Work

Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth

Year: 2022

Type: article

Abstract: The goal of this study was to identify features in mouse electrocorticogram recordings that indicate the depth of anesthesia as approximated by the administered anesthetic dosage. Anesthetic depth in laboratory animals must be precisely monitored and controlled. However, for the most common lab species (mice) few indicators useful for monitoring anesthetic 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 anesthesia and explored their usefulness in monitoring anesthetic depth through machine learning techniques. Using a gradient boosting regressor framework we identified interhemispheric somatosensory coherence as the most informative and reliable electrocorticogram feature for determining anesthetic depth, yielding good generalization and performance over many subjects. Knowing that interhemispheric somatosensory coherence indicates the effectively administered isoflurane concentration is an important step for establishing better anesthetic monitoring protocols and closed-loop systems for animal surgeries.

Source: Frontiers in Neuroinformatics

DOI: 10.3389/fninf.2022.971231

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

Institutions: University of Zurich, ETH Zurich, Institute of Neuroinformatics

Language: English

Topic: Anesthesia and Sedative Agents

Subfield: Anesthesiology and Pain Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,691