Selfee, self-supervised features extraction of animal behaviors

  1. Yinjun Jia  Is a corresponding author
  2. Shuaishuai Li
  3. Xuan Guo
  4. Bo Lei
  5. Junqiang Hu
  6. Xiao-Hong Xu
  7. Wei Zhang  Is a corresponding author
  1. Tsinghua University, China
  2. Chinese Academy of Sciences, China

Abstract

Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in laboratories for behavior analysis. However, it has not been achieved to use an end-to-end unsupervised neural network to extract comprehensive and discriminative features directly from social behavior video frames for annotation and analysis purposes. Here, we report a self-supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end-to-end way. Visualization and classification of the extracted features (Meta-representations) validate that Selfee processes animal behaviors in a way similar to human perception. We demonstrate that Meta-representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in-depth analysis. Furthermore, time-series analyses of Meta-representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self-supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.

Data availability

Major data used in this study were uploaded to Dryad, including pretrained weights. Data could be accessed via:https://doi.org/10.5061/dryad.brv15dvb8.With the uploaded dataset and pretrained weights, our experiments could be replicated. However, due to its huge size and the limited internet service resources, we are currently not able to share our full training dataset. The full dataset is as large as 400GB, which is hard to upload to a public server and will be difficult for others users to download.For training dataset, it would be available from the corresponding author upon reasonable request (wei_zhang@mail.tsinghua.edu.cn), and then we can discuss how to transfer the dataset. No project proposal is needed as long as the dataset is not used for any commercial purpose.Our Python scripts could be accessed on GitHub: https://github.com/EBGU/SelfeeOther software used in our project include ImageJ(https://imagej.net/software/fiji/) and GraphPad Prism(https://www.graphpad.com/).All data used to plot graphs and charts in the manuscript can be fully accessed on Dryad (DOI 10.5061/dryad.brv15dvb8).

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Yinjun Jia

    IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
    For correspondence
    jyj20@mails.tsinghua.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
  2. Shuaishuai Li

    Institute of Neuroscience, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Xuan Guo

    IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Bo Lei

    IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Junqiang Hu

    IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Xiao-Hong Xu

    Institute of Neuroscience, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Wei Zhang

    IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
    For correspondence
    wei_zhang@mail.tsinghua.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0512-3096

Funding

National Natural Science Foundation of China (32022029)

  • Wei Zhang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All mating experiments were approved by the Animal Care and Use Committee of the Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China (IACUC No. NA-016-2016)All studies and experimental protocols of CIS and OFT were approved by Institutional Animal Care and Use Committee (IACUC) at Tsinghua University (No. 19-ZY1). Experiments were performed using the principles outlined in the Guide for the Care and Use of Laboratory Animals of Tsinghua University.

Copyright

© 2022, Jia et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,039
    views
  • 755
    downloads
  • 15
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Yinjun Jia
  2. Shuaishuai Li
  3. Xuan Guo
  4. Bo Lei
  5. Junqiang Hu
  6. Xiao-Hong Xu
  7. Wei Zhang
(2022)
Selfee, self-supervised features extraction of animal behaviors
eLife 11:e76218.
https://doi.org/10.7554/eLife.76218

Share this article

https://doi.org/10.7554/eLife.76218

Further reading

    1. Neuroscience
    Ulrike Pech, Jasper Janssens ... Patrik Verstreken
    Research Article

    The classical diagnosis of Parkinsonism is based on motor symptoms that are the consequence of nigrostriatal pathway dysfunction and reduced dopaminergic output. However, a decade prior to the emergence of motor issues, patients frequently experience non-motor symptoms, such as a reduced sense of smell (hyposmia). The cellular and molecular bases for these early defects remain enigmatic. To explore this, we developed a new collection of five fruit fly models of familial Parkinsonism and conducted single-cell RNA sequencing on young brains of these models. Interestingly, cholinergic projection neurons are the most vulnerable cells, and genes associated with presynaptic function are the most deregulated. Additional single nucleus sequencing of three specific brain regions of Parkinson’s disease patients confirms these findings. Indeed, the disturbances lead to early synaptic dysfunction, notably affecting cholinergic olfactory projection neurons crucial for olfactory function in flies. Correcting these defects specifically in olfactory cholinergic interneurons in flies or inducing cholinergic signaling in Parkinson mutant human induced dopaminergic neurons in vitro using nicotine, both rescue age-dependent dopaminergic neuron decline. Hence, our research uncovers that one of the earliest indicators of disease in five different models of familial Parkinsonism is synaptic dysfunction in higher-order cholinergic projection neurons and this contributes to the development of hyposmia. Furthermore, the shared pathways of synaptic failure in these cholinergic neurons ultimately contribute to dopaminergic dysfunction later in life.

    1. Neuroscience
    Gergely F Turi, Sasa Teng ... Yueqing Peng
    Research Article

    Synchronous neuronal activity is organized into neuronal oscillations with various frequency and time domains across different brain areas and brain states. For example, hippocampal theta, gamma, and sharp wave oscillations are critical for memory formation and communication between hippocampal subareas and the cortex. In this study, we investigated the neuronal activity of the dentate gyrus (DG) with optical imaging tools during sleep-wake cycles in mice. We found that the activity of major glutamatergic cell populations in the DG is organized into infraslow oscillations (0.01–0.03 Hz) during NREM sleep. Although the DG is considered a sparsely active network during wakefulness, we found that 50% of granule cells and about 25% of mossy cells exhibit increased activity during NREM sleep, compared to that during wakefulness. Further experiments revealed that the infraslow oscillation in the DG was correlated with rhythmic serotonin release during sleep, which oscillates at the same frequency but in an opposite phase. Genetic manipulation of 5-HT receptors revealed that this neuromodulatory regulation is mediated by Htr1a receptors and the knockdown of these receptors leads to memory impairment. Together, our results provide novel mechanistic insights into how the 5-HT system can influence hippocampal activity patterns during sleep.