Database Open Access

Wide-field calcium imaging sleep state database

Eric Landsness Xiaohui Zhang Wei Chen Hanyang Miao Michelle Tang Lindsey Brier Mark Anastasio Jin-Moo Lee Joseph Culver

Published: March 17, 2022. Version: 1.0.1

When using this resource, please cite: (show more options)
Landsness, E., Zhang, X., Chen, W., Miao, H., Tang, M., Brier, L., Anastasio, M., Lee, J., & Culver, J. (2022). Wide-field calcium imaging sleep state database (version 1.0.1). PhysioNet.

Additionally, please cite the original publication:

Zhang, X., Landsness, E. C., Chen, W., Miao, H., Tang, M., Brier, L. M., ... & Anastasio, M. A. (2022). Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning. Journal of neuroscience methods, 366, 109421.

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.


A collection of wide-field calcium imaging (WFCI) sleep and wake recordings collected from twelve transgenic mice expressing GCaMP6f in excitatory neurons. Each mouse underwent a three-hour undisturbed WFCI recording session where wake, REM (rapid eye movement) sleep and NREM (non-REM) sleep was recorded. Each WFCI recording is manually scored by sleep scoring experts in 10-second epochs as wake, NREM or REM by use of adjunct EEG/EMG. The dataset contains annotated WFCI recordings, brain mask and the Paxinos atlas used for defining the brain regions. The dataset was collected as part of a study evaluating a deep learning-based automated sleep state classification method.


Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows recording of regional neuronal depolarization in mice across the entire cortex on a sub-second temporal scale [1-4]. WFCI has been employed to study mouse brain physiology during quiet wakefulness [3], decision-making behavior [5], anesthesia [6] and under disease states [7]. Recently, it has also been employed to characterize the dynamics of neural activity during sleep [8].

The purpose of this dataset is to curate WFCI recordings during different sleep-wake states in different experimental conditions. Sleep state was confirmed by the use of adjunctive electroencephalographic (EEG)/electromyographic EMG signals characterized as wake, NREM or REM by expert sleep scorers.

This WFCI sleep staging dataset was originally collected as part of a study evaluating a deep learning-based automated sleep state classification method. Detailed information about the motivation, the study design, as well as description of methods regarding implementation of algorithms and analysis will be found in the associated paper that is currently under review for publication.

The hope is that as more WFCI sleep recordings across experimental conditions and recording equipment is collected, that this database will be a resource for researchers interested in understanding the neural correlates of sleep.


The dataset includes 3-hour WFCI recordings collected from transgenic mice expressing GCaMP6f in excitatory neurons. The study was approved by the Washington University School of Medicine Institutional Animal Care and Use Committee and performed in accordance with National Institutes of Health Guide for the Care and Use of Laboratory Animals. Mice were housed in 12-hour light/dark cycles with lights on at 6:00 AM and given ad lib access to food and water.

In order visualize neuronal depolarization through intact skull mice had Plexiglass head caps affixed with a translucent adhesive cement followed by EEG and EMG electrode implantation. Two-weeks after surgery, mice were placed in a black, felt pouch with their heads secured in place under four LEDs: 454 nm (blue, GCaMP6 excitation), 523 nm (green), 595 nm (yellow), and 640 nm (red) for hyperspectral oximetric imaging. WFCI data and EEG/EMG signals is recorded simultaneously. Images were acquired with an EMCCD camera. Frame rate was 16.81 Hz. All recordings occurred between 9:00 AM and 1:00 PM to maximize the chance of recording sleep.

Image preprocessing is conducted using custom MATLAB package [9], which includes image registration, signal detrending, smoothing and regression [6,8]. No additional digital filtering was applied. Recordings are then segmented into 10s epochs and manually scored by inspecting EEG/EMG. One of the three states (wake, NREM or REM) was assigned to each epoch. Broadband WFCI recordings of consecutive 10s epochs for each mouse are stored in each mat.file along with the corresponding annotated labels.

Data Description

Each of the twelve mice underwent a 3-hour WFCI recording. Twelve folders containing different number of recording segments are created for each individual mouse (Ms1, Ms2, ...). For each mouse, the order of recording files are identified by the sequence number fcXX (e.g. a complete ordered recording of a mouse is concatenated by fc1, fc2, …, fc12). Three different variables are available for each recording segment file:

  • data: Contains WFCI data of size [#pixels, #pixels, #frames, #epochs] = [128, 128, 168, n], n may vary because not all recordings are exactly 3-hours.
  • scoringindex_file: Contains 1D vector of corresponding scoring of size [1, #epochs] = [1, n], n is the same as the variable 'data.' The labels for each states are: 0=wake, 1=NREM, 2=Artifacts, 3=REM.
  • xform_mask: Contains binary mask (1=brain, 0= non-brain) defining the brain tissue within the field of view.

In addition, the Paxinos atlas [10] used in our study is provided as a separate file atlas.mat, which can be used to identify different brain regions.

Usage Notes

The WFCI sleep staging database may be of interest to sleep researchers interested in characterizing the neural correlates of sleep.

This may also be useful to those in developing automated sleep state classification methods of WFCI data with the human EEG/EMG adjunctive human scoring being used as a ground truth data to validate such methods.

All files of this dataset are store in mat.file, which can be read by MATLAB or SciPy directly. Moreover, the brain mask and the atlas file provide a convenience for users to locate the WFCI data in each brain regions within the field of view.


The authors declare no ethics concerns.

Release Notes

Version 1.0.0: Initial release.

Version 1.0.1: Citation to original publication added, author list updated.


We thank Dr. Lindsey M. Brier for her help on data collection and preprocessing. This work is supported in part by NIH/NINDS 1 K08 NS109292-01A1, AHA 20CDA35310607, AASMF 201-BS-19 for E.L., F30AG061932 to L.M.B..

Conflicts of Interest

The authors have no conflicts of interest to declare.


  1. Ma, Y., Shaik, M. A., Kim, S. H., Kozberg, M. G., Thibodeaux, D. N., Zhao, H. T., ... & Hillman, E. M. (2016). Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1705), 20150360.
  2. Kozberg, M. G., Ma, Y., Shaik, M. A., Kim, S. H., & Hillman, E. M. (2016). Rapid postnatal expansion of neural networks occurs in an environment of altered neurovascular and neurometabolic coupling. Journal of Neuroscience, 36(25), 6704-6717.
  3. Ma, Y., Shaik, M. A., Kozberg, M. G., Kim, S. H., Portes, J. P., Timerman, D., & Hillman, E. M. (2016). Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proceedings of the National Academy of Sciences, 113(52), E8463-E8471.
  4. Matsui, T., Murakami, T., & Ohki, K. (2016). Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity. Proceedings of the National Academy of Sciences, 113(23), 6556-6561.
  5. Allen, W. E., Kauvar, I. V., Chen, M. Z., Richman, E. B., Yang, S. J., Chan, K., ... & Deisseroth, K. (2017). Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron, 94(4), 891-907.
  6. Wright, P. W., Brier, L. M., Bauer, A. Q., Baxter, G. A., Kraft, A. W., Reisman, M. D., ... & Culver, J. P. (2017). Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice. PLOS One, 12(10), e0185759.
  7. Balbi, M., Vanni, M. P., Vega, M. J., Silasi, G., Sekino, Y., Boyd, J. D., ... & Murphy, T. H. (2019). Longitudinal monitoring of mesoscopic cortical activity in a mouse model of microinfarcts reveals dissociations with behavioral and motor function. Journal of Cerebral Blood Flow & Metabolism, 39(8), 1486-1500.
  8. Brier, L. M., Landsness, E. C., Snyder, A. Z., Wright, P. W., Baxter, G. A., Bauer, A. Q., ... & Culver, J. P. (2019). Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia. Neurophotonics, 6(3), 035002.
  9. Brier, L. M., & Culver, J. P. (2021). An open source statistical and data processing toolbox for wide-field optical imaging in mice. bioRxiv.
  10. Paxinos, G., & Franklin, K. B. (2019). Paxinos and Franklin's the mouse brain in stereotaxic coordinates. Academic press.


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LICENSE.txt (download) 14.5 KB 2022-03-17
README.txt (download) 626 B 2021-08-08
SHA256SUMS.txt (download) 11.2 KB 2022-03-22
atlas.mat (download) 1.7 KB 2021-08-08
mouse_info.csv (download) 547 B 2021-09-07