Robust discrimination between EEG responses to categories of environmental sounds in early coma

Abstract

Humans can recognize categories of environmental sounds, including vocalizations produced by humans and animals and the sounds of man-made objects. Most neuroimaging investigations of environmental sound discrimination have studied subjects while consciously perceiving and often explicitly recognizing the stimuli. Consequently, it remains unclear to what extent auditory object processing occurs independently of task demands and consciousness. Studies in animal models have shown that environmental sound discrimination at a neural level persists even in anesthetized preparations, whereas data from anesthetized humans has thus far provided null results. Here, we studied comatose patients as a model of environmental sound discrimination capacities during unconsciousness. We included 19 comatose patients treated with therapeutic hypothermia (TH) during the first 2 days of coma, while recording nineteen-channel electroencephalography (EEG). At the level of each individual patient, we applied a decoding algorithm to quantify the differential EEG responses to human vs. animal vocalizations as well as to sounds of living vocalizations vs. man-made objects. Discrimination between vocalization types was accurate in 11 patients and discrimination between sounds from living and man-made sources in 10 patients. At the group level, the results were significant only for the comparison between vocalization types. These results lay the groundwork for disentangling truly preferential activations in response to auditory categories, and the contribution of awareness to auditory category discrimination.

Prediction of awakening from hypothermic postanoxic coma based on auditory discrimination

“…It has been previously used for decoding responses in healthy subjects 33,34 and comatose patients. 21,35 This algorithm consists of modeling the distribution of single-trial EEG responses across all electrodes using a mixture of Gaussian models (GMM) in an n-dimensional space where n represents the number of electrodes. 32,33 The models are computed through an expectation-maximization algorithm 36 for each patient and recording (TH/NT) separately, using only one part of the available data (training data set, consisting of 90% of the artifact-free single trials).…”

Quantifying the time for accurate EEG decoding of single value-based decisions

“…The employed approach consists of modeling single-trial voltage topographies (i.e. the configuration of electric activity across all electrodes at any given instant) based on Mixture of Gaussians models (GMM) (see similar applications of the GMM for single-trial EEG analysis in (Bernasconi et al, 2011;Cossy et al, 2014;De Lucia et al, 2012). The probability distribution of a GMM model with Q Gaussians in total was computed in an N-dimensional space (N = total number of electrodes) as:…”

Experience-based Auditory Predictions Modulate Brain Activity to Silence as Do Real Sounds

“…This approach has been successfully used in the past to decode EEG responses to auditory stimuli (Cossy, Tzovara, Simonin, Rossetti, & De Lucia, 2014;Tzovara et al, 2013;De Lucia, Tzovara, Bernasconi, Spierer, & Murray, 2012;Bernasconi et al, 2011). Here, we applied this algorithm in the groups of passive and active participants separately.…”