Park Changjoo, Gim Jawon, Lee Sungjin, Lee Kea Joo, Kim Jinseop S
Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea.
Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea.
Front Neuroanat. 2022 Mar 11;16:760279. doi: 10.3389/fnana.2022.760279. eCollection 2022.
The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.
对大规模体积电子显微镜（EM）图像进行连接组学分析，有助于发现隐藏的神经连接。虽然EM图像的神经元重建技术正在迅速发展，但突触检测技术却滞后了。在此，我们提出一种方法，可自动检测三维EM图像中的突触，特别是针对小鼠小脑分子层（CML）。该方法旨在准确检测可识别类型的重建神经元片段之间的突触。它提取重建神经元片段之间的接触，并借助类型信息和两种深度学习人工智能（AI）将其分类为突触或非突触。该方法还可以确定突触的突触前和突触后侧，并确定兴奋性和抑制性突触类型。对于包含508个突触的CML测试体积，该方法的F1分数估计为0.955。为了证明其可用性，我们测量了该体积中突触的大小和数量，并研究了CML神经元片段之间的亚细胞连接。利用组织特异性特性的该方法的基本思想可以扩展到其他脑区。
