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通过微型CT成像和深度学习揭示了蜜蜂大脑大小和对称性的自然变化

Philipp D Lösel^1,2,3, Coline Monchanin^4,5, Renaud Lebrun^6,7

¹Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.

PLoS computational biology

|October 2, 2023

概括

使用微型CT成像和深度学习对蜜蜂大脑的自动化分析揭示了大脑大小和侧向化的显著差异. 这种快速,用户友好的方法可以进行大规模的神经解剖学比较,以获得进化见解.

科学领域:

神经科学是一个神经科学.
进化生物学进化生物学
动物行为动物行为

背景情况:

比较神经解剖学对于理解动物行为,生态和进化至关重要.
手动分析大脑形态是耗时的,限制了大规模研究.
需要自动化方法来分析大脑形态学的大数据集.

研究的目的:

开发和验证一种快速的,自动化的方法,使用微型CT成像和深度学习分析3D大脑形态.
为了研究蜜蜂和大蜂的大脑大小和横向化的个体间变化.
探索这种方法对大规模比较神经解剖学的潜力.

主要方法:

微计算机断层扫描 (micro-CT) 图像被用来从187个蜜蜂和大蜂大脑中获取3D数据.
用深度学习算法对3D图像数据进行自动分析.
进行了统计分析,以确定大脑大小和叶片横向化的变化.

主要成果:

观察到大脑总大小的强烈个体间差异,在殖民地和物种之间一致.
在熊蜂的光学和天线叶中检测到显著的横向化.
自动化方法被证明是快速的,强大的,和用户友好的.

结论:

自动的神经解剖学分析可以揭示大脑形态学的生物学相关变异.
观察到的大脑变异可能解释社会昆虫的行为差异.
这种方法促进了大规模的比较研究,促进了我们对大脑进化和认知的理解.

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