Optimization of Traced Neuron Skeleton Using Lasso-Based Model

The crucial role that hippocampus Cyclooxygenase-2 plays in memory

It is generally accepted that Cyclooxygenase-2 (COX-2) is activated to cause inflammation. However, COX-2 is also constitutively expressed at the postsynaptic dendrites and excitatory terminals of the cortical and spinal cord neurons. Although some evidence suggests that COX-2 release during neuronal signalling may be pivotal for regulating the function of memory, the significance of constitutively expressed COX-2 in neuron is still unclear. This research aims to discover the role of COX-2 in memory beyond neuroinflammation and to determine whether the inhibition of COX-2 can cause cognitive dysfunction by influencing dendritic plasticity and its underlying mechanism. We found COX-2 gene knockout (KO) could significantly impact the learning and memory ability, cause neuronal structure disorder and influence gamma oscillations. These might be mediated by the inhibition of prostaglandin (PG) E2/cAMP pathway and phosphorylated protein kinase A (p-PKA)-phosphorylated cAMP response element binding protein (p-CREB)-brain derived neurotrophic factor (BDNF) axis. It suggested COX-2 might play a critical role in learning, regulating neuronal structure and gamma oscillations in the hippocampus CA1 by regulating COX-2/BDNF signalling pathway.

A robust approach to 3D neuron shape representation for quantification and classification

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing "curve" skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

3D Neuron Morphology Analysis

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing"curve"skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

NRRS: a re-tracing strategy to refine neuron reconstruction

It is crucial to develop accurate and reliable algorithms for fine reconstruction of neural morphology from whole-brain image datasets. Even though the involvement of human experts in the reconstruction process can help to ensure the quality and accuracy of the reconstructions, automated refinement algorithms are necessary to handle substantial deviations problems of reconstructed branches and bifurcation points from the large-scale and high-dimensional nature of the image data. Our proposed Neuron Reconstruction Refinement Strategy (NRRS) is a novel approach to address the problem of deviation errors in neuron morphology reconstruction. Our method partitions the reconstruction into fixed-size segments and resolves the deviation problems by re-tracing in two steps. We also validate the performance of our method using a synthetic dataset. Our results show that NRRS outperforms existing solutions and can handle most deviation errors. We apply our method to SEU-ALLEN/BICCN dataset containing 1741 complete neuron reconstructions and achieve remarkable improvements in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. Our findings demonstrate the critical role of NRRS in refining neuron morphology reconstruction.

Availability and implementation

The proposed refinement method is implemented as a Vaa3D plugin and the source code are available under the repository of vaa3d_tools/hackathon/Levy/refinement. The original fMOST images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). The synthetic dataset is hosted on GitHub (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/Levy/refinement).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Tracing weak neuron fibers

MOTIVATION

Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons.

RESULTS

We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types.

AVAILABILITY AND IMPLEMENTATION

Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images

We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functions that aid experts to effectively and precisely annotate micrometer resolution neurites. It also offers an automatic neurites segmentation using convolutional neuronal networks (CNN) trained by the Toolbox annotations and somas segmentation using thresholding. To visualize neurites in a given volume, NeuroConstruct offers a hybrid rendering by combining iso-surface rendering of high-confidence classified neurites, along with real-time rendering of raw volume using a 2D transfer function for voxel classification score versus voxel intensity value. For a complete reconstruction of the 3D neurites, we introduce a Registration Toolbox that provides automatic coarse-to-fine alignment of serially sectioned samples. The quantitative and qualitative analysis show that NeuroConstruct outperforms the state-of-the-art in all design aspects. NeuroConstruct was developed as a collaboration between computer scientists and neuroscientists, with an application to the study of cholinergic neurons, which are severely affected in Alzheimer's disease.

Super-resolution Segmentation Network for Reconstruction of Packed Neurites

Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in brain research. However, the difficulty in reconstructing dense neurites, wherein massive labor is required for accurate reconstruction in most cases, has not been well resolved. In this work, we provide a new pathway for solving this challenge by proposing the super-resolution segmentation network (SRSNet), which builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution (HR) space. During the segmentation process, the distances between the boundaries of the packed neurites are enlarged, and only the central parts of the neurites are segmented. Owing to this strategy, the super-resolution segmented images are produced for subsequent reconstruction. We carried out experiments on neuronal images with a voxel size of 0.2 μm × 0.2 μm × 1 μm produced by fMOST. SRSNet achieves an average F1 score of 0.88 for automatic packed neurites reconstruction, which takes both the precision and recall values into account, while the average F1 scores of other state-of-the-art automatic tracing methods are less than 0.70.

A 3D Tubular Flux Model for Centerline Extraction in Neuron Volumetric Images

Digital morphology reconstruction from neuron volumetric images is essential for computational neuroscience. The centerline of the axonal and dendritic tree provides an effective shape representation and serves as a basis for further neuron reconstruction. However, it is still a challenge to directly extract the accurate centerline from the complex neuron structure with poor image quality. In this paper, we propose a neuron centerline extraction method based on a 3D tubular flux model via a two-stage CNN framework. In the first stage, a 3D CNN is used to learn the latent neuron structure features, namely flux features, from neuron images. In the second stage, a light-weight U-Net takes the learned flux features as input to extract the centerline with a spatial weighted average strategy to constrain the multi-voxel width response. Specifically, the labels of flux features in the first stage are generated by the 3D tubular model which calculates the geometric representations of the flux between each voxel in the tubular region and the nearest point on the centerline ground truth. Compared with self-learned features by networks, flux features, as a kind of prior knowledge, explicitly take advantage of the contextual distance and direction distribution information around the centerline, which is beneficial for the precise centerline extraction. Experiments on two challenging datasets demonstrate that the proposed method outperforms other state-of-the-art methods by 18% and 35.1% in F1-measurement and average distance scores at the most, and the extracted centerline is helpful to improve the neuron reconstruction performance.

Conductive Nerve Guidance Conduits Based on Morpho Butterfly Wings for Peripheral Nerve Repair

Peripheral nerve injury (PNI), causing loss of sensory and motor function, is a complex and challenging disease in the clinic due to the restricted regeneration capacity. Nerve guidance conduits (NGCs) have become a promising substitute for peripheral nerve regeneration, but their efficacy is often limited. Here, inspired by the physiological structures of peripheral nerves, we present a conductive topological scaffold for nerve repair by modifying Morpho butterfly wing with reduced graphene oxide (rGO) nanosheets and methacrylated gelatin (GelMA) hydrogel encapsulated brain-derived neurotrophic factor (BDNF). Benefiting from the biocompatibility of GelMA hydrogel, the conductivity of rGO and parallel nanoridge structures of wing scales, PC12 cells, and neural stem cells grown on the modified wing have an increased neurite length with guided cellular orientation. In addition, the NGCs are successfully obtained by manually rolling up the scaffolds and exhibited great performance in repairing 10 mm sciatic nerve defects in rats, and we believe that the NGCs can be applied in reparing longer nerve defects in the future by further optimization. We also demonstrate the feasibility of electrically conductive NGCs based on the rGO/BDNF/GelMA-integrated Morpho butterfly wing as functional nerve regeneration conduits, which may have potential value for application in repairing peripheral nerve injuries.

GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.

3D Free-Standing Ordered Graphene Network Geometrically Regulates Neuronal Growth and Network Formation

The control of cell-microenvironment interactions plays a pivotal role in constructing specific scaffolds for tissue engineering. Here, we fabricated a 3D free-standing ordered graphene (3D-OG) network with a precisely defined pattern. When primary cortical cells are cultured on 3D-OG scaffolds, they form well-defined 3D connections. Astrocytes have a more ramified shape similar to that seen in vivo because of the nanosized ripples and wrinkles on the surface of graphene skeleton. Neurons have axons and dendrites aligned along the graphene skeleton, allowing the formation of neuronal networks with highly controlled connections. Neuronal networks have higher electrical activity with functional signaling over a long distance along the graphene skeleton. Our study, for the first time, investigated the geometrical cues on ordered neuronal growth and network formation with the support of graphene in 3D, which therefore advanced the development of customized scaffolds for brain-machine interfaces or neuroprosthetic devices.