Mapping the neuroethological signatures of pain, analgesia and recovery in mice
Authors/Creators
- Bohic, Manon1
- Pattison, Luke A.2
- Jhumka, Z. Anissa3
- Rossi, Heather3
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Thackray, Joshua K.4
- Ricci, Matthew5
- Mossazghi, Nahom6
- Foster, William3
- Ogundare, Simon3
- Twomey, Colin R.7
- Hilton, Helen2
- Arnold, Justin3
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Tischfield, Max A.8
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Yttri, Eric A.6
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Smith, Ewan St. John2
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Abdus-Saboor, Ishmail3
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Abraira, Victoria E.1
- 1. W.M. Keck Center for Collaborative Neuroscience, and the Cell Biology and Neuroscience Department, Rutgers , The State University of New Jersey
- 2. Department of Pharmacology, University of Cambridge
- 3. Zuckerman Mind Brain Behavior Institute and Department of Biological Sciences, Columbia University
- 4. Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey
- 5. Data Science Initiative, Brown University; School of Computer Science and Engineering, The Hebrew University of Jerusalem
- 6. Department of Biomedical Engineering, Carnegie Mellon University; Department of Biological Sciences, Carnegie Mellon University
- 7. Department of Biology, University of Pennsylvania
- 8. Cell Biology and Neuroscience Department, Rutgers, The State University of New Jersey; Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey; Tourette International Collaborative Genetics Study (TIC Genetics)
Description
Mapping the neuroethological signatures of pain, analgesia and recovery in mice.
Repository containing datasets obtained for M. Bohic, L Pattison, et al. 2023. Neuron.
Abstract
Ongoing pain is driven by direct activation of pain-sensing neurons and neuroimmune mediated sensitization. These heightened pain states alter physiology, reduce motor function, and affect motivation to engage in normal behaviors. The complexity of the pain state has evaded a comprehensive definition, especially in nonverbal animals. Here in mice, we capture the physiological state of sensitized neurons and use computational tools to automatically map behavioral signatures of evoked and spontaneous displays of pain at different time points for both an acute model of paw inflammatory pain and a chronic model of knee joint pain. First, retrograde labeling coupled with electrophysiology of neurons innervating injury sites revealed key time points corresponding to peripheral sensitivity in multiple pain models. Next, we used high-speed videography combined with supervised and unsupervised machine learning tools to uncover sensory-evoked defensive coping postures, unique to each pain condition. Using 3D pose analytics inspired by natural language processing, we identify movement sequences that correspond to robust representations of ongoing pain states. With this new analytical framework, we find that commonly used analgesics do not return an animal’s behavior to a pre-injury state. Instead, animals adopt a novel set of spontaneous behaviors that are maintained even after most evoked behavioral features of pain have resolved. Together, these findings reveal previously unidentified neuroethological signatures of pain and analgesia at timescales when inflammation induces heightened pain states and during recovery.