Bolder Maximilian F, Jung Klaus, Stern Michael
Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Hannover, Germany.
Institute of Physiology and Cell Biology, University of Veterinary Medicine Hannover, Hannover, Germany.
R Soc Open Sci. 2022 Feb 9;9(2):210932. doi: 10.1098/rsos.210932. eCollection 2022 Feb.
Hibernation, as an adaptation to seasonal environmental changes in temperate or boreal regions, has profound effects on mammalian brains. Social insects of temperate regions hibernate as well, but despite abundant knowledge on structural and functional plasticity in insect brains, the question of how seasonal activity variations affect insect central nervous systems has not yet been thoroughly addressed. Here, we studied potential variations of serotonin-immunoreactivity in visual information processing centres in the brain of the long-lived ant species . Quantitative immunofluorescence analysis revealed stronger serotonergic signals in the lamina and medulla of the optic lobes of wild or active laboratory workers than in hibernating animals. Instead of statistical inference by testing, differentiability of seasonal serotonin-immunoreactivity was confirmed by a machine learning analysis using convolutional artificial neuronal networks (ANNs) with the digital immunofluorescence images as input information. Machine learning models revealed additional differences in the third visual processing centre, the lobula. We further investigated these results by gradient-weighted class activation mapping. We conclude that seasonal activity variations are represented in the ant brain, and that machine learning by ANNs can contribute to the discovery of such variations.
冬眠作为一种对温带或寒带地区季节性环境变化的适应方式，对哺乳动物的大脑有着深远影响。温带地区的群居昆虫也会冬眠，然而，尽管人们对昆虫大脑的结构和功能可塑性已有丰富了解，但季节性活动变化如何影响昆虫中枢神经系统这一问题尚未得到充分探讨。在此，我们研究了长寿蚂蚁物种大脑视觉信息处理中心中5-羟色胺免疫反应性的潜在变化。定量免疫荧光分析显示，野生或活跃的实验室工蚁视叶的板层和髓质中的5-羟色胺能信号比冬眠动物更强。通过使用卷积人工神经网络（ANN）并以数字免疫荧光图像作为输入信息的机器学习分析，证实了季节性5-羟色胺免疫反应性的可区分性，而不是通过测试进行统计推断。机器学习模型揭示了第三个视觉处理中心小叶中的其他差异。我们通过梯度加权类激活映射进一步研究了这些结果。我们得出结论，季节性活动变化在蚂蚁大脑中有所体现，并且人工神经网络的机器学习有助于发现此类变化。
