AUTHOR=Schmidt Dominik , English Gwendolyn , Gent Thomas C. , Yanik Mehmet Fatih , von der Behrens Wolfger 

TITLE=Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.971231

DOI=10.3389/fninf.2022.971231

ISSN=1662-5196

ABSTRACT=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.