Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth - Schmidt, Dominik; English, Gwendolyn; Gent, Thomas C.; Yanik, Mehmet Fatih; von der Behrens, Wolfger (Frontiers in Neuroinformatics, 2022)
The goal of this study was to identify features in mouse electrocorticogram recordings that indicate the depth of anaesthesia as approximated by the administered anesthetic dosage. Anaesthetic depth in laboratory animals must be precisely monitored and controlled. However, for the most common lab species (mice) few indicators useful for monitoring anaesthetic depth have been established. We used electrocorticogram recordings in mice, coupled with peripheral stimulation, in order to identify features of brain activity modulated by isoflurane anaesthesia and explored their usefulness in monitoring anaesthetic depth through machine learning techniques. Using a gradient boosting regressor framework we identified interhemispheric coherence as the most informative and reliable electrocorticogram feature for determining anaesthetic depth, yielding good generalization and performance over many subjects. Knowing that interhemispheric coherence indicates the effectively administered isoflurane concentration is an important step for establishing better anaesthetic monitoring protocols and closed-loop systems for animal surgeries.