Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning.
Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning.
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DOI：
10.1371/journal.pone.0191708
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Horiuchi M
中科院分区：
文献类型：
--
作者：
Higaki A;Mogi M;Iwanami J;Min LJ;Bai HY;Shan BS;Kukida M;Kan-No H;Ikeda S;Higaki J;Horiuchi M
Morris水迷宫测试(MWM)是评价啮齿动物空间学习能力的最流行和最成熟的行为学测试之一。传统的培训周期约为5天，但没有明确的证据或指导方针说明适当的持续时间。在许多情况下，从之前的数据及其趋势来看，MWM的最终结果似乎是可以预测的。因此，我们假设，如果能够高精度地预测最终结果，就可以缩短实验周期，减轻测试人员的负担。人工神经网络(ANN)是一种有用的数据集建模方法，它使我们能够获得准确的数学模型。因此，我们构建了一个神经网络系统，根据之前在正常小鼠和血管性痴呆模型小鼠中获得的4天数据来估计MWM的最终结果。10周龄雄性C57B1/6小鼠(野生型，WT)分为双侧颈总动脉狭窄组(WT-BCAS)和假手术组(WT-sham)。术后6周用MWM评定患者的认知功能。WT-BCAS组的平均逃避潜伏期明显长于WT-Sham组。所有数据都被收集起来，作为人工神经网络系统的训练数据和测试数据。我们将多层感知器(MLP)定义为使用开源深度学习框架Chainer的预测模型。经过一定次数的更新后，我们将预测值和实测值与测试数据进行了比较。在WT-SHAM和WT-BCAS中，更新的ANN模型都得到了显著的相关系数。接下来，我们用相同的数据集分析了人类测试员的预测能力。在WT-SHAM和WT-BCAS中，人工测试人员和ANN模型之间的预测精度没有显著差异。综上所述，在血管性痴呆模型中，神经网络深度学习方法可以从4天的数据中预测MWM的最终结果，并具有很高的预测精度。
The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents’ spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on testers reduced. An artificial neural network (ANN) is a useful modeling method for datasets that enables us to obtain an accurate mathematical model. Therefore, we constructed an ANN system to estimate the final outcome in MWM from the previously obtained 4 days of data in both normal mice and vascular dementia model mice. Ten-week-old male C57B1/6 mice (wild type, WT) were subjected to bilateral common carotid artery stenosis (WT-BCAS) or sham-operation (WT-sham). At 6 weeks after surgery, we evaluated their cognitive function with MWM. Mean escape latency was significantly longer in WT-BCAS than in WT-sham. All data were collected and used as training data and test data for the ANN system. We defined a multiple layer perceptron (MLP) as a prediction model using an open source framework for deep learning, Chainer. After a certain number of updates, we compared the predicted values and actual measured values with test data. A significant correlation coefficient was derived form the updated ANN model in both WT-sham and WT-BCAS. Next, we analyzed the predictive capability of human testers with the same datasets. There was no significant difference in the prediction accuracy between human testers and ANN models in both WT-sham and WT-BCAS. In conclusion, deep learning method with ANN could predict the final outcome in MWM from 4 days of data with high predictive accuracy in a vascular dementia model.
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