bioRxivNeurosciencePreprint
Pre-anaesthetic anxiety phenotype stratifies cortical excitability, learned navigation, and post-anaesthetic sleep in sevoflurane-exposed mice
Alp AltunkayaiD, Monika Vadkertiová, Alina Kreitmaier, Catharina Hedenig, Selina WipperiD, M.V. Schmidt, Matthias KreuzeriD, Gerhard RammesiD, Gerhard Schneider, Thomas FenzliD
Abstract
Trait anxiety is a common pre-surgical phenotype and has been linked to adverse postoperative cognitive outcome, but whether it modulates the response to general anaesthesia has not been tested in a controlled design that isolates the anaesthetic from surgical injury. We classified 42 male C57BL/6N mice as low-anxiety (LA, n = 24) or high-anxiety (HA, n = 18) by unsupervised k-medoids clustering on cued fear-retrieval freezing, and exposed all animals to sevoflurane under live-EEG titration without surgery. At adequate anaesthetic depth, HA mice carried a flatter aperiodic (1/f) cortical slope than LA mice (AUC = 0.85, [0.70, 0.95]). Conventional water-cross-maze scoring returned null at post-anaesthetic re-test, but unsupervised decomposition of the same trial videos localised the effect to loss of a specific learned transition in HA mice (AUC = 0.72, [0.56, 0.86]). The dark-phase REMS response reversed direction, with HA mice losing REMS against their own baseline while LA mice gained it (AUC = 0.82, [0.65, 0.94]). The three effects are directionally consistent with modulation of cortical excitation-to-inhibition balance under sevoflurane. The aperiodic exponent and emergence-phase EEG trajectory, both recoverable from routine frontal EEG, are candidate pre-anaesthetic biomarkers.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: spatial domain detection
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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