Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth
OPEN ACCESS
Author / Producer
Date
2022
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
16
Pages / Article No.
971231
Publisher
Frontiers Media
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
depth of anesthesia; gradient boosting; cortico-cortical coherence; mouse; somatosensory cortex (S1)
Organisational unit
09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
Notes
Funding
172962 - Deviant Signals in the Thalamocortical Loop: Circuitry and Perception (SNF)