适用于高尔基染色和荧光标记鼠脑的跨脑区神经追踪方法研究
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摘要
大脑如此复杂的功能，从根本上依赖于数以亿计的神经元相互连接构成的神经网络。一般地，特定的脑功能依赖于特定的神经环路。神经环路包括局部连接和长程连接两种形式。电子显微镜或传统光学成像技术只能获取小范围内精细（纳米或亚微米级）的局部神经连接，而核磁共振成像等技术只能获取粗糙（百微米级）的长程神经连接。为获得对脑结构的整体认识，有必要在大尺度范围（厘米级）获取精细（亚微米级）的神经网络连接，而这一直是科学界的一大难题。直到近年来，一种显微光学切片断层成像技术首次在国际上取得了较大突破，先后报道了目前为止最为精细的高尔基染色和荧光标记小鼠全脑高分辨数据集，这为人们进行小鼠脑内跨脑区的精细神经环路研究，提供了重要的基础数据。然而，原始数据并不能直接为神经科学家所用，只有基于神经追踪技术从数据中提取出神经元网络结构，才能通过定量分析，深入揭示脑结构和功能的联系。
近几十年来，神经追踪技术得到了长足发展，但大多数方法仅适用于特定的神经元图像而不具备通用性。上述高分辨小鼠全脑数据集有其显著的独特性，主要体现在高分辨全脑数据的海量性、高尔基染色数据中的空心胞体与密集突起、荧光数据中的长程神经元投射可延伸数毫米距离等。这些前所未有的问题，使得针对本文数据开发准确、高效的神经追踪算法变得十分重要。
本文针对显微光学切片断层成像系统获取的小鼠全脑高分辨数据集，建立了有效的神经追踪方法，并结合生物学问题对追踪结果进行了定量分析。
（1）局部神经追踪。对于高尔基染色数据集，本文通过改进2.5维（2.5D）形态学方法实现胞体定位；提出一种基于梯度判据的射线爆发算法，用于实现空心胞体不规则表面的形态检测；通过改进OpenSnake追踪算法的三维（3D）交互功能提高突起追踪准确性、基于神经元的物理连接关系优化追踪结果，并与上述胞体检测算法相集成，实现了针对高尔基染色数据的准确、半自动神经追踪方法。实验结果表明，胞体定位准确率超过93%；对于89%的定位准确的胞体，其表面检测准确率超过84%；突起全自动追踪准确率超过86%，中心线平均误差低至0.5μm。
（2）长程神经追踪。对于荧光标记小鼠全脑数据集，通过将Amira软件提供的大数据处理模块与纤维追踪模块巧妙结合，首次提出了针对荧光标记数据中长程神经投射进行高效追踪的解决方案。对于单个数毫米距离的投射神经元追踪，相比传统、低效（数天）的分块追踪再拼接的思路，本文仅需耗时2到3小时。
（3）不同脑区神经元形态的定量对比。基于上述局部神经追踪方法，追踪了皮层不同层的锥体神经元。对这些神经元的胞体表面积与体积、突起表面积变化规律等形态参数进行了初步定量比较，同时基于sholl分析计算了突起密度分布，结果表明皮层第2/3层、4层、5层的锥体神经元在上述形态参数方面具有一定的差异性，这种差异性有可能为神经元分类研究提供指导。
（4）长程神经投射通路在核团水平的定位。本文基于稀疏标记的14天荧光鼠脑数据集进行长程神经追踪。由于至今尚无14天小鼠脑图谱，本文借助磁共振成像获取的14天与成年参考鼠脑数据集，将所有追踪的长程投射神经元分段配准到成年小鼠立体定位图谱，最终实现长程神经投射在核团水平的定位。部分定位结果不仅验证了已有的文献报道结果，如体感皮层与丘脑之间经典的双向投射；也发现了一些潜在的神经投射通路，如从DpG到RtTg的投射，这些新发现将为跨脑区信号处理通路的功能研究提供参考依据。
The complex functions of brain fundamentally depend on the neural networkconstructed by hundreds of millions of neurons. Generally, specific brain function dependson specific neural circuit, which contains local connections and long-distance connections.Electronic and traditional light microscopy can only acquire small, high-resolution localconnnections. Magnetic Resonance Imaging can only acquire coarse, long-distanceconnections. In order to get a overall understanding of brain structure, it’s necessary to getlarge-scale and precise neural network information, which has always been a majorchallenge in neuroscience community. Until recently, a new imaging technique calledMicro-Optical Sectioning Tomograpy made great breakthrough for the first time, reportingthe first high-resolution datasets of Golgi-staining and fluorescence-labeled whole mousebrain. These basic datsets make it possible for people to do research on precise neuralcircuit in whole brain. However, these datasets cann’t be used by neuroscientists directly.Quantitative anaysis cann't be used to reveal the relation between structure and functionuntil neural network structures are extracted from these datasets by neuron tracing.
In the last decades, considerable progress has been made on neuron tracing technique.But most of them could be suitable only for their specific datset without generalapplication. The datasets above show their unique characteristics, such as large amount ofdataset, the hollow somas and dense dendrites in Golgi dataset, and the long-distanceneuron projection in fluorescence dataset. These unprecedented challenges make it urgentto develop accurate and efficient neuron tracing method for these new datasets.
In this thesis, we establish effective neuron tracing method for our dataset, and dosome quantitative analysis on the tracing result to address some biological problems.
(1) Local neuron tracing. For Golgi dataset, an enhanced2.5D morphological method isprovided for soma location; a new gradient-based Rayburst algorithm is presented forthe morphological detection of hollow soma’s irregular surface; via improving the3Dinteractive performance of OpenSnake tracing algorithm, and optimising tracingresults based on neuronal connection, a accurate and semi-automatic method for neuron tracing from Golgi datasets is proposed by integrating our soma detectionalgorithm. The accuracy for soma localization and soma surface detection is greaterthan93%and84%, respectively. For neurite tracing, the accuracy of automatic tracingis more than86%, and the average deviation of traced centerline is about0.5μm.
(2) Long-distance neuron tracing. For fluorescent mouse brain dataset, we firstly providesthe efficient solution based on Amira software to long-distance neuron tracing.Compared with traditional and inefficient (several days) block tracing and stitching,our method only consumes2to3hours to trace a single neuron projection.
(3) Quantitative analysis on the morphologies of neuronal in different brain regions. Basedon our local neuron tracing method, several pyrimidal neurons in different cortexlayers were traced and some morphological features such as soma surfce area, volumeand distribution of neurite surface are analyzed. The results show these neurons areembedded with certain differences on these morphological features. These differencesmight have laid a great foundation for the research on neuron classification.
(4) Location of long-distance neuron projection at nuclei level. The dataset of sparselyfluorescence-labeled mouse brain (Postnatal day14, P14) was used for long-distanceneuron tracing. Since no brain atlas for P14mouse is available now, the P14and adultmouse brain datasets from MRI imaging are chosen as reference for registering alltraced long-distance neurons projection to The Mouse Brain in StereotaxicCoordinates atlas regionally, thus realizing the location of long-distance neuronprojection at nuclei level. The results not only confirm some previous report, such asthe classic bidirectional projection in the somatosensory pathway, but also highlightsome unreported but putative projection pathways, such as the projection from DpG toRtTg. these new findings will provide reference for the research on brain-wideinformation processing functions.
近几十年来，神经追踪技术得到了长足发展，但大多数方法仅适用于特定的神经元图像而不具备通用性。上述高分辨小鼠全脑数据集有其显著的独特性，主要体现在高分辨全脑数据的海量性、高尔基染色数据中的空心胞体与密集突起、荧光数据中的长程神经元投射可延伸数毫米距离等。这些前所未有的问题，使得针对本文数据开发准确、高效的神经追踪算法变得十分重要。
本文针对显微光学切片断层成像系统获取的小鼠全脑高分辨数据集，建立了有效的神经追踪方法，并结合生物学问题对追踪结果进行了定量分析。
（1）局部神经追踪。对于高尔基染色数据集，本文通过改进2.5维（2.5D）形态学方法实现胞体定位；提出一种基于梯度判据的射线爆发算法，用于实现空心胞体不规则表面的形态检测；通过改进OpenSnake追踪算法的三维（3D）交互功能提高突起追踪准确性、基于神经元的物理连接关系优化追踪结果，并与上述胞体检测算法相集成，实现了针对高尔基染色数据的准确、半自动神经追踪方法。实验结果表明，胞体定位准确率超过93%；对于89%的定位准确的胞体，其表面检测准确率超过84%；突起全自动追踪准确率超过86%，中心线平均误差低至0.5μm。
（2）长程神经追踪。对于荧光标记小鼠全脑数据集，通过将Amira软件提供的大数据处理模块与纤维追踪模块巧妙结合，首次提出了针对荧光标记数据中长程神经投射进行高效追踪的解决方案。对于单个数毫米距离的投射神经元追踪，相比传统、低效（数天）的分块追踪再拼接的思路，本文仅需耗时2到3小时。
（3）不同脑区神经元形态的定量对比。基于上述局部神经追踪方法，追踪了皮层不同层的锥体神经元。对这些神经元的胞体表面积与体积、突起表面积变化规律等形态参数进行了初步定量比较，同时基于sholl分析计算了突起密度分布，结果表明皮层第2/3层、4层、5层的锥体神经元在上述形态参数方面具有一定的差异性，这种差异性有可能为神经元分类研究提供指导。
（4）长程神经投射通路在核团水平的定位。本文基于稀疏标记的14天荧光鼠脑数据集进行长程神经追踪。由于至今尚无14天小鼠脑图谱，本文借助磁共振成像获取的14天与成年参考鼠脑数据集，将所有追踪的长程投射神经元分段配准到成年小鼠立体定位图谱，最终实现长程神经投射在核团水平的定位。部分定位结果不仅验证了已有的文献报道结果，如体感皮层与丘脑之间经典的双向投射；也发现了一些潜在的神经投射通路，如从DpG到RtTg的投射，这些新发现将为跨脑区信号处理通路的功能研究提供参考依据。
The complex functions of brain fundamentally depend on the neural networkconstructed by hundreds of millions of neurons. Generally, specific brain function dependson specific neural circuit, which contains local connections and long-distance connections.Electronic and traditional light microscopy can only acquire small, high-resolution localconnnections. Magnetic Resonance Imaging can only acquire coarse, long-distanceconnections. In order to get a overall understanding of brain structure, it’s necessary to getlarge-scale and precise neural network information, which has always been a majorchallenge in neuroscience community. Until recently, a new imaging technique calledMicro-Optical Sectioning Tomograpy made great breakthrough for the first time, reportingthe first high-resolution datasets of Golgi-staining and fluorescence-labeled whole mousebrain. These basic datsets make it possible for people to do research on precise neuralcircuit in whole brain. However, these datasets cann’t be used by neuroscientists directly.Quantitative anaysis cann't be used to reveal the relation between structure and functionuntil neural network structures are extracted from these datasets by neuron tracing.
In the last decades, considerable progress has been made on neuron tracing technique.But most of them could be suitable only for their specific datset without generalapplication. The datasets above show their unique characteristics, such as large amount ofdataset, the hollow somas and dense dendrites in Golgi dataset, and the long-distanceneuron projection in fluorescence dataset. These unprecedented challenges make it urgentto develop accurate and efficient neuron tracing method for these new datasets.
In this thesis, we establish effective neuron tracing method for our dataset, and dosome quantitative analysis on the tracing result to address some biological problems.
(1) Local neuron tracing. For Golgi dataset, an enhanced2.5D morphological method isprovided for soma location; a new gradient-based Rayburst algorithm is presented forthe morphological detection of hollow soma’s irregular surface; via improving the3Dinteractive performance of OpenSnake tracing algorithm, and optimising tracingresults based on neuronal connection, a accurate and semi-automatic method for neuron tracing from Golgi datasets is proposed by integrating our soma detectionalgorithm. The accuracy for soma localization and soma surface detection is greaterthan93%and84%, respectively. For neurite tracing, the accuracy of automatic tracingis more than86%, and the average deviation of traced centerline is about0.5μm.
(2) Long-distance neuron tracing. For fluorescent mouse brain dataset, we firstly providesthe efficient solution based on Amira software to long-distance neuron tracing.Compared with traditional and inefficient (several days) block tracing and stitching,our method only consumes2to3hours to trace a single neuron projection.
(3) Quantitative analysis on the morphologies of neuronal in different brain regions. Basedon our local neuron tracing method, several pyrimidal neurons in different cortexlayers were traced and some morphological features such as soma surfce area, volumeand distribution of neurite surface are analyzed. The results show these neurons areembedded with certain differences on these morphological features. These differencesmight have laid a great foundation for the research on neuron classification.
(4) Location of long-distance neuron projection at nuclei level. The dataset of sparselyfluorescence-labeled mouse brain (Postnatal day14, P14) was used for long-distanceneuron tracing. Since no brain atlas for P14mouse is available now, the P14and adultmouse brain datasets from MRI imaging are chosen as reference for registering alltraced long-distance neurons projection to The Mouse Brain in StereotaxicCoordinates atlas regionally, thus realizing the location of long-distance neuronprojection at nuclei level. The results not only confirm some previous report, such asthe classic bidirectional projection in the somatosensory pathway, but also highlightsome unreported but putative projection pathways, such as the projection from DpG toRtTg. these new findings will provide reference for the research on brain-wideinformation processing functions.
引文
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