|Place of Conferral||中国科学院自动化研究所|
|Keyword||神经突触 连接组 电子显微镜 深度学习 重建|
2. 提出了一种基于 convLSTM 的神经元追踪算法。
3. 面向小鼠耳蜗带状突触，建立了一种基于 3D Detection Network 的突触识别算法，量化分析结果发现了异质性突触功能精细调节的结构性证据。
Mapping synaptic connectome is an important technology of understanding the principle of brain as well as exploring the mechanism of brain diseases. With the rapid development of serial section scanning electron microscopy technology, analyzing the 3D synaptic connections between neurons and mapping the nanoscale brain circuit have become possible. However, due to the distinct image characteristics caused by different brain tissues and imaging methods, it is difficult to achieve satisfactory performances by applying the constant deep learning algorithms.
Based on serial section scanning electron microscopy images, this paper firstly developed automatic synapse, mitochondrion reconstruction algorithms and neuron tracing algorithm based on convolutional neural networks. Next, be geared to the needs of specific scientific problems in mouse auditory system, a set of customized algorithms were designed for images from different brain regions and different imaging methods in order to meet the demand of different reconstruction tasks. Finally, we applied above algorithms and studied the organizations and configurations of ribbon synapses in mouse cochlear and multi-contact synapses in auditory cortex, providing the structural evidences of a fine-tuned synaptic function. The main achievements and contributions of the paper are as follows:
1. Automatic detection algorithms for synapses and mitochondria based on convolutional neural network were proposed.
In order to solve the problem that the existing synapse detection algorithms were not suitable for highly anisotropic data, this paper proposed a novel two-step synapse detection method. Firstly, we utlized Mask R-CNN to obtain the segmentation and detection results of synapses on 2D slices. Then, a 3D connection algorithm was designed based on the continuity information of synapses in adjacent layers, which realized the fast and accurate synapse detection in highly anisotropic data. The experimental results on Syn-SEM dataset demonstrate that the proposed method achieve superior detection performances (Precision: 73%, Recall: 72%) than other methods. To tackle the problem of incomplete detection of long and narrow mitochondria by existing methods, an recursive segmentation subnetwork was added to the two-stage instance segmentation network to obtain more accurate results by iteratively refining the mitochondrial membrane. The segmentation performance (Precision: 94%, Recall: 84%) on Mito-SEM dataset suggest that the porposed method achieve state-of-the-art results. We reconstructed synapses and mitochondria based on above methods in 40 × 40 × 50 um3 volume from mouse cortex, and we made statistical analysis to validate the random spatial distribution of synapses and the fine nanotunneling structure of mitochondria.
2. A neuron tracing algorithm based on convLSTM was proposed.
To tackle the problems of existing neuron tracing algorithm, such as low level of automation and unsuitability for highly anisotropic data, this paper proposed a neuron tracing network by combining 2D CNN and convLSTM, which can explore the intra-slice and inter-slice features simultaneously and avoid the mismatches on the object scale between feature and image spaces. A novel recursive training method was introduced into the training process, which can reduce the impact of error accumulation by simulating the data from real scenes. To reduce the effects of local misalignments in large-scale data, a deep features-based shift estimation and correction module was designed to calibrate linear deformation during neuron tracing process. ARE and VI metrics of this approach exceed existing neuron tracing methods on SEM and CREMI datasets, which demonstrates the effectiveness.
3. A ribbon synapse detection algorithm based on 3D Detection Network was proposed, and structural evidence of a fine-tuned cytoarchitecture for heterogeneous synaptic function in cochlear inner hair cell was investigated.
As the receptor of auditory system, cochlea can convert sound signals into electrical signals. Inner hair cells (IHCs) are responsible for sound perception. However, the structural relationship between ribbon synapses and other organelles in IHCs is still unclear. In order to explore the subcellular structure of IHCs, this paper used the serial block-face scanning electron microscope for imaging and designed a set of automatic IHC ultrastructures detection algorithm based on 3D convolutional neural network. According to the biological features of ribbon synapses, a novel 3D instance segmentation network was proposed to obtain the accurate detection of ribbon synapses, and yielded 92% and 88% detection precision and recall. An automatic reconstruction algorithm for mitochondria was developed based on 3D U-Net and 3D connection algorithm, and yielded 97% segmentation precision and recall. A total of 34 IHCs were reconstructed from electron microscopy volume of two mice. And morphological quantification of staggered arrangement of IHCs was carried out. Cell-wide structural quantification of ribbon synapses and mitochondria suggests significant differences of ribbon morphology gradient and mitochondrial organization between adjacent IHCs in staggered pairs. Correlation analysis argues for a "location-specific" correlation between ribbon synapses and mitochondria.
4. An automatic location and classification method of multi-contact synapses was proposed, and a special pattern of synaptic connections was found to be related with the fear memory.
As the information processing center of auditory system, the change of synaptic connection patterns in auditory cortex provides the basis for the coding and storage of memory. Existing methods demonstrate that the newly formed synapses may coexist with the old synapses to form "one-to-many" or "many-to-one" multi-contact synapses. However, the relationship between the synaptic connection pattern and fear memory is unclear. This paper set up the conditioned groups and the control groups with the auditory fear conditioning as the behavioral paradigm. 2.8 ×105 um3 electron microscopy data from mice of 3 group pairs were yielded by using the serial section electron microscope imaging technology. Upon reconstructing over 160,000 synapses, this paper designed a location and classification method of multi-contact synapses based on the features of a variety of ultrastructures. The statistical analysis shows that fear learning don't change the density of synapses but significantly increases the proportion of "one-to-many" multi-contact synapses. Dense neuron reconstructions show that more than 85% of these synapses are connected to different dendrites. Mathematical modeling results suggest that the synaptic connection pattern of the multi-dendrites greatly improves the information storage capacity, which may represent a new synaptic memory engram.
|刘静. 面向神经突触连接组的深度学习算法研究及应用[D]. 中国科学院自动化研究所. 中国科学院大学,2021.|
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