Dataset Information

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

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.

New method

A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM.

Results

Sleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered.

Comparison with existing method

On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring.

Conclusions

The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.

SUBMITTER: Zhang X

PROVIDER: S-EPMC9006179 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Zhang Xiaohui X Landsness Eric C EC Chen Wei W Miao Hanyang H Tang Michelle M Brier Lindsey M LM Culver Joseph P JP Lee Jin-Moo JM Anastasio Mark A MA

Journal of neuroscience methods 20211122

<h4>Background</h4>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 ne ...[more]

PMID: 34822945

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Dataset Information

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

Background

New method

Results

Comparison with existing method

Conclusions

Publications

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

Similar Datasets

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