A machine-vision approach for automated pain measurement at millisecond timescales
A machine-vision approach for automated pain measurement at millisecond timescales
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DOI：
10.7554/elife.57258
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发表时间：
2020-08-06
期刊：
影响因子：
7.7
通讯作者：
Abdus-Saboor, Ishmail
中科院分区：
文献类型：
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作者：
Jones, Jessica M.;Foster, William;Abdus-Saboor, Ishmail
客观和自动测量小鼠的疼痛仍然是神经科学发现的障碍。在这里，我们通过高速摄像和机器和深度学习方法的自动爪子跟踪来捕捉小鼠疼痛行为期间的爪子运动学。我们的统计软件平台PAWS（拔出速度下的疼痛评估）使用爪子位置随时间的单变量投影来自动量化七个行为特征，这些特征被组合成一个单一的单变量疼痛评分。自动爪子跟踪与PAWS相结合揭示了一种行为上不同的小鼠品系，该品系显示出对机械刺激的超敏反应。为了证明PAWS用于检测脊髓与中枢介导的行为反应的功效，我们化学发生激活杏仁核中的伤害感受神经元，其进一步分离疼痛相关的行为特征和由此产生的疼痛评分。总之，这种自动疼痛量化方法将增加收集严格行为数据的客观性，并且它与用于确定小鼠疼痛状态的其他神经回路解剖工具兼容。
Objective and automatic measurement of pain in mice remains a barrier for discovery in neuroscience. Here, we capture paw kinematics during pain behavior in mice with high-speed videography and automated paw tracking with machine and deep learning approaches. Our statistical software platform, PAWS (Pain Assessment at Withdrawal Speeds), uses a univariate projection of paw position over time to automatically quantify seven behavioral features that are combined into a single, univariate pain score. Automated paw tracking combined with PAWS reveals a behaviorally divergent mouse strain that displays hypersensitivity to mechanical stimuli. To demonstrate the efficacy of PAWS for detecting spinally versus centrally mediated behavioral responses, we chemogenetically activated nociceptive neurons in the amygdala, which further separated the pain-related behavioral features and the resulting pain score. Taken together, this automated pain quantification approach will increase objectivity in collecting rigorous behavioral data, and it is compatible with other neural circuit dissection tools for determining the mouse pain state.