Characterizing Transition State in Mouse Vigilance with EEG-EMG Hypnodensity
Study ObjectivesVigilance-state transitions are continuous biological processes, yet conventional rodent sleep scoring relies on discrete epochs that obscure intermediate states. As no standardized framework exists for characterizing these intermediate states in rodents, this study aimed to characterize the temporal dynamics of transitions in mice and validate a machine-learning approach for objective detection. MethodsChronic EEG and EMG recordings were obtained from male C57BL/6N mice. We extracted 56-second windows containing stable transitions between Wakefulness (WAKE), Non-Rapid Eye Movement Sleep (NREMS), and Rapid Eye Movement Sleep (REMS). Eight trained experts manually annotated the onset and duration of transitions to establish ground truth and assess inter-rater reliability. Using quantitative EEG/EMG features (e.g., spectral power, complexity, EMG variance) derived from stable states, Support Vector Machine (SVM) classifiers were trained to predict transition midpoints in independent test animals. ResultsInter-rater agreement among experts was moderate to low, particularly for WAKE to NREMS and NREMS to REMS transitions, reflecting inherent ambiguity in manual scoring. Temporal analysis revealed distinct dynamics across transition types; NREMS to REMS transitions were significantly longer than all others, while REMS to NREMS transitions were the most abrupt. Despite the variability in human scoring, SVM models trained only on stable-state features successfully predicted expert-defined transition midpoints. ConclusionsOur approach not only characterized the recognizable dynamics across transition types in mice, but also provides a reproducible framework for quantifying sleep-wake transitions, which is crucial for studying arousal stability and related impairments in disease. Statement of SignificanceTraditional sleep scoring enforces discrete boundaries between vigilance states, overlooking transitional dynamics that may be critical for understanding arousal regulation. We developed a novel hypnodensity-based framework to systematically identify and characterize intermediate vigilance states in mice using EEG-EMG recordings. By combining expert annotations with machine learning, we revealed that transitions between sleep and wake involve continuous processes with mixed state features, rather than instantaneous switches. This approach provides the first standardized method for quantifying transitional vigilance states in rodents, enabling deeper investigation of arousal instability in neurological disorders. Our framework advances automated sleep analysis beyond classical three-state classification