.The RNN synthesized output waveforms that were in close agreement with grand-average ERPs for each group (r2> 0.9,p< 0.0001). Varying the input state parameter generated model outputs reflecting changes in ERP morphology predicted to occur between states. Positive peak amplitudes within 25-50 ms, and negative peak amplitudes within 50-75 ms post-stimulus-onset, were found to display a sigmoidal characteristic during the transition from anaesthetized to conscious states. In contrast, negative peak amplitudes within 0-25 ms displayed greater linearity.Significance.This study demonstrates a method for modelling changes in ERP morphology that accompany transitions between states of consciousness using an RNN. In future studies, this approach may be applied to human data to support the clinical use of ERPs to predict transition to consciousness.