Quantification of early learning and movement sub-structure predictive of motor performance.
Quantification of early learning and movement sub-structure predictive of motor performance.
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DOI：
10.1038/s41598-021-93944-9
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发表时间：
2021-07-13
影响因子：
4.6
通讯作者：
Pascual JM
中科院分区：
文献类型：
--
作者：
Jakkamsetti V;Scudder W;Kathote G;Ma Q;Angulo G;Dobariya A;Rosenberg RN;Beutler B;Pascual JM
从加速旋转杆上跌落的时间被广泛地用来评估啮齿动物的运动性能。我们推断，这一简单的结果可以通过任务中明确的额外措施(无论多么不明显)来改进，以检查我们所称的运动子结构。我们的目标是比使用跌倒时间更有力地描述正常变异或运动障碍。我们还假设，在子结构的早期测量(或特征)可以预测经历一系列试验的小鼠预期的学习。使用正常的未经治疗和巴氯芬治疗的运动受损的小鼠，我们定义了这些特征，并在三个连续的试验中使用爪子视频跟踪来自动分析它们，包括爪子位置、速度、加速度、方差和近似熵。光谱弧长产生的速度和加速度均匀度。我们发现，在正常小鼠中，在三个试验中，爪子移动的平稳性与轮杆跌倒时间呈负相关。垂直运动的近似熵越大，水平运动的变化相反，第一次尝试摔倒的时间就越长。第一次试验的水平近似熵在最初的几秒钟内预测了随后的跌倒时间。这允许在只进行一次转棒试验后，将不同体重的未经处理的小鼠组分开，并检测到巴氯芬治疗后其他方面未受影响的小鼠，其倒下时间与对照组相似。一个机器学习支持向量机分类器证实了这些发现。总而言之，从转杆上掉下来的时间与几个指标有很好的相关性，包括在试验的头几秒钟内获得的一些指标，以及在前两个试验中对学习做出反应的一些指标，允许在学习完成之前进行预测或先发制人的实验操作。
Time-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our goal was to characterize normal variation or motor impairment more robustly than by using time-to-fall. We also hypothesized that measures (or features) early in the sub-structure could anticipate the learning expected of a mouse undergoing serial trials. Using normal untreated and baclofen-treated movement-impaired mice, we defined these features and automated their analysis using paw video-tracking in three consecutive trials, including paw location, speed, acceleration, variance and approximate entropy. Spectral arc length yielded speed and acceleration uniformity. We found that, in normal mice, paw movement smoothness inversely correlated with rotarod time-to-fall for the three trials. Greater approximate entropy in vertical movements, and opposite changes in horizontal movements, correlated with greater first-trial time-to-fall. First-trial horizontal approximate entropy in the first few seconds predicted subsequent time-to-fall. This allowed for the separation, after only one rotarod trial, of different-weight, untreated mouse groups, and for the detection of mice otherwise unimpaired after baclofen, which displayed a time-to-fall similar to control. A machine-learning support vector machine classifier corroborated these findings. In conclusion, time-to-fall off a rotarod correlated well with several measures, including some obtained during the first few seconds of a trial, and some responsive to learning over the first two trials, allowing for predictions or preemptive experimental manipulations before learning completion.
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