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Automated sleep stage classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning

Authors: Zhang X, Landsness EC, Chen W, Miao H, Tang M, Brier LM, Culver JC, Lee JM, Anastasio MA
University of Illinois at Urbana-Champaign, Urbana, IL - 61801, USA
Washington University School of Medicine
Washington University School of Engineering

Contact: xiaohui8@illinois.edu, landsness@wustl.edu, maa@illinois.edu

Abstract: Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wake, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed. A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to a multiplex visibility graph (MVG). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wake, NREM and REM.

Schematic

System Requirements

The conda environment including all necessaray packages can be created using file environment.yml:

conda env create --file environment.yml

Dataset

The WFCI data in this paper is available on PhysioNet:

Construction of multiplex visibility graph

The directory MVG contains the following sub-directories:

  • atlas.mat: variables for defining Paxinos atlas
  • define_rois.m: function used for defining which parcels will be used to construct MVG
  • parcel2trace.m: function to compute the avarage time series for each parcel
  • extrac_MVG.m: the main script to construct MVG

The construction of MVG is based on MATLAB, simply modify the data path highlighted in comment and run extract_MVG.m.

Classifying sleep on MVG with deep learning

The directory network training contains the top level scripts:

  • dataloader_MVG.py: Script for loading the MVG representations from WFCI data epochs
  • model_cnn2d.py: Script for the compact 2D CNN to classify sleep
  • utils.py: script to compute evaluation metrics
  • config.txt: example txt. file for defining list of subjects used in training and validation
  • train.sh: Scripts for running the network training.
  • checkpoints: a pretrained model in our paper is included.

To train the network, make sure to

  • Modify the data path highlighted in comment in dataloader_MVG.py.
  • Make you own config.txt listing the training/validation mouse name.
  • Define parameters in bash script train.sh and run chmod u+x train.sh to make the script executable.
  • Simply run ./train.sh to start your training

Citations

1. Landsness, E., & Zhang, X. (2021). Wide-field calcium imaging sleep state database (version 1.0.0). PhysioNet. https://doi.org/10.13026/jzqa-j937.
2. Zhang, X., Landsness, E. C., Chen, W., Miao, H., Tang, M., Brier, L. M., ... & Anastasio, M. A. (2021). Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning. Journal of Neuroscience Methods, 109421.

References

1. Iacobello, G., Ridolfi, L., & Scarsoglio, S. (2021). Large-to-small scale frequency modulation analysis in wall-bounded turbulence via visibility networks. Journal of Fluid Mechanics, 918.
Chicago	
2. Iacobello, G., Marro, M., Ridolfi, L., Salizzoni, P., & Scarsoglio, S. (2019). Experimental investigation of vertical turbulent transport of a passive scalar in a boundary layer: Statistics and visibility graph analysis. Physical Review Fluids, 4(10), 104501.
3. Paxinos, G., & Franklin, K. B. (2019). Paxinos and Franklin's the mouse brain in stereotaxic coordinates. Academic press.
Chicago