Quantitative evaluation of 3D mouse behaviors in the open field using markerless
使用无标记技术定量评估开放视野中的 3D 小鼠行为
基本信息
- 批准号:8318070
- 负责人:
- 金额:$ 19.06万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-08-15 至 2015-07-31
- 项目状态:已结题
- 来源:
- 关键词:Advanced DevelopmentAlgorithmsAmyotrophic Lateral SclerosisAnimal DiseasesAnimalsAreaBehaviorBehavioralCharacteristicsColorDataData AnalysesDevelopmentDiseaseEnvironmentEvaluationExperimental ModelsForelimbFosteringFunctional disorderGoalsHandHeightHindlimbHumanHuman ResourcesHuntington DiseaseLeadLegLengthLimb structureLocomotionMeasurementMeasuresMethodsMissionMotionMotorMovementMultiple SclerosisMusMuscleMusculoskeletal EquilibriumNeuromuscular DiseasesOutcomeParalysedParkinson DiseasePatternPerformancePhasePositioning AttributePublic HealthQuantitative EvaluationsRattusResearchResearch PersonnelRodentRodent ModelScientistSeveritiesSeverity of illnessSpeedSpinal cord injuryStrokeTechniquesTestingTextureTimeTreatment EffectivenessUnited States National Institutes of HealthVariantWeightbaseburden of illnessclinically relevantcomputerized data processingdisabilityfootinjuredinnovationkinematicsmeetingsmethod developmentmotor deficitmotor impairmentmusculoskeletal injurynerve injuryopen field behaviorreconstructionrelating to nervous systemtherapy development
项目摘要
DESCRIPTION (provided by applicant): Thousands of scientists strive to identify cellular mechanisms that can lead to breakthroughs in the development of ameliorative treatments for debilitating neural and muscular conditions such as spinal cord injury, stroke, Parkinson's disease, Huntingtons disease, ALS and Multiple Sclerosis. Most studies use rodent models to test hypotheses, and these studies are all limited by the methods available to evaluate animal motor function. To continue to make advancements in developing treatments for these debilitating conditions, there is a critical need for more sensitive, repeatable, and time efficient methods for quantifying functional movements in an open field environment. The goal of this project is to fill this critical need by developing a method to automatically quantify and classify gross 3D movements and stepping kinematics in the open field for multiple animals simultaneously, without placing markers on the animals or requiring hand digitization. State of the art 3D volumetric reconstruction techniques will be used to measure and classify characteristic movements of uninjured and injured mice, and anatomical landmark tracking algorithms will quantify stepping coordination. Open field evaluations of motor performance allow a wide variety of behaviors to be analyzed and testing of multiple animals simultaneously, which will significantly reduce the time needed for evaluation. The markerless measurement and motor function evaluation technique will be developed and tested by classifying spinal cord injury (SCI) severity in mice. These animals display extremely challenging motor dysfunction that ranges from complete paralysis to normal locomotion. Assessment methods which are effective for murine SCI would likely generalize to larger rodents like rats and to other disease states where motor deficits are less severe. To make the method accessible to researchers, the number of cameras, data capture rates and trial lengths needed to accurately quantify and classify open field behavior will be minimized. This proposal will develop, refine and optimize this markerless approach for evaluating motor function by accomplishing the following specific aims: (1) Automatically identify clinically relevant 3D movements in the open field which reflect motor impairments of different severities using center of volume measurements and can be measured for multiple animals simultaneously. (2) Relate disease severity to patterns of fore- and hindlimb coordination in the open field. This approach is innovative because it will apply markerless motion tracking algorithms, which have been primarily developed for humans, to questions related to mouse motor function. This will give researchers access to quantitative measures of animal motion that have never been possible before. This approach can produce significant and fundamental changes in behavioral movement assessments since it will combine all of the most powerful features of current state-of-the art motor function assessments. The result would be an automated method that produces accurate, sensitive, repeatable measurements of open-field movements for use in quantitative evaluations of motor performance.
描述(由申请人提供):数千名科学家致力于确定细胞机制,这些机制可以导致在开发用于使人衰弱的神经和肌肉疾病(如脊髓损伤、中风、帕金森病、亨廷顿病、ALS和多发性硬化症)的改善治疗方面取得突破。大多数研究使用啮齿动物模型来检验假设,这些研究都受到评估动物运动功能的方法的限制。为了继续在开发用于这些衰弱病症的治疗方面取得进展,迫切需要更灵敏、可重复和时间有效的方法来量化开放场地环境中的功能运动。该项目的目标是通过开发一种方法来满足这一关键需求,该方法可以自动量化和分类多个动物同时在开放领域中的总3D运动和步进运动学,而无需在动物上放置标记或需要手动数字化。最先进的3D体积重建技术将用于测量和分类未受伤和受伤小鼠的特征运动,解剖标志跟踪算法将量化步进协调。运动性能的开放领域评估允许同时分析和测试多种动物的各种行为,这将显著减少评估所需的时间。将通过对小鼠脊髓损伤(SCI)严重程度进行分类来开发和测试无标记测量和运动功能评估技术。这些动物表现出极具挑战性的运动功能障碍,范围从完全瘫痪到正常运动。对小鼠SCI有效的评估方法可能会推广到更大的啮齿动物,如大鼠和其他运动缺陷不太严重的疾病状态。为了使研究人员能够使用该方法,将最大限度地减少准确量化和分类旷场行为所需的相机数量、数据捕获率和试验长度。该提案将通过实现以下具体目标来开发、改进和优化这种用于评估运动功能的无标记方法:(1)使用体积测量中心自动识别开放场中的临床相关3D运动,其反映不同严重程度的运动损伤,并且可以同时测量多个动物。(2)将疾病的严重程度与开放场地中的前肢和后肢协调模式联系起来。这种方法是创新的,因为它将主要为人类开发的无标记运动跟踪算法应用于与鼠标运动功能相关的问题。这将使研究人员能够获得以前从未有过的动物运动的定量测量。这种方法可以在行为运动评估中产生显著和根本的变化,因为它将联合收割机结合当前最先进的运动功能评估的所有最强大的特征。其结果将是一种自动化的方法,产生准确,灵敏,可重复的测量开放领域的运动,用于定量评估电机性能。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
D M Basso其他文献
D M Basso的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('D M Basso', 18)}}的其他基金
Eccentric Motor-Control Training to Improve Human SCI
偏心运动控制训练可改善人类 SCI
- 批准号:
8809921 - 财政年份:2014
- 资助金额:
$ 19.06万 - 项目类别:
Peripheral Trafficking in Locomotor Networks After Thoracic SCI
胸部 SCI 后运动网络的外围贩运
- 批准号:
8925937 - 财政年份:2014
- 资助金额:
$ 19.06万 - 项目类别:
Peripheral Trafficking in Locomotor Networks After Thoracic SCI
胸部 SCI 后运动网络的外围贩运
- 批准号:
8808967 - 财政年份:2014
- 资助金额:
$ 19.06万 - 项目类别:
Quantitative evaluation of 3D mouse behaviors in the open field using markerless
使用无标记技术定量评估开放视野中的 3D 小鼠行为
- 批准号:
8229581 - 财政年份:2011
- 资助金额:
$ 19.06万 - 项目类别:
Behavioral and Cellular Determinants of Treadmill Training and Recovery after SCI
跑步机训练和脊髓损伤后恢复的行为和细胞决定因素
- 批准号:
8502766 - 财政年份:2011
- 资助金额:
$ 19.06万 - 项目类别:
Behavioral and Cellular Determinants of Treadmill Training and Recovery after SCI
跑步机训练和脊髓损伤后恢复的行为和细胞决定因素
- 批准号:
8699853 - 财政年份:2011
- 资助金额:
$ 19.06万 - 项目类别:
Behavioral and Cellular Determinants of Treadmill Training and Recovery after SCI
跑步机训练和脊髓损伤后恢复的行为和细胞决定因素
- 批准号:
8258604 - 财政年份:2011
- 资助金额:
$ 19.06万 - 项目类别:
Behavioral and Cellular Determinants of Treadmill Training and Recovery after SCI
跑步机训练和脊髓损伤后恢复的行为和细胞决定因素
- 批准号:
8321987 - 财政年份:2011
- 资助金额:
$ 19.06万 - 项目类别:
Behavioral and cellular determinants of treadmill training and recovery after SCI
SCI 后跑步机训练和恢复的行为和细胞决定因素
- 批准号:
9340300 - 财政年份:2011
- 资助金额:
$ 19.06万 - 项目类别:
Behavioral and cellular determinants of treadmill training and recovery after SCI
SCI 后跑步机训练和恢复的行为和细胞决定因素
- 批准号:
9763664 - 财政年份:2011
- 资助金额:
$ 19.06万 - 项目类别:
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 19.06万 - 项目类别:
Continuing Grant