用于小鼠疼痛研究中行为和大脑动态时间同步的自动眯眼法

IF 1.2 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES
Nathan McCutcheon, Micah S Johnson, Brandon Rea, Mahnoor Ghumman, Levi Sowers, Rainbo Hultman
{"title":"用于小鼠疼痛研究中行为和大脑动态时间同步的自动眯眼法","authors":"Nathan McCutcheon, Micah S Johnson, Brandon Rea, Mahnoor Ghumman, Levi Sowers, Rainbo Hultman","doi":"10.3791/67136","DOIUrl":null,"url":null,"abstract":"<p><p>Spontaneous pain has been challenging to track in real time and quantify in a way that prevents human bias. This is especially true for metrics of head pain, as in disorders such as migraine. Eye squint has emerged as a continuous variable metric that can be measured over time and is effective for predicting pain states in such assays. This paper provides a protocol for the use of DeepLabCut (DLC) to automate and quantify eye squint (Euclidean distance between eyelids) in restrained mice with freely rotating head motions. This protocol enables unbiased quantification of eye squint to be paired with and compared directly against mechanistic measures such as neurophysiology. We provide an assessment of AI training parameters necessary for achieving success as defined by discriminating squint and non-squint periods. We demonstrate an ability to reliably track and differentiate squint in a CGRP-induced migraine-like phenotype at a sub second resolution.</p>","PeriodicalId":48787,"journal":{"name":"Jove-Journal of Visualized Experiments","volume":" 213","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies.\",\"authors\":\"Nathan McCutcheon, Micah S Johnson, Brandon Rea, Mahnoor Ghumman, Levi Sowers, Rainbo Hultman\",\"doi\":\"10.3791/67136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Spontaneous pain has been challenging to track in real time and quantify in a way that prevents human bias. This is especially true for metrics of head pain, as in disorders such as migraine. Eye squint has emerged as a continuous variable metric that can be measured over time and is effective for predicting pain states in such assays. This paper provides a protocol for the use of DeepLabCut (DLC) to automate and quantify eye squint (Euclidean distance between eyelids) in restrained mice with freely rotating head motions. This protocol enables unbiased quantification of eye squint to be paired with and compared directly against mechanistic measures such as neurophysiology. We provide an assessment of AI training parameters necessary for achieving success as defined by discriminating squint and non-squint periods. We demonstrate an ability to reliably track and differentiate squint in a CGRP-induced migraine-like phenotype at a sub second resolution.</p>\",\"PeriodicalId\":48787,\"journal\":{\"name\":\"Jove-Journal of Visualized Experiments\",\"volume\":\" 213\",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jove-Journal of Visualized Experiments\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.3791/67136\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jove-Journal of Visualized Experiments","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.3791/67136","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

实时跟踪自发性疼痛并以防止人为偏差的方式对其进行量化一直是一项挑战。对于头部疼痛(如偏头痛)的测量尤其如此。眯眼已成为一种连续可变的指标,可在一段时间内进行测量,并能在此类检测中有效预测疼痛状态。本文提供了一种使用 DeepLabCut(DLC)自动量化受约束小鼠斜视(眼睑之间的欧氏距离)的方案,小鼠的头部运动可以自由旋转。该方案可将斜视的无偏量化与神经生理学等机理测量进行配对和直接比较。我们对人工智能训练参数进行了评估,这些参数是成功实现区分斜视和非斜视期所必需的。我们展示了在 CGRP 诱导的偏头痛样表型中以亚秒级分辨率可靠跟踪和区分斜视的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies.

Spontaneous pain has been challenging to track in real time and quantify in a way that prevents human bias. This is especially true for metrics of head pain, as in disorders such as migraine. Eye squint has emerged as a continuous variable metric that can be measured over time and is effective for predicting pain states in such assays. This paper provides a protocol for the use of DeepLabCut (DLC) to automate and quantify eye squint (Euclidean distance between eyelids) in restrained mice with freely rotating head motions. This protocol enables unbiased quantification of eye squint to be paired with and compared directly against mechanistic measures such as neurophysiology. We provide an assessment of AI training parameters necessary for achieving success as defined by discriminating squint and non-squint periods. We demonstrate an ability to reliably track and differentiate squint in a CGRP-induced migraine-like phenotype at a sub second resolution.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Jove-Journal of Visualized Experiments
Jove-Journal of Visualized Experiments MULTIDISCIPLINARY SCIENCES-
CiteScore
2.10
自引率
0.00%
发文量
992
期刊介绍: JoVE, the Journal of Visualized Experiments, is the world''s first peer reviewed scientific video journal. Established in 2006, JoVE is devoted to publishing scientific research in a visual format to help researchers overcome two of the biggest challenges facing the scientific research community today; poor reproducibility and the time and labor intensive nature of learning new experimental techniques.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信