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首页> 外文期刊>Journal of Neuroscience Methods >Automated discrimination of psychotropic drugs in mice via computer vision-based analysis.
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Automated discrimination of psychotropic drugs in mice via computer vision-based analysis.

机译：通过基于计算机视觉的分析自动区分小鼠中的精神药物。

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We developed an inexpensive computer vision-based method utilizing an algorithm which differentiates drug-induced behavioral alterations. The mice were observed in an open-field arena and their activity was recorded for 100 min. For each animal the first 50 min of observation were regarded as the drug-free period. Each animal was exposed to only one drug and they were injected (i.p.) with either amphetamine or cocaine as the stimulant drugs or morphine or diazepam as the inhibitory agents. The software divided the arena into virtual grids and calculated the number of visits (sojourn counts) to the grids and instantaneous speeds within these grids by analyzing video data. These spatial distributions of sojourn counts and instantaneous speeds were used to construct feature vectors which were fed to the classifier algorithms for the final step of matching the animals and the drugs. The software decided which of the animals were drug-treated at a rate of 96%. The algorithm achieved 92% accuracy in sorting the data according to the increased or decreased activity and then determined which drug was delivered. The method differentiated the type of psychostimulant or inhibitory drugs with a success ratio of 70% and 80%, respectively. This method provides a new way to automatically evaluate and classify drug-induced behaviors in mice.
机译：我们开发了一种廉价的基于计算机视觉的方法，利用一种算法来区分药物引起的行为改变。在露天场所观察小鼠，并记录其活动100分钟。对于每只动物，观察的前50分钟被视为无药期。每只动物仅暴露于一种药物，并向它们注射（i.p.）苯丙胺或可卡因作为刺激药物，或吗啡或地西epa作为抑制剂。该软件将竞技场划分为虚拟网格，并通过分析视频数据来计算对网格的访问次数（逗留次数）以及这些网格内的瞬时速度。这些定居数和瞬时速度的空间分布用于构建特征向量，将其输入到分类器算法中，以进行动物和药物的匹配。该软件确定对哪些动物进行了96％的药物处理。该算法根据增加或减少的活性对数据进行排序，然后确定要递送的药物，达到92％的准确性。该方法区分了精神刺激药或抑制药的类型，成功率分别为70％和80％。该方法提供了一种自动评估小鼠中药物诱导行为的新方法。

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