Name: a novel single animal motor function tracking system using simple, readily available software
Uploaded: 2018-08-31T13:30:00.000+00:00
Duration: 8 min 22 s
Description: Watch this Scientific Journal Video about A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software at JoVE.com

这项研究得到了 B1495-R (杰弗里 r. Capadona) 和美国退伍军人事务部的科学家和工程师 (美国青年) (杰弗里 r Capadona) 总统早期职业奖的支持。研究开发服务。此外, 这项工作还部分由主管卫生事务的助理国防部长办公室通过经同侪审查的医学研究计划获得支持。W81XWH-15-1-0608。作者承认其暑期研究资助的资金来源。这些内容不代表美国退伍军人事务部或美国政府的意见。作者感谢博行荒川在 CWRU 啮齿动物行为核心为他的指导设计和测试啮齿动物行为协议。作者还要感谢 CWRU 机械和航空工程系的詹姆斯. 德雷克和凯文. 他在设计和制造啮齿动物阶梯试验方面的帮助。

所有程序和动物护理的做法都是根据路易斯. 克利夫兰退伍军人事务部医疗中心机构动物护理和使用委员会批准和执行的。行为测试协议紧跟以前发布的工作12,13。
1. 行为测试: 拍摄任务
注意: 在这里, 动物被测试了8周, 以检测任何慢性行为变化。研究持续时间取决于用于研究的应用/损伤/疾病模型。
<ol><li>
开放域网格测试 注: 网格测试有一个1米2区域与 40 cm 墙壁并且被修造了内部。底部被分成九个正方形通过明亮的彩色磁带 (图 1A)。对于自动 BVAS 的使用来说, 线条和网格的颜色与大鼠形成鲜明的对比是很重要的。这里使用白大鼠;因此, 网格背景被画成黑色, 线条是用明亮的粉红色胶带创建的。<ol>
<li>测试动物每星期2x 的所有测试, 包括在开始研究前的一周, 以便创建基线评分, 以便在以后的计算中使用。</li>
		<li>在每次测试会议开始和每种动物之间, 用二氧化氯基消毒剂清洁所有测试设备。</li>
		<li>将动物带到房间进行测试, 并允许他们在开始试验前20分钟调整到房间。</li>
		<li>开始拍摄并将动物放在网格的中心, 面对研究员, 开始进行网格测试。</li>
		<li>允许动物自由运行3分钟, 而视频记录从上面。</li>
		<li>停止拍摄时, 3 分钟的时间已经结束, 并返回该动物的家庭笼。使用以二氧化氯为基础的消毒剂清洁网格, 在测试前要注意充分干燥表面。每节测试每种动物1x。</li>
	</ol>
</li>
	<li>
梯子测试 注: 梯子测试是内置的, 并由两个丙烯酸侧壁, 每1米长度, 连接3毫米直径的梯级间隔在2厘米间隔 (图 2)。这些动物需要一周的阶梯训练才能开始正式的测试。培训和测试协议之间没有区别。请注意, 培训不需要拍摄。<ol>
<li>用于与 BVAS, 用黑色海报板覆盖在梯子后面的墙, 并描绘了水平的阶梯, 开始, 并完成线与明亮的磁带, 以创建与白鼠的对比。</li>
		<li>测试动物每星期2x 的所有测试, 包括在开始研究前的一周, 以创建基线评分, 以便在以后的计算中使用。</li>
		<li>在每次测试会议开始和每种动物之间, 用二氧化氯基消毒剂清洁所有测试设备。</li>
		<li>将动物带到房间进行测试, 并允许他们在开始试验前20分钟调整到房间。</li>
		<li>在梯子测试之前把动物放在一个干净的临时笼子里。 注意: 临时笼子是一个短期笼子容纳动物, 而动物的家庭笼子是使用在梯子的尽头。没有年龄偏见, 因为所有的动物使用的研究是相同的年龄和完整的测试在同一时间段。这里使用的控制是一个天真的动物从来没有接受过手术。</li>
		<li>将梯子装置设置为跨越两个笼子;开始笼子是一个干净的笼子, 并且末端笼子是动物的生存笼子, 刺激完成奔跑。</li>
		<li>把相机放在三脚架上, 以梯子的长度为中心。延长三脚架, 使相机的镜头是在梯子的高度。定位摄像头, 使横档与透镜完全对准, 因为这对于 BVAS 代码中的滑动检测算法很重要。</li>
		<li>开始视频录音, 让老鼠开始运行时, 把他们的前爪在梯子的第一个梯级。允许动物移动到梯子上。</li>
		<li>允许动物在拍摄时以自己的速度从起始线移动到终点线。一旦动物完成了跑步, 结束录像并从梯子上移走动物。</li>
		<li>考虑运行失败的运行, 如果动物转过身或保持停滞, 二十年代. 如果动物跑不动, 结束录像, 从梯子上取下动物。为失败的运行分配一个点球评分, 这相当于在手术前基线测试中记录的最慢的时间。</li>
		<li>确保每一个动物每测试一天完成5个运行, 并在每次试验之间给他们1分钟的休息时间。 注意: 在这项研究中, 动物每星期测试 2x 8 周。然而, 时间是由研究者自行决定的。</li>
	</ol>
</li>
</ol>2. 文件存储和命名
注意: BVAS 代码使用专门指定的视频文件和文件夹命名约定, 以便能够可靠地分析和分析视频。当前不支持不同的命名约定。测试会话完成后, 视频文件将以默认名称保存在默认位置。
<ol><li>
单个视频命名
	<ol>
<li>命名每个视频与动物的指定所有的网格测试视频。</li>
		<li>为每个动物每测试一次收集5个梯子视频。选择所有五视频并重命名第一个视频, 使用动物的指定。例如, 如果动物的名字是 "C1NS", 这些文件被命名为 "C1NS (1)", "C1NS (2)", "C1NS (3)"等。
</li>
	</ol>
</li>
	<li>
存储文件夹命名
	<ol>
<li>拍摄现在以每种动物命名的视频, 并将文件放在自己的存储文件夹中。使用以下约定命名此文件夹: "TestingWeek_TestMode_MM_DD_YY"。 注: 例如, 如果此文件夹持有网格视频的第一周, 并在 1月1日st2018 拍摄, 则保存这些视频的文件夹被命名为 "Week1_GridTest_01_01_18"。</li>
		<li>将这个专门命名的文件夹放在另一个文件夹中, 这将表示这组实验所属于的更大的研究。 注意: 文件夹树系统没有命名要求来保持学习和测试模式的组织。系统选择的唯一文件夹是保存在步骤2.2.1 中命名的视频的文件夹。</li>
	</ol>
</li>
	<li>
电子表格文件创建
	<ol>
<li>使用外部电子表格程序 (请参阅材料表), 创建一个新的空电子表格文件, 以存储每个研究和测试模式的数据。 注意: 此文件没有命名或存储位置要求。每项研究都需要两个电子表格文件供系统使用, 一个用于网格测试, 一个用于梯形测试。现在保留这些文件为空。</li>
		<li>检查以确保在 BVAS 运行时, 这些电子表格文件不会在任何计算机上打开。 注意: 如果 BVAS 尝试打开已打开的电子表格文件, 则会导致错误。</li>
	</ol>
</li>
</ol>3. 系统安装
注意: BVAS 是在 PC 操作系统上建立和测试的, 并利用运行时组件从简单且易于使用的编码软件。当前不支持其他系统配置。
<ol><li>
安装最新的 BVAS 版本
	<ol>
<li>通过双击标记为 "BVASX" 的安装程序图标运行安装程序。XXinstaller。"x XX" 是当前版本号, 目前是 "BVAS3.30installer"。 注: 这是开源软件。请与相应的作者联系以得到最新版本。</li>
		<li>按照屏幕上的说明安装 BVAS。 注: 安装程序将检查系统中是否有正确版本的软件 (例如, MATLAB 运行时), 如果找不到, 它将从 internet 安装软件。</li>
		<li>安装后, 单击 "BVAS.exe"文件启动程序。</li>
	</ol>
</li>
</ol>4. 系统使用
<ol><li>打开程序
	<ol>
<li>双击 BVAS 程序图标以弹出 BVAS 主菜单, 允许以下选项: "网格分析"、"梯形分析"、"梯子审阅" 或 "退出"。 注意: 还有一个下拉菜单位于左上角, 标签为 "电子邮件设置" (图 3a)。</li>
	</ol></li>
	<li>电子邮件设置
	<ol>
<li>单击 "电子邮件设置" 下拉菜单, 然后首先选择 "更改电子邮件目的地"。</li>
		<li>输入首选电子邮件地址以接收分析的更新并选择 "确定"。选择 "取消" 不更改当前保存的电子邮件。</li>
		<li>单击 "发送测试电子邮件" 按钮, 将测试电子邮件发送到当前保存在系统中的电子邮件。关闭 "电子邮件发送!" 窗口弹出式表示测试完成。</li>
	</ol></li>
	<li>野外网格测试分析
	<ol>
<li>从顶部菜单屏幕中选择 "网格分析"。</li>
		<li>确保 "单个视频/整个会话/绘图数据" 的切换按钮显示整个会话, 以完成对整套测试会话视频的分析。</li>
		<li>利用上输入框旁边的 "..." 按钮浏览文件系统以查找要分析的视频文件。选择在步骤2.2.1 中讨论的样式中标记的文件夹。</li>
		<li>利用下一输入框旁边的按钮来浏览文件系统以选择存储数据的电子表格文件。 注意: 请注意, 除了将数据输出到电子表格文件之外, 每个视频的完整路径数据都存储在包含步骤4.3.3 中选择的视频的同一目录中的数据文件 (. 席子) 中。</li>
		<li>确保步骤4.3.2 中提到的切换按钮设置为 "单个视频" 以完成对单个视频的分析。</li>
		<li>利用上面输入框旁边的按钮来浏览文件系统以查找要分析的视频文件。</li>
		<li>利用下一个输入框旁边的按钮来浏览文件系统, 以选择一个目录来保存数据文件 (. 垫), 这是由单一的视频分析结果。 注: 数据文件将以该视频命名, 以动物命名。</li>
		<li>从步骤4.3.2 中提到的切换中选择 "输出数据"。从以前分析过的网格视频中输出数据。图 1B和1C显示了这些绘制的数据的示例。</li>
		<li>利用上面输入框旁边的按钮来浏览文件系统以选择以前创建的网格视频数据文件。选择 "转到" 以创建绘图和数据图。 注意: 此函数还创建一个文本文件, 名称相同, 并与数据文件位于同一个目录中, 其中有动物的运行数据。</li>
		<li>一旦找到了所需的文件, 请选择 "转到" 开始视频分析。请注意, "返回" 按钮将重新打开顶部菜单, "退出" 按钮将完全关闭该程序。选择 "调试显示" 切换以显示跟踪算法的可视表示形式 (图 3C和3D)。</li>
		<li>选择 "转到" 开始分析过程。检查审阅者屏幕上每个视频的图像。如果有超过五个视频, 请使用 "下一页" 和 "上一个" 页面按钮。</li>
		<li>若要显示手动选择菜单, 请选择 "编辑" 按钮 (如果没有绿线), 或者绿线看起来不在测试区域周围的正确正方形中。要编辑角, 请选择 "编辑角" 按钮。 注: "下一帧" 按钮将代表图像更改为所讨论视频的下一帧。如果自动给出的帧受阻或不集中, 则选择 "下一帧" 按钮。</li>
		<li>按照代表图像右侧的说明选择测试区域并按 "Enter" 完成选择过程。 注意: 选定的绿线现在将显示在代表图像上。</li>
		<li>选择 "完成" 以保存所选内容并返回审阅者屏幕。</li>
		<li>选择 "完成" 按钮以在每个视频的网格区域正确选择后启动完整的分析过程。允许程序运行, 并在完成后将出现成功提示。 注: 对视频会话的完整分析可能需要一段时间, 每只动物大约10分钟。</li>
	</ol></li>
	<li>梯子测试分析 注意: 初始梯形测试分析的协议与步骤4.3 中概述的协议非常相似. 10–4.3. 15。这些步骤之前的显著差异如下:<ol>
<li>从顶部菜单中选择 "梯子分析" 以打开 "梯子分析" 菜单。</li>
		<li>查看切换中的两个选项, 即 "单个视频" 和 "整个会话" (图 3B)。</li>
		<li>使用 "整个会话", 选择上一输入... 按钮以选择在步骤2.2.1.Select 中命名的梯形视频的文件夹下... 按钮, 选择一个目录, 以保存一个文件夹的数据从梯子视频, 以便以后审阅。</li>
		<li>如果选择了 "单个视频", 请选择带有上.. 按钮和位置的单个视频文件, 与在步骤4.4.3 中相同。</li>
		<li>选择 "转到" 开始分析过程, 并提出梯子测试区域审阅者屏幕。</li>
		<li>按照步骤 4.3. 10–4.3. 15 相同的方式为梯子为网格。允许程序运行, 并在完成后将出现成功提示。 注意: 如果在分析过程中出现问题, 电子邮件将发送到步骤4.2.2 中输入的电子邮件地址。完成任何类型的分析后, 将完成电子邮件发送到相同的电子邮件地址。</li>
	</ol></li>
	<li>阶梯图像审阅 注意: 下面的协议是回顾以前分析过的梯子视频, 以确认任何故障和爪子滑。注意, 在 "梯形图像审阅" (步骤 4.5. 1–4.5 5) 完成之前, 不会生成用于查看梯子交叉点的电子表格文件。<ol>
<li>从顶部菜单中, 选择 "梯子图像审阅"。这就带来了梯子审查文件选择屏幕。</li>
		<li>选择 "顶部..." 按钮以选择数据文件的文件夹。 注意: 这些文件保存在步骤4.4.2 中选择的位置, 并以以下格式命名: "M1_D1_Y1 梯视频会话 HH_MM M2_D2_YYYY"。M1、D1 和 Y1 是分析会话的日期。"阶梯视频会话" 将阅读 "单视频分析", 如果只有一个视频分析。HH_MM 和 M2_D2_YYYY 是开始审阅会话的时间和日期。</li>
		<li>选择底部... 以选择为研究创建的电子表格文件。</li>
		<li>选择 "转到" 开始手动审阅过程。</li>
		<li>查看自动打开的 "梯子数据审阅者" 菜单。如果检测到滑动, 请使用 "下一帧" 和 "最后一帧" 按钮检查任何连续录制的帧 (如果可用)。</li>
		<li>利用滑动开关表示老鼠的脚滑倒了。如果检测是假阳性, 请将开关放在 "请勿滑动" 上。单击 "保存/继续" 以移动到下一个检测 (图 2A)。</li>
		<li>在失败的情况下, 使用切换指示故障是由于逆转 (在运行过程中鼠转过来) 还是停滞 (老鼠未能及时完成运行)。</li>
		<li>单击 "保存/继续" 以移动到下一个检测。在上次检测时, 单击 "保存/继续" 以完成分析, 并将数据保存到前面选择的电子表格文件中。驳回完成通知。 注意: 审阅完成后, 被审阅的文件夹被重命名为 "审阅 M1_D1_Y1 梯形视频会话 HH_MM M2_D2_YYYY"。</li>
	</ol></li>
</ol>5. 输出数据分析
<ol><li>
网格电子表格文件
	<ol>
<li>从每个网格视频中找到在电子表格文件中创建的数据。 注: 列的左向右如下: 动物名称, 测试日期, 学习次数, 总行程, 总时间停止, 最大速度达到, 右转弯, 左转弯, 网格线交叉 (图 1D)。</li>
		<li>使用这些指标量化的总马达性能和创建比较地块之间的动物和时间点。</li>
	</ol>
</li>
	<li>
梯子电子表格文件
	<ol>
<li>在失败和爪子滑动检查之后, 从每个梯子视频中找到所创建的电子表格文件中的数据。 注: 列从左向右, 如下所示: 动物名称、运行编号1–5、学习数周、测试日期、运行类型 (成功或失败)、成功运行的时间、完成百分比、每个爪子上的爪子滑和故障模式。对于失败, 时间列为空, 失败模式列将读取反转或停滞 (图 2B)。</li>
		<li>使用这些指标来量化优良的马达性能, 并创建动物和时间点之间的比较地块。</li>
	</ol>
</li>
</ol>

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作者没有什么可透露的。

该协议最关键的部分, 以确保强大的分析是一致的拍摄。如果视频是很好的灯光和拍摄在正确的位置, 正如在第一部分讨论的协议, 系统将能够做一个精确的分析。与任何图像处理问题一样, 在预处理过程中完成的工作将使后处理更加准确和简单。因此, 确保仪器和动物在测试过程中有良好的光照, 并且在框架中的任何阴影或其他运动保持在最低限度, 这意味着 BVAS 可以在更高的精度水平上运行。
...

有许多建立的方法来评估功能和行为马达和认知障碍1,2,3。一些比较常用的方法包括通过爪子放置、步进和在梯子试验4上的肢体协调来测试精细的马达功能,通过野外网格测试测试总马达功能和应力行为5 ,6, 并通过强制游泳测试7,8或转子杆9测试恐惧, 抑郁症和绝望。然而, 许多这些方法依赖于人类研究人员 "评分" 动物或判断其表现主观。对主观的人的评估的需要可能减慢数据的世代和分析, 并且当前机会为研究偏见的故意或无意的影响在研究10。此外, 对数据的主观评估也带来了不准确的数据表示的风险, 无论是通过健忘、不良动机、不当训练, 还是过失11。
我们最近报告了在植入 intracortical 电极12,13的大鼠的开放田间网格试验和阶梯试验的使用。由于这些研究发现的新颖性, 我们立即开始在实验室进行的许多研究中使用这些和额外的功能测试。由于主观评价者数量的增加而导致的无意的人为产生的变异性, 并为了提高分析吞吐量, 我们着手创建一个自动化的、计算机辅助的程序来评分行为测试,并极大地限制了误差的可能性。
在这里, 我们报告 BVAS 的发展。BVAS 采用计算机分析法, 分别将野外网格测试和梯形测试作为总的和精细的运动函数的度量。这些结果可以用来阐明可能的运动功能缺陷造成的伤害或疾病, 无论是伤害或疾病模型。分析代码可以调整, 以解释行为测试设备的变化或得分的各种指标的运动功能。因此, BVAS 可以在许多应用中实现, 超出了我们的预期用途, 也可用于其他实验室目前使用的那些。
请注意, 打开的字段网格和梯形测试需要视频录制。因此, 每个测试都需要一个摄像机 [1080 p, 最少15帧每秒 (fps)], 一台笔记本电脑, 和一个存储视频数据的房间。对于这两种测试, 把相机放在一个中心位置, 使整个设备可以在框架上看到。将相机固定在三脚架或脚手架上, 以便在测试期间不移动。尽量保持视频帧的边缘与测试设备的边缘平行。确保相同的人员完成所有测试, 房间是良好的照明与温度控制系统。在整个测试过程中, 对所有动物使用相同的房间, 对房间进行最小的更改。谷物或香蕉薯片可以奖励动物完成行为测试。

<table><tbody><tr><td>Sprague Dawley rats, male, 201-225 g</td><td>Charles River</td><td>CD</td><td></td></tr><tr><td>Webcam HD Pro c920</td><td>Logitec</td><td>960-000764</td><td></td></tr><tr><td>Excel</td><td>Microsoft</td><td>N/A</td><td></td></tr><tr><td>Matalb 2017a, Computer Vision System Toolbox</td><td>Mathworks</td><td>N/A</td><td></td></tr><tr><td>Open field grid test</td><td>Made in-house at Case Western Reserve University</td><td>N/A</td><td></td></tr><tr><td>Ladder test</td><td>Made in-house at Case Western Reserve University</td><td>N/A</td><td></td></tr></tbody></table>

a novel single animal motor function tracking system using simple, readily available software

我们最近证明, 植入 intracortical 电极在大鼠的运动 corteces 导致立即和持久的运动缺陷。通过露天场网格测试, 通过一个测量总马达功能的运动损伤, 通过阶梯测试测量电机的精细功能。在这里, 我们讨论了一个技术, 自动量化的视频记录测试使用我们的定制 Capadona 行为视频分析系统: 网格和梯子测试, 或 BVAS。利用简单和易于使用的编码软件 (见材料表), 这个程序允许跟踪一个单一的动物在开放的领域网格和梯子测试。在开放域网格跟踪中, 代码阈值视频的强度, 跟踪大鼠的位置在3分钟的网格测试期间, 并分析路径。它然后计算和返回测量的总距离, 达到的最大速度, 左和右转数, 和网格线的总数量的大鼠。在梯子跟踪, 代码再次阈值视频的强度, 跟踪移动的大鼠在梯子上, 并返回计算的测量, 包括时间花了大鼠穿过梯子, 在飞机下发生的爪子滑的数量阶梯梯级, 以及由于停滞或逆转而导致的失败的发生率。我们设想在这里开发的 BVAS 可以用于分析各种应用中的运动功能, 包括许多损伤或疾病模型。

根据这里提出的方法, 老鼠完成了开放领域网格和梯子测试每星期2x。对数据进行了分析, 通过使用 BVAS 和手动与停止手表的培训和新手审评员。所给出的结果是一个单一的非植入性控制动物在8周的研究中平均每周得分, 其中周 "0" 对应基线测试。注意1周没有测试, 因为这是手术动物的休息周。由于打开的字段网格每天测试 1x, 因此梯子每天测试 5x, 每周有两个测试天, 有一?...

Watch this Scientific Journal Video about A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software at JoVE.com

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

目前的研究旨在自动化对大鼠运动缺陷的量化。最初的评估模型评估电机皮质 intracortical 微电极植入引起的马达损耗。我们报告的开发和使用的跟踪算法使用易于适应, 简单, 易于使用的编码软件。

一种新的单动物运动功能跟踪系统, 使用简单、易于使用的软件

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor ...

a-novel-single-animal-motor-function-tracking-system-using-simple

Universidad San Sebastian

Research

JoVE Journal

Behavior

6.5K 次观看.  Louis Stokes Cleveland Department of Veterans Affairs Medical Center. 目前的研究旨在自动化对大鼠运动缺陷的量化。最初的评估模型评估电机皮质 intracortical 微电极植入引起的马达损耗。我们报告的开发和使用的跟踪算法使用易于适应, 简单, 易于使用的编码软件。

视频: A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software

Locomotor activity (LMA) is a simple and easily performed measurement of behavior in mice and other rodents. Improvements in video tracking software (VTS) have allowed it to be coupled to LMA testing, dramatically improving specificity and sensitivity when compared to the line crossings method with manual scoring. In addition, VTS enables high-throughput experimentation. While similar to automated video tracking used for the open field test (OFT), LMA testing is unique in that it allows mice to remain in their home cage and does not utilize the anxiogenic stimulus of bright lighting during the active phase of the light-dark cycle. Traditionally, LMA has been used for short periods of time (mins), while longer movement studies (hrs-days) have often used implanted transmitters and biotelemetry. With the option of real-time tracking, long-, like short-term LMA testing, can now be conducted using videography. Long-term LMA testing requires a specialized, but easily constructed, cage so that food and water (which is usually positioned on the cage top) does not obstruct videography. Importantly, videography and VTS allows for the quantification of parameters, such as path of mouse movement, that are difficult or unfeasible to measure with line crossing and/or biotelemetry. In sum, LMA testing coupled to VTS affords a more complete description of mouse movement and the ability to examine locomotion over an extended period of time.

Locomotor activity (LMA) is a simple and easily performed measurement of behavior in mice. Coupling of video tracking software (VTS) and LMA allows for the improvement of specificity and sensitivity, especially when compared with the manual, line crossing method of LMA analysis. Additionally VTS allows long-term tracking of mouse LMA.

鼠标短期和长期活动性分析视频跟踪软件

Locomotor  activity (LMA) is a simple and easily performed measurement of behavior in mice  and other rodents. Improvements in video tracking software ...

科研

行为学

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

We introduce the foot-stepping serial reaction time (SRT) task. This modified SRT task, complementing the classic SRT task that involves only finger-pressing movement, better approximates daily sequenced activities and allows researchers to study the dynamic processes underlying discrete response measures and disentangle the explicit process operating in implicit sequence learning.

隐式电机序列学习中的 "马达": 步进串行反应时间任务

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of complex algorithms and mathematical modeling that accompany 3D data collection and analysis procedures. This work provides a simple, user-friendly, step-by-step detailed methodology for 3D kinematic gait analysis during treadmill locomotion in healthy and neurotraumatic rats using a six-camera motion capture system. Also provided are details on 1) calibration of the system in an experimental set-up customized for quadrupedal locomotion, 2) data collection for treadmill locomotion in adult rats using markers positioned on all four limbs, 3) options available for video tracking and processing, and 4) basic 3D kinematic data generation and visualization and quantification of data using the built-in data collection software. Finally, it is suggested that the utility of this motion capture system be expanded to studying a variety of motor behaviors before and after neurotrauma.

Presented here is a protocol to collect and analyze three-dimensional kinematics of quadrupedal locomotion in rodents for preclinical studies.

3D运动性步态分析,用于啮齿动物的临床前研究

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of ...

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. Several automated versions of skilled reaching have been developed recently. Here, we describe a version that automatically presents pellets to rats while recording high-definition video from multiple angles at high frame rates (300 fps). The paw and individual digits are tracked with DeepLabCut, a machine learning algorithm for markerless pose estimation. This system can also be synchronized with physiological recordings, or be used to trigger physiologic interventions (e.g., electrical or optical stimulation).

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. We describe an automated version of skilled reaching with motion tracking and three-dimensional reconstruction of reach trajectories.

自动大鼠单丸到达与3维重建的爪子和数字轨迹

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the ...

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Exploring the neurobiological mechanisms of social behavior requires behavioral tests that can be applied to animal models in an unbiased and observer-independent manner. Since the beginning of the millennium, the three-chamber test has been widely used as a standard paradigm to evaluate sociability (social preference) and social novelty preference in small rodents. However, this test suffers from multiple limitations, including its dependence on spatial navigation and negligence of behavioral dynamics. Presented and validated here is a novel experimental system that offers an alternative to the three-chamber test, while also solving some of its caveats. The system requires a simple and affordable experimental apparatus and publicly available open-source analysis system, which automatically measures and analyzes multiple behavioral parameters at individual and population levels. It allows detailed analysis of the behavioral dynamics of small rodents during any social discrimination test. We demonstrate the efficiency of the system in analyzing the dynamics of social behavior during the social preference and social novelty preference tests as performed by adult male mice and rats. Moreover, we validate the ability of the system to reveal modified dynamics of social behavior in rodents following manipulations such as whisker trimming. Thus, the system allows for rigorous investigation of social behavior and dynamics in small rodent models and supports more accurate comparisons between strains, conditions, and treatments.

Described here is a novel automated experimental system that offers an alternative to the three-chamber test and also solves several caveats. This system supplies multiple behavioral parameters that enable rigorous analysis of small rodent behavioral dynamics during the social preference and social novelty preference tests.

跟踪小啮齿动物社会偏好行为动态的系统

Exploring the neurobiological mechanisms of social behavior requires behavioral tests that can be applied to animal models in an unbiased and ...

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on the triggering of lever and nose-poke sensors present inside the chambers. While this provides a detailed view of when and how animals perform certain responses, it cannot be used to evaluate behaviors that do not trigger any sensors. As such, assessing how animals position themselves and move inside the chamber is rarely possible. To obtain this information, researchers generally have to record and analyze videos. Manufacturers of operant conditioning chambers can typically supply their customers with high-quality camera setups. However, these can be very costly and do not necessarily fit chambers from other manufacturers or other behavioral test setups. The current protocol describes how to build an inexpensive and versatile video camera using hobby electronics components. It further describes how to use the image analysis software package DeepLabCut to track the status of a strong light signal, as well as the position of a rat, in videos gathered from an operant conditioning chamber. The former is a great aid when selecting short segments of interest in videos that cover entire test sessions, and the latter enables analysis of parameters that cannot be obtained from the data logs produced by the operant chambers.

This protocol describes how to build a small and versatile video camera, and how to use videos obtained from it to train a neural network to track the position of an animal inside operant conditioning chambers. This is a valuable complement to standard analyses of data logs obtained from operant conditioning tests.

使用多功能自制摄像机和 DeepLabCut 跟踪操作调节室中的大鼠

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on ...

A Simple Technique to Assay Locomotor Activity in Drosophila

Drosophila melanogaster is an ideal model organism for studying various diseases due to its abundance of advanced genetic manipulation techniques and diverse behavioral features. Identifying behavioral deficiency in animal models is a crucial measure of disease severity, for example, in neurodegenerative diseases where patients often experience impairments in motor function. However, with the availability of various systems to track and assess motor deficits in fly models, such as drug-treated or transgenic individuals, an economical and user-friendly system for precise evaluation from multiple angles is still lacking. A method based on the AnimalTracker application programming interface (API) is developed here, which is compatible with the Fiji image processing program, to systematically evaluate the movement activities of both adult and larval individuals from recorded video, thus allowing for the analysis of their tracking behavior. This method requires only a high-definition camera and a computer peripheral hardware integration to record and analyze behavior, making it an affordable and effective approach for screening fly models with transgenic or environmental behavioral deficiencies. Examples of behavioral tests using pharmacologically treated flies are given to show how the techniques can detect behavioral changes in both adult flies and larvae in a highly repeatable manner.

A Simple Technique to Assay Locomotor Activity in Drosophila

The present protocol assesses the locomotor activity of Drosophila by tracking and analyzing the movement of flies in a hand-made arena using open-source software Fiji, compatible with plugins to segment pixels of each frame based on high-definition video recording to calculate parameters of speed, distance, etc.

一种测定果蝇运动活性的简单技术

Drosophila melanogaster is an ideal model organism for studying various diseases due to its abundance of advanced genetic manipulation techniques and ...

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

The analysis of neuronal network function requires a reliable measurement of behavioral traits. Since the behavior of freely moving animals is variable to a certain degree, many animals have to be analyzed, to obtain statistically significant data. This in turn requires a computer assisted automated quantification of locomotion patterns. To obtain high contrast images of almost translucent and small moving objects, a novel imaging technique based on frustrated total internal reflection called FIM was developed. In this setup, animals are only illuminated with infrared light at the very specific position of contact with the underlying crawling surface. This methodology results in very high contrast images. Subsequently, these high contrast images are processed using established contour tracking algorithms. Based on this, we developed the FIMTrack software, which serves to extract a number of features needed to quantitatively describe a large variety of locomotion characteristics. During the development of this software package, we focused our efforts on an open source architecture allowing the easy addition of further modules. The program operates platform independent and is accompanied by an intuitive GUI guiding the user through data analysis. All locomotion parameter values are given in form of csv files allowing further data analyses. In addition, a Results Viewer integrated into the tracking software provides the opportunity to interactively review and adjust the output, as might be needed during stimulus integration. The power of FIM and FIMTrack is demonstrated by studying the locomotion of Drosophila larvae.

FIM is a novel, cost effective imaging system designed to track small moving objects such as C. elegans, planaria or Drosophila larvae. The accompanying FIMTrack program is designed to deliver fast and efficient data analysis. Together, these tools allow high-throughput analysis of behavioral traits.

FIM成像和FIMtrack:两个新的工具允许高吞吐量和成本效益分析步态

The analysis of neuronal network function requires a reliable measurement of behavioral traits. Since the behavior of freely moving animals is variable to ...

神经科学

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

Fine motor skills are essential in everyday life and can be compromised in several nervous system disorders. The acquisition and performance of these tasks require sensory-motor integration and involve precise control of bilateral brain circuits. Implementing unimanual behavioral paradigms in animal models will improve the understanding of the contribution of brain structures, like the striatum, to complex motor behavior as it allows manipulation and recording of neural activity of specific nuclei in control conditions and disease during the performance of the task. 
Since its creation, optogenetics has been a dominant tool for interrogating the brain by enabling selective and targeted activation or inhibition of neuronal populations. The combination of optogenetics with behavioral assays sheds light on the underlying mechanisms of specific brain functions. Wireless head-mounted systems with miniaturized light-emitting diodes (LEDs) allow remote optogenetic control in an entirely free-moving animal. This avoids the limitations of a wired system being less restrictive for animals' behavior without compromising light emission efficiency. The current protocol combines a wireless optogenetics approach with high-speed videography in a unimanual dexterity task to dissect the contribution of specific neuronal populations to fine motor behavior.

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

The present protocol describes how to use wireless optogenetics combined with high-speed videography in a single pellet reach-to-grasp task to characterize the neural circuits involved in the performance of skilled motor behavior in freely moving mice.

体内 熟练运动行为的无线光遗传学控制

Fine motor skills are essential in everyday life and can be compromised in several nervous system disorders. The acquisition and performance of these ...

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

用于监测小脑依赖性感觉联想学习的灵活平台

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in ...

一种新的单动物运动功能跟踪系统, 使用简单、易于使用的软件

摘要

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此视频中的章节

鼠标短期和长期活动性分析视频跟踪软件

隐式电机序列学习中的 "马达": 步进串行反应时间任务

3D运动性步态分析,用于啮齿动物的临床前研究

自动大鼠单丸到达与3维重建的爪子和数字轨迹

跟踪小啮齿动物社会偏好行为动态的系统

使用多功能自制摄像机和 DeepLabCut 跟踪操作调节室中的大鼠

一种测定果蝇运动活性的简单技术

FIM成像和FIMtrack:两个新的工具允许高吞吐量和成本效益分析步态

体内熟练运动行为的无线光遗传学控制

用于监测小脑依赖性感觉联想学习的灵活平台

一种新的单动物运动功能跟踪系统, 使用简单、易于使用的软件

摘要

探索更多视频

此视频中的章节

鼠标短期和长期活动性分析视频跟踪软件

隐式电机序列学习中的 "马达": 步进串行反应时间任务

3D运动性步态分析,用于啮齿动物的临床前研究

自动大鼠单丸到达与3维重建的爪子和数字轨迹

跟踪小啮齿动物社会偏好行为动态的系统

使用多功能自制摄像机和 DeepLabCut 跟踪操作调节室中的大鼠

一种测定果蝇运动活性的简单技术

FIM成像和FIMtrack:两个新的工具允许高吞吐量和成本效益分析步态

体内 熟练运动行为的无线光遗传学控制

用于监测小脑依赖性感觉联想学习的灵活平台

体内熟练运动行为的无线光遗传学控制