Automated synaptic connectivity inference for volume electron microscopy
Automated synaptic connectivity inference for volume electron microscopy
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DOI：
10.1038/nmeth.4206
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发表时间：
2017-04-01
期刊：
影响因子：
48
通讯作者：
Kornfeld, Joergen
中科院分区：
文献类型：
--
作者：
Dorkenwald, Sven;Schubert, Philipp J.;Kornfeld, Joergen
Teravoxel神经组织的体积电子显微镜数据集现在可以在几周内获得，但数据分析需要数年的手工劳动。我们开发了SyConn框架，该框架使用深度卷积神经网络和随机森林分类器，通过自动识别线粒体、突触及其类型、轴突、树突、棘、髓鞘、胞体和细胞类型，从手动神经突骨架重建中推断出注释丰富的突触连接矩阵。我们测试了我们的方法从斑马鱼，小鼠和斑胸草雀连续块面电子显微镜数据集，并计算了鸣禽基底神经节的突触布线。例如，我们发现，在体内具有高放电率的基底神经节细胞类型具有较高的线粒体和囊泡密度，并且突触的大小和数量系统地缩放，这取决于受神经支配的突触后细胞类型。
Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.