Dataset of mouse EEG and behaviors under threat-and-escape paradigm (solitary threat condition)

Original publication link: https://doi.org/10.1073/pnas.2308762120

A set of mouse EEG data (raw data files in [data_EEG-BIDS.zip]) and simultaneously recorded video (raw data files and processed behavioral data in [data_behavior.zip]). A mirror of this dataset is located here: https://gin.g-node.org/hiobeen/Mouse-threat-and-escape-Han-et-al-PNAS.

Check this how-to-start guide to this dataset! 
 

1. EEG dataset

Most of all, the overall dataset structure adheres to the BIDS-EEG format introduced by Pernet et al. (2019). Within the top-level directory 'data_EEG-BIDS/', the EEG data is organized under paths starting with 'sub-##/'. These EEG recordings (mouse n = 8) were recorded under the Threat-and-escape paradigm experiment, which involves dynamic interactions with a spider robot. This experiment was done in two separate conditions: the solitary condition, where a mouse was exposed to the robot alone in the arena (referred to as the 'Single' condition), and the group condition, where mice encountered the robot alongside other conspecifics (referred to as the 'Group' condition). This dataset only includes the data from solitary condition. CBRAIN headstage (Kim et al., 2019) was employed to record this EEG data at a sampling rate of 1024 Hz. The recordings were taken from the medial prefrontal cortex (channel 1) and the basolateral amygdala (channel 2). For a comprehensive understanding of the experimental methods and procedures, please see our original publications: Han et al. (2023), Cho et al. (2023), and Kim et al. (2020).

2. Position dataset

Another top-level directory, 'data_behavior/', contains simultaneously recorded video (in avi format) ('data_behavior/raw/') and position extracted from the video in csv format ('data_behavior/processed/'). The positions are located in the 'stimuli/position/' directory.

Position tracking is performed using the U-Net architecture of CNN (Ronnenberger, 2015; also see, Han et al. in press for detailed procedure). This method tracks the body area's location to extract its centroid.

3. References

Han, H. B., Shin, H. S., Jeong, Y., Kim, J., Choi, J. H., (2023) Dynamic switching of neural oscillations in the prefrontal–amygdala circuit for naturalistic freeze-or-flight, Proceedings of the National Academy of Sciences,, 120(37), https://doi.org/10.1073/pnas.2308762120

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., & Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific data, 6(1), 103.

Kim, J., Kim, C., Han, H. B., Cho, C. J., Yeom, W., Lee, S. Q., & Choi, J. H. (2020). A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC). Science Advances, 6(49).

Cho, S., & Choi, J. H. (2023). A guide towards optimal detection of transient oscillatory bursts with unknown parameters. Journal of Neural Engineering.

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234-241). Springer International Publishing.

written by Hio-Been Han, hiobeen.han@gmail.com, 2023-09-07.