Abstract

Recognizing and responding to threat cues is essential to survival. Freezing is a predominant threat behavior in rats. We have recently shown that a threat cue can organize diverse behaviors beyond freezing, including locomotion (Chu et al., 2024). However, that experimental design was complex, required many sessions, and had rats receive many foot shock presentations. Moreover, the findings were descriptive. Here, we gave female and male Long Evans rats cue light illumination paired or unpaired with foot shock (8 total) in a conditioned suppression setting, using a range of shock intensities (0.15, 0.25, 0.35, or 0.50 mA). We found that conditioned suppression was only observed at higher foot shock intensities (0.35 mA and 0.50 mA). We constructed comprehensive temporal ethograms by scoring 22,272 frames across 12 behavior categories in 200-ms intervals around cue light illumination. The 0.50 mA and 0.35 mA shock-paired visual cues suppressed reward seeking, rearing, and scaling, as well as light-directed rearing and light-directed scaling. The shock-paired visual cue further elicited locomotion and freezing. Linear discriminant analyses showed that ethogram data could accurately classify rats into paired and unpaired groups. Using complete ethogram data produced superior classification compared to behavior subsets, including an Immobility subset featuring freezing. The results demonstrate diverse threat behaviors – in a short and simple procedure – containing sufficient information to distinguish the visual fear conditioning status of individual rats.

Data availability

Raw frames and scored behaviors have been deposited: doi:10.7910/DVN/Z4YJRJ

The following data sets were generated

Article and author information

Author details

  1. David C Williams

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  2. Amanda Chu

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  3. Nicholas T Gordon

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  4. Aleah M DuBois

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  5. Suhui Qian

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  6. Genevieve Valvo

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  7. Selena Shen

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  8. Jacob B Boyce

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  9. Anaise C Fitzpatrick

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  10. Mahsa Moaddab

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  11. Emma L Russell

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  12. Liliuokalani H Counsman

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    Competing interests
    No competing interests declared.
  13. Michael A McDannald

    Department of Psychology and Neuroscience, Boston College, Chestnut Hill, United States
    For correspondence
    michael.mcdannald@bc.edu
    Competing interests
    Michael A McDannald, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8525-1260

Funding

National Institutes of Health (R01-MH117791)

  • Michael A McDannald

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

Ethics

Animal experimentation: Animal care was in accordance with NIH and Boston College guidelines. The Boston College experimental protocol supporting these procedures is 2024-001.

Copyright

© 2025, Williams et al.

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

Metrics

  • 451
    views
  • 66
    downloads
  • 0
    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. David C Williams
  2. Amanda Chu
  3. Nicholas T Gordon
  4. Aleah M DuBois
  5. Suhui Qian
  6. Genevieve Valvo
  7. Selena Shen
  8. Jacob B Boyce
  9. Anaise C Fitzpatrick
  10. Mahsa Moaddab
  11. Emma L Russell
  12. Liliuokalani H Counsman
  13. Michael A McDannald
(2025)
Ethograms predict visual fear conditioning status in rats
eLife 14:e102782.
https://doi.org/10.7554/eLife.102782

Share this article

https://doi.org/10.7554/eLife.102782

Further reading

    1. Neuroscience
    Sven Ohl, Martin Rolfs
    Research Article

    Detecting causal relations structures our perception of events in the world. Here, we determined for visual interactions whether generalized (i.e. feature-invariant) or specialized (i.e. feature-selective) visual routines underlie the perception of causality. To this end, we applied a visual adaptation protocol to assess the adaptability of specific features in classical launching events of simple geometric shapes. We asked observers to report whether they observed a launch or a pass in ambiguous test events (i.e. the overlap between two discs varied from trial to trial). After prolonged exposure to causal launch events (the adaptor) defined by a particular set of features (i.e. a particular motion direction, motion speed, or feature conjunction), observers were less likely to see causal launches in subsequent ambiguous test events than before adaptation. Crucially, adaptation was contingent on the causal impression in launches as demonstrated by a lack of adaptation in non-causal control events. We assessed whether this negative aftereffect transfers to test events with a new set of feature values that were not presented during adaptation. Processing in specialized (as opposed to generalized) visual routines predicts that the transfer of visual adaptation depends on the feature similarity of the adaptor and the test event. We show that the negative aftereffects do not transfer to unadapted launch directions but do transfer to launch events of different speeds. Finally, we used colored discs to assign distinct feature-based identities to the launching and the launched stimulus. We found that the adaptation transferred across colors if the test event had the same motion direction as the adaptor. In summary, visual adaptation allowed us to carve out a visual feature space underlying the perception of causality and revealed specialized visual routines that are tuned to a launch’s motion direction.

    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.