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

Journal: Journal of neuroscience methods
Published Date: Nov 22, 2021

Abstract

BACKGROUND: 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 wakefulness, 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.

Authors

  • Xiaohui Zhang
    Department of Orthopaedic Surgery, the Second Hospital &Clinical Medical School, Lanzhou University, Lanzhou, Gansu Province, China.
  • Eric C Landsness
    Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Hanyang Miao
    Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
  • Michelle Tang
    Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
  • Lindsey M Brier
    Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
  • Joseph P Culver
    Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA; Department of Electrical and Systems Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA; Department of Physics, Washington University School of Arts and Science, St. Louis, MO 63130, USA.
  • Jin-Moo Lee
    Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri; Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri. Electronic address: leejm@wustl.edu.
  • Mark A Anastasio
    Department of Biomedical Engineering, Washington University, St. Louis, MO 63110, USA.

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