Pupil Dynamics-derived Sleep Stage Classification of a Head-fixed Mouse Using a Recurrent Neural Network
Goh Kobayashi1, Kenji F Tanaka1, Norio Takata1
1Division of Brain Sciences, Institute for Advanced Medical Research, Keio University School of Medicine, Tokyo, Japan.
The Keio Journal of Medicine
|February 5, 2023
Summary
This study introduces a new method for classifying mouse sleep states using pupil dynamics, avoiding invasive EEG/EMG recordings. The long short-term memory (LSTM) model accurately identifies NREM, REM, and WAKE states, improving sleep research efficiency.
Area of Science:
- Neuroscience
- Computational Biology
- Sleep Science
Background:
- Traditional sleep state classification relies on electroencephalography (EEG) and electromyography (EMG) with manual expert correction.
- Current methods face limitations including time consumption, interference with imaging, surgical risks, and incompatibility with certain experimental setups.
Purpose of the Study:
- To develop a non-invasive pupil dynamics-based method for classifying vigilance states (NREM, REM, WAKE) in head-fixed mice.
- To overcome the limitations of conventional EEG/EMG-based sleep scoring.
Main Methods:
- Utilized a long short-term memory (LSTM) model, a type of recurrent neural network, for multi-class sleep state labeling.
- Integrated EEG and EMG recordings with left eye pupillometry tracked by DeepLabCut, a markerless tracking toolbox.
- Employed pupil diameter, location, velocity, and eyelid opening as features for the LSTM model at a 10 Hz sampling rate.
Main Results:
- The LSTM model achieved higher classification performance (macro F1 score: 0.77, accuracy: 86%) compared to a feed-forward neural network.
- Pupil dynamics revealed potential subdivisions within established EEG/EMG-defined vigilance states.
- Demonstrated the feasibility of pupil dynamics for accurate sleep stage scoring.
Conclusions:
- Pupil dynamics offer a viable, non-invasive alternative for sleep stage scoring in head-fixed mice.
- This approach enhances efficiency and reduces risks associated with traditional methods.
- The findings suggest pupil dynamics could refine our understanding of vigilance states.
