NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites

High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy

Neuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations of cells can obscure such insights. Here, we report large-scale automation of neuronal morphology reconstruction and analysis on a dataset of 813 inhibitory neurons characterized using the Patch-seq method, which enables measurement of multiple properties from individual neurons, including local morphology and transcriptional signature. We demonstrate that these automated reconstructions can be used in the same manner as manual reconstructions to understand the relationship between some, but not all, cellular properties used to define cell types. We uncover gene expression correlates of laminar innervation on multiple transcriptomically defined neuronal subclasses and types. In particular, our results reveal correlates of the variability in Layer 1 (L1) axonal innervation in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex.

Automated Analysis of Neuronal Morphology in 2D Fluorescence Micrographs through an Unsupervised Semantic Segmentation of Neurons

Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering extensive distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and prone to bias. Most automated analyses of neurons rely on reconstruction of the neuron as a whole without a semantic analysis of each neurite. A fully-automated classification of all neurites still remains unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in two-dimensional fluorescence micrographs of cultured neurons with minimal requirements for user interaction. Single neurons are first reconstructed into binarized images using a Hessian-based segmentation algorithm to detect thin neurite structures combined with intensity- and shape-based reconstruction of the cell body. Neurites are then classified into axon, dendrites and their branches of increasing order using a geodesic distance transform of the cell skeleton. The software was benchmarked against a published dataset and reproduced the phenotype observed after manual annotation. Our tool promises accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.

Gapr for large-scale collaborative single-neuron reconstruction

Whole-brain analysis of single-neuron morphology is crucial for unraveling the complex structure of the brain. However, large-scale neuron reconstruction from terabyte and even petabyte data of mammalian brains generated by state-of-the-art light microscopy is a daunting task. Here, we developed 'Gapr' (Gapr accelerates projectome reconstruction) that streamlines deep learning-based automatic reconstruction, 'automatic proofreading' that reduces human workloads at high-confidence sites, and high-throughput collaborative proofreading by crowd users through the Internet. Furthermore, Gapr offers a seamless user interface that ensures high proofreading speed per annotator, on-demand conversion for handling large datasets, flexible workflows tailored to diverse datasets and rigorous error tracking for quality control. Finally, we demonstrated Gapr's efficacy by reconstructing over 4,000 neurons in mouse brains, revealing the morphological diversity in cortical interneurons and hypothalamic neurons. Here, we present Gapr as a solution for large-scale single-neuron reconstruction projects.

Brain Image Segmentation for Ultrascale Neuron Reconstruction via an Adaptive Dual-Task Learning Network

Accurate morphological reconstruction of neurons in whole brain images is critical for brain science research. However, due to the wide range of whole brain imaging, uneven staining, and optical system fluctuations, there are significant differences in image properties between different regions of the ultrascale brain image, such as dramatically varying voxel intensities and inhomogeneous distribution of background noise, posing an enormous challenge to neuron reconstruction from whole brain images. In this paper, we propose an adaptive dual-task learning network (ADTL-Net) to quickly and accurately extract neuronal structures from ultrascale brain images. Specifically, this framework includes an External Features Classifier (EFC) and a Parameter Adaptive Segmentation Decoder (PASD), which share the same Multi-Scale Feature Encoder (MSFE). MSFE introduces an attention module named Channel Space Fusion Module (CSFM) to extract structure and intensity distribution features of neurons at different scales for addressing the problem of anisotropy in 3D space. Then, EFC is designed to classify these feature maps based on external features, such as foreground intensity distributions and image smoothness, and select specific PASD parameters to decode them of different classes to obtain accurate segmentation results. PASD contains multiple sets of parameters trained by different representative complex signal-to-noise distribution image blocks to handle various images more robustly. Experimental results prove that compared with other advanced segmentation methods for neuron reconstruction, the proposed method achieves state-of-the-art results in the task of neuron reconstruction from ultrascale brain images, with an improvement of about 49% in speed and 12% in F1 score.

Automated neuronal reconstruction with super-multicolour Tetbow labelling and threshold-based clustering of colour hues

Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here, we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we establish stochastic super-multicolour labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron is labelled with a unique combination of fluorescent proteins, which are then imaged and separated by linear unmixing. We also establish an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder), which identifies neurite fragments with similar colour combinations. To classify colour combinations, we develop unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space are clustered based on a given threshold distance. Our strategy allows the reconstruction of neurites for up to hundreds of neurons at the millimetre scale without using their physical continuity.

Integrated platform for multiscale molecular imaging and phenotyping of the human brain

Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multiscale details of individual cells and their connectivity in the human organ-scale system. We developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain. The platform includes three core elements: a vibrating microtome for ultraprecision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and a computational pipeline for reconstructing three-dimensional connectivity across multiple brain slabs (UNSLICE). We applied this platform for analyzing human Alzheimer's disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain.

Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.

Quantifying morphologies of developing neuronal cells using deep learning with imperfect annotations

The functionality of human intelligence relies on the interaction and health of neurons, hence, quantifying neuronal morphologies can be crucial for investigating the functionality of the human brain. This paper proposes a deep learning (DL) based method for segmenting and quantifying neuronal structures in fluorescence microscopy images of developing neuronal cells cultured in vitro. Compared to the majority of supervised DL-based segmentation methods that heavily rely on creating exact corresponding masks of neuronal structures for the preparation of training samples, the proposed approach allows for imperfect annotation of neurons, as it only requires tracing the centrelines of the neurites. This ability accelerates the preparation of training data by several folds. Our proposed framework is built on a modified version of PSPNet with an EfficientNet backbone pre-trained on the CityScapes dataset. To handle the imperfectness of training samples, we incorporated a weighted combination of two loss functions, namely the Dice loss and Lovász loss functions, into our network. We evaluated the proposed framework and several other state-of-the-art methods on a published dataset of approximately 900 manually quantified cultured mouse neurons. Our results indicate a close correlation between the proposed method and manual quantification in terms of neuron length and the number of branches while demonstrating improved analysis speed. Furthermore, the proposed method achieved high accuracy in neuron segmentation, as evidenced by the evaluation of the neurons' length and number of branches.

A modular framework for multi-scale tissue imaging and neuronal segmentation

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries.

Subregion preference in the long-range connectome of pyramidal neurons in the medial prefrontal cortex

BACKGROUND

The medial prefrontal cortex (mPFC) is involved in complex functions containing multiple types of neurons in distinct subregions with preferential roles. The pyramidal neurons had wide-range projections to cortical and subcortical regions with subregional preferences. Using a combination of viral tracing and fluorescence micro-optical sectioning tomography (fMOST) in transgenic mice, we systematically dissected the whole-brain connectomes of intratelencephalic (IT) and pyramidal tract (PT) neurons in four mPFC subregions.

RESULTS

IT and PT neurons of the same subregion projected to different target areas while receiving inputs from similar upstream regions with quantitative differences. IT and PT neurons all project to the amygdala and basal forebrain, but their axons target different subregions. Compared to subregions in the prelimbic area (PL) which have more connections with sensorimotor-related regions, the infralimbic area (ILA) has stronger connections with limbic regions. The connection pattern of the mPFC subregions along the anterior-posterior axis showed a corresponding topological pattern with the isocortex and amygdala but an opposite orientation correspondence with the thalamus.

CONCLUSIONS

By using transgenic mice and fMOST imaging, we obtained the subregional preference whole-brain connectomes of IT and pyramidal tract PT neurons in the mPFC four subregions. These results provide a comprehensive resource for directing research into the complex functions of the mPFC by offering anatomical dissections of the different subregions.

Complete Neuron Reconstruction Based on Branch Confidence

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions (i.e., resolution anisotropy), which severely deteriorates the quality, reconstruction, and analysis of 3D volume images. By leveraging the natural anisotropy, we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets. By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods. In the experiments, we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms, e.g., wide-field, laser-scanning, or super-resolution microscopy. For the first time, Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2 × 0.2 × 0.2 μm3, which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost. Overall, Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline

Background

Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons.

Methods

For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting.

Results

Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.

Whole-mouse clearing and imaging at the cellular level with vDISCO

Homeostatic and pathological phenomena often affect multiple organs across the whole organism. Tissue clearing methods, together with recent advances in microscopy, have made holistic examinations of biological samples feasible. Here, we report the detailed protocol for nanobody(V_HH)-boosted 3D imaging of solvent-cleared organs (vDISCO), a pressure-driven, nanobody-based whole-body immunolabeling and clearing method that renders whole mice transparent in 3 weeks, consistently enhancing the signal of fluorescent proteins, stabilizing them for years. This allows the reliable detection and quantification of fluorescent signal in intact rodents enabling the analysis of an entire body at cellular resolution. Here, we show the high versatility of vDISCO applied to boost the fluorescence signal of genetically expressed reporters and clear multiple dissected organs and tissues, as well as how to image processed samples using multiple fluorescence microscopy systems. The entire protocol is accessible to laboratories with limited expertise in tissue clearing. In addition to its applications in obtaining a whole-mouse neuronal projection map, detecting single-cell metastases in whole mice and identifying previously undescribed anatomical structures, we further show the visualization of the entire mouse lymphatic system, the application for virus tracing and the visualization of all pericytes in the brain. Taken together, our vDISCO pipeline allows systematic and comprehensive studies of cellular phenomena and connectivity in whole bodies.

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.

Neuron tracing from light microscopy images: automation, deep learning and bench testing

MOTIVATION

Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey papers about neuron tracing from light microscopy data in the last decade, thanks to the rapid development of the field, there is a need to update recent progress in a review focusing on new methods and remarkable applications.

RESULTS

This review outlines neuron tracing in various scenarios with the goal to help the community understand and navigate tools and resources. We describe the status, examples and accessibility of automatic neuron tracing. We survey recent advances of the increasingly popular deep-learning enhanced methods. We highlight the semi-automatic methods for single neuron tracing of mammalian whole brains as well as the resulting datasets, each containing thousands of full neuron morphologies. Finally, we exemplify the commonly used datasets and metrics for neuron tracing bench testing.

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.

High-throughput morphometric and transcriptomic profiling uncovers composition of naïve and sensory-deprived cortical cholinergic VIP/CHAT neurons

Cortical neuronal networks control cognitive output, but their composition and modulation remain elusive. Here, we studied the morphological and transcriptional diversity of cortical cholinergic VIP/ChAT interneurons (VChIs), a sparse population with a largely unknown function. We focused on VChIs from the whole barrel cortex and developed a high-throughput automated reconstruction framework, termed PopRec, to characterize hundreds of VChIs from each mouse in an unbiased manner, while preserving 3D cortical coordinates in multiple cleared mouse brains, accumulating thousands of cells. We identified two fundamentally distinct morphological types of VChIs, bipolar and multipolar that differ in their cortical distribution and general morphological features. Following mild unilateral whisker deprivation on postnatal day seven, we found after three weeks both ipsi- and contralateral dendritic arborization differences and modified cortical depth and distribution patterns in the barrel fields alone. To seek the transcriptomic drivers, we developed NuNeX, a method for isolating nuclei from fixed tissues, to explore sorted VChIs. This highlighted differentially expressed neuronal structural transcripts, altered exitatory innervation pathways and established Elmo1 as a key regulator of morphology following deprivation.

Practical guide for preparation, computational reconstruction and analysis of 3D human neuronal networks in control and ischaemic conditions

To obtain commensurate numerical data of neuronal network morphology in vitro, network analysis needs to follow consistent guidelines. Important factors in successful analysis are sample uniformity, suitability of the analysis method for extracting relevant data and the use of established metrics. However, for the analysis of 3D neuronal cultures, there is little coherence in the analysis methods and metrics used in different studies. Here, we present a framework for the analysis of neuronal networks in 3D. First, we selected a hydrogel that supported the growth of human pluripotent stem cell-derived cortical neurons. Second, we tested and compared two software programs for tracing multi-neuron images in three dimensions and optimized a workflow for neuronal analysis using software that was considered highly suitable for this purpose. Third, as a proof of concept, we exposed 3D neuronal networks to oxygen-glucose deprivation- and ionomycin-induced damage and showed morphological differences between the damaged networks and control samples utilizing the proposed analysis workflow. With the optimized workflow, we present a protocol for preparing, challenging, imaging and analysing 3D human neuronal cultures.

Summary: An optimized protocol is presented that allows morphological, quantifiable differences between the damaged and control human neuronal networks to be detected in three-dimensional cultures.

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.

Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in the lateral image plane and low-resolution 2D images in other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution but also restores suppressed visual details between the imaging planes and removes imaging artifacts.

Volumetric fluorescence microscopy is often limited by anisotropic spatial resolution. Here, the authors present an unsupervised deep-learning approach that enhances axial resolution by learning from high-resolution lateral images, and demonstrate isotropic resolution and restoration of suppressed visual details.

Hidden Markov modeling for maximum probability neuron reconstruction

Recent advances in brain clearing and imaging have made it possible to image entire mammalian brains at sub-micron resolution. These images offer the potential to assemble brain-wide atlases of neuron morphology, but manual neuron reconstruction remains a bottleneck. Several automatic reconstruction algorithms exist, but most focus on single neuron images. In this paper, we present a probabilistic reconstruction method, ViterBrain, which combines a hidden Markov state process that encodes neuron geometry with a random field appearance model of neuron fluorescence. ViterBrain utilizes dynamic programming to compute the global maximizer of what we call the most probable neuron path. We applied our algorithm to imperfect image segmentations, and showed that it can follow axons in the presence of noise or nearby neurons. We also provide an interactive framework where users can trace neurons by fixing start and endpoints. ViterBrain is available in our open-source Python package brainlit.

ViterBrain is an automated probabilistic reconstruction method that can reconstruct neuronal geometry and processes from microscopy images with code available in the open-source Python package, brainlit.

Structure-Guided Segmentation for 3D Neuron Reconstruction

Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset.

Brain-wide projection reconstruction of single functionally defined neurons

Reconstructing axonal projections of single neurons at the whole-brain level is currently a converging goal of the neuroscience community that is fundamental for understanding the logic of information flow in the brain. Thousands of single neurons from different brain regions have recently been morphologically reconstructed, but the corresponding physiological functional features of these reconstructed neurons are unclear. By combining two-photon Ca²⁺ imaging with targeted single-cell plasmid electroporation, we reconstruct the brain-wide morphologies of single neurons that are defined by a sound-evoked response map in the auditory cortices (AUDs) of awake mice. Long-range interhemispheric projections can be reliably labelled via co-injection with an adeno-associated virus, which enables enhanced expression of indicator protein in the targeted neurons. Here we show that this method avoids the randomness and ambiguity of conventional methods of neuronal morphological reconstruction, offering an avenue for developing a precise one-to-one map of neuronal projection patterns and physiological functional features.

Brain-wide axonal projections of single neurons have been extensively reconstructed without any functional characterization. The authors present a method that allows for developing a precise one-to-one map of both projection patterns and functional features of single neurons in mice.

Quantitative mapping of keratin networks in 3D

Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical, and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in canine, murine, and human epithelial cells both, in vitro and in vivo. Numerical models are derived from confocal airyscan high-resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature, and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral, and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

TISSUE CLEARING

Tissue clearing of gross anatomical samples was first described over a century ago and has only recently found widespread use in the field of microscopy. This renaissance has been driven by the application of modern knowledge of optical physics and chemical engineering to the development of robust and reproducible clearing techniques, the arrival of new microscopes that can image large samples at cellular resolution and computing infrastructure able to store and analyze large data volumes. Many biological relationships between structure and function require investigation in three dimensions and tissue clearing therefore has the potential to enable broad discoveries in the biological sciences. Unfortunately, the current literature is complex and could confuse researchers looking to begin a clearing project. The goal of this Primer is to outline a modular approach to tissue clearing that allows a novice researcher to develop a customized clearing pipeline tailored to their tissue of interest. Further, the Primer outlines the required imaging and computational infrastructure needed to perform tissue clearing at scale, gives an overview of current applications, discusses limitations and provides an outlook on future advances in the field.

Minimizing probability graph connectivity cost for discontinuous filamentary structures tracing in neuron image

Neuron tracing from optical image is critical in understanding brain function in diseases. A key problem is to trace discontinuous filamentary structures from noisy background, which is commonly encountered in neuronal and some medical images. Broken traces lead to cumulative topological errors, and current methods were hard to assemble various fragmentary traces for correct connection. In this paper, we propose a graph connectivity theoretical method for precise filamentary structure tracing in neuron image. First, we build the initial subgraphs of signals via a region-to-region based tracing method on CNN predicted probability. CNN technique removes noise interference, whereas its prediction for some elongated fragments is still incomplete. Second, we reformulate the global connection problem of individual or fragmented subgraphs under heuristic graph restrictions as a dynamic linear programming function via minimizing graph connectivity cost, where the connected cost of breakpoints are calculated using their probability strength via minimum cost path. Experimental results on challenging neuronal images proved that the proposed method outperformed existing methods and achieved similar results of manual tracing, even in some complex discontinuous issues. Performances on vessel images indicate the potential of the method for some other tubular objects tracing.

Cross-Streams Through the Ventral Posteromedial Thalamic Nucleus to Convey Vibrissal Information

Whisker detection is crucial to adapt to the environment for some animals, but how the nervous system processes and integrates whisker information is still an open question. It is well-known that two main parallel pathways through Ventral posteromedial thalamic nucleus (VPM) ascend to the barrel cortex, and classical theory suggests that the cross-talk from trigeminal nucleus interpolaris (Sp5i) to principal nucleus (Pr5) between the main parallel pathways contributes to the multi-whisker integration in barrel columns. Moreover, some studies suggest there are other cross-streams between the parallel pathways. To confirm their existence, in this study we used a dual-viral labeling strategy and high-resolution, large-volume light imaging to get the complete morphology of individual VPM neurons and trace their projections. We found some new thalamocortical projections from the ventral lateral part of VPM (VPMvl) to barrel columns. In addition, the retrograde-viral labeling and imaging results showed there were the large trigeminothalamic projections from Sp5i to the dorsomedial section of VPM (VPMdm). Our results reveal new cross-streams between the parallel pathways through VPM, which may involve the execution of multi-whisker integration in barrel columns.

Foreground Estimation in Neuronal Images With a Sparse-Smooth Model for Robust Quantification

3D volume imaging has been regarded as a basic tool to explore the organization and function of the neuronal system. Foreground estimation from neuronal image is essential in the quantification and analysis of neuronal image such as soma counting, neurite tracing and neuron reconstruction. However, the complexity of neuronal structure itself and differences in the imaging procedure, including different optical systems and biological labeling methods, result in various and complex neuronal images, which greatly challenge foreground estimation from neuronal image. In this study, we propose a robust sparse-smooth model (RSSM) to separate the foreground and the background of neuronal image. The model combines the different smoothness levels of the foreground and the background, and the sparsity of the foreground. These prior constraints together contribute to the robustness of foreground estimation from a variety of neuronal images. We demonstrate the proposed RSSM method could promote some best available tools to trace neurites or locate somas from neuronal images with their default parameters, and the quantified results are similar or superior to the results that generated from the original images. The proposed method is proved to be robust in the foreground estimation from different neuronal images, and helps to improve the usability of current quantitative tools on various neuronal images with several applications.

Weakly Supervised Neuron Reconstruction From Optical Microscopy Images With Morphological Priors

Manually labeling neurons from high-resolution but noisy and low-contrast optical microscopy (OM) images is tedious. As a result, the lack of annotated data poses a key challenge when applying deep learning techniques for reconstructing neurons from noisy and low-contrast OM images. While traditional tracing methods provide a possible way to efficiently generate labels for supervised network training, the generated pseudo-labels contain many noisy and incorrect labels, which lead to severe performance degradation. On the other hand, the publicly available dataset, BigNeuron, provides a large number of single 3D neurons that are reconstructed using various imaging paradigms and tracing methods. Though the raw OM images are not fully available for these neurons, they convey essential morphological priors for complex 3D neuron structures. In this paper, we propose a new approach to exploit morphological priors from neurons that have been reconstructed for training a deep neural network to extract neuron signals from OM images. We integrate a deep segmentation network in a generative adversarial network (GAN), expecting the segmentation network to be weakly supervised by pseudo-labels at the pixel level while utilizing the supervision of previously reconstructed neurons at the morphology level. In our morphological-prior-guided neuron reconstruction GAN, named MP-NRGAN, the segmentation network extracts neuron signals from raw images, and the discriminator network encourages the extracted neurons to follow the morphology distribution of reconstructed neurons. Comprehensive experiments on the public VISoR-40 dataset and BigNeuron dataset demonstrate that our proposed MP-NRGAN outperforms state-of-the-art approaches with less training effort.

Neuron segmentation using 3D wavelet integrated encoder-decoder network

MOTIVATION

3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task.

RESULTS

In this article, we propose a 3D wavelet and deep learning-based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction.

AVAILABILITYAND IMPLEMENTATION

The data and codes for this work are available at https://github.com/LiQiufu/3D-WaveUNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dl-3-N-Butylphthalide Promotes Angiogenesis in an Optimized Model of Transient Ischemic Attack in C57BL/6 Mice

Transient ischemic attack (TIA) has been widely regarded as a clinical entity. Even though magnetic resonance imaging (MRI) results of TIA patients are negative, potential neurovascular damage might be present, and may account for long-term cognitive impairment. Animal models that simulate human diseases are essential tools for in-depth study of TIA. Previous studies have clarified that Dl-3-N-butylphthalide (NBP) promotes angiogenesis after stroke. However, the effects of NBP on TIA remain unknown. This study aims to develop an optimized TIA model in C57BL/6 mice to explore the microscopic evidence of ischemic injury after TIA, and investigate the therapeutic effects of NBP on TIA. C57BL/6 mice underwent varying durations (7, 8, 9 or 10 min) of middle cerebral artery occlusion (MCAO). Cerebral artery occlusion and reperfusion were assessed by laser speckle contrast imaging. TIA and ischemic stroke were distinguished by neurological testing and MRI examination at 24 h post-operation. Neuronal apoptosis was examined by TUNEL staining. Images of submicron cerebrovascular networks were obtained via micro-optical sectioning tomography. Subsequently, the mice were randomly assigned to a sham-operated group, a vehicle-treated TIA group or an NBP-treated TIA group. Vascular density was determined by immunofluorescent staining and fluorescein isothiocyanate method, and the expression of angiogenic growth factors were detected by western blot analysis. We found that an 8-min or shorter period of ischemia induced neither permanent neurological deficits nor MRI detectable brain lesions in C57BL/6 mice, but histologically caused neuronal apoptosis and cerebral vasculature abnormalities. NBP treatment increased the number of CD31+ microvessels and perfused microvessels after TIA. NBP also up-regulated the expression of VEGF, Ang-1 and Ang-2 and improved the cerebrovascular network. In conclusion, 8 min or shorter cerebral ischemia induced by the suture MCAO method is an appropriate TIA model in C57BL/6 mice, which conforms to the definition of human TIA, but causes microscopic neurovascular impairment. NBP treatment increased the expression of angiogenic growth factors, promoted angiogenesis and improved cerebral microvessels after TIA. Our study provides new insights on the pathogenesis and potential treatments of TIA.

Automated Neuron Tracing Using Content-Aware Adaptive Voxel Scooping on CNN Predicted Probability Map

Neuron tracing, as the essential step for neural circuit building and brain information flow analyzing, plays an important role in the understanding of brain organization and function. Though lots of methods have been proposed, automatic and accurate neuron tracing from optical images remains challenging. Current methods often had trouble in tracing the complex tree-like distorted structures and broken parts of neurite from a noisy background. To address these issues, we propose a method for accurate neuron tracing using content-aware adaptive voxel scooping on a convolutional neural network (CNN) predicted probability map. First, a 3D residual CNN was applied as preprocessing to predict the object probability and suppress high noise. Then, instead of tracing on the binary image produced by maximum classification, an adaptive voxel scooping method was presented for successive neurite tracing on the probability map, based on the internal content properties (distance, connectivity, and probability continuity along direction) of the neurite. Last, the neuron tree graph was built using the length first criterion. The proposed method was evaluated on the public BigNeuron datasets and fluorescence micro-optical sectioning tomography (fMOST) datasets and outperformed current state-of-art methods on images with neurites that had broken parts and complex structures. The high accuracy tracing proved the potential of the proposed method for neuron tracing on large-scale.

Quantification of relative neurite tortuosity using Fourier transforms

BACKGROUND

The tortuosity of nerve fibers has been shown to be important for identifying and monitoring clinically relevant manifestations resulting from of a variety of ocular and systemic disease pathologies and disorders. However, quantifying tortuosity in dense neurite networks can prove challenging, as existing methods require manual scoring and/or complete segmentation of the neurite network.

NEW METHOD

We measured neurite tortuosity by quantifying the degree of directional coherence in the Fourier transform of segmented neurite masks. This allowed for the analysis of neurite tortuosity without requiring complete segmentation of the neurite network. We were also able to adapt this method to measure tortuosity at different length and size scales.

RESULTS

With this novel method, neurite tortuosity was accurately quantified in simulated data sets at multiple length scales and scale variant and scale invariant tortuosity was accurately distinguished. Use of this method on images of murine corneal neurites correctly distinguished known differences between neurite tortuosity in the peripheral and central cornea.

COMPARISON WITH EXISTING METHOD(S)

Other methods require complete segmentation of neurites, which can be prohibitive in dense and/or sparsely labeled neurite networks such as in the cornea. Additionally, other methods require manual curation, manual scoring, or generation of a curated training set, while our novel method directly measures tortuosity as an intrinsic property of the image.

CONCLUSIONS

We report the use of Fourier transforms for quantification of neurite tortuosity at multiple length scales, and with an image input that contains incompletely segmented neurites. This new method does not require manual training or curation, allowing a direct and rapid measurement of neurite tortuosity, thereby enhancing the accuracy and utility of neurite tortuosity measurements for evaluation of ocular and systemic disease pathology.

Exploring highly reliable substructures in auto-reconstructions of a neuron

The digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron's reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D-based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.

NeuroRetriever: Automatic Neuron Segmentation for Connectome Assembly

Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias. In this study, we report an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of confocal fluorescence images of single neurons in the adult Drosophila brain. NeuroRetriever uses a high-dynamic-range thresholding method to segment three-dimensional morphology of single neurons based on branch-specific structural features. Applying NeuroRetriever to automatically segment single neurons in 22,037 raw brain images, we successfully retrieved 28,125 individual neurons validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.

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.

Exploring concomitant neuroimaging and genetic alterations in patients with and patients without auditory verbal hallucinations: A pilot study and mini review

Objective

To explore concomitant neuroimaging and genetic alterations in patients with schizophrenia with or without auditory verbal hallucinations (AVHs), and to discuss the use of pattern recognition techniques in the development of an objective index that may improve diagnostic accuracy and treatment outcomes for schizophrenia.

Methods

The pilot study included patients with schizophrenia with AVHs (SCH-AVH group) and without AVHs (SCH-no AVH group). High throughput sequencing (HTS) was performed to explore RNA networks. Global functional connectivity density (gFCD) analysis was performed to assess functional connectivity (FC) alterations of the default mode network (DMN). Quantitative long noncoding (lnc) RNA and mRNA expression data were related to peak T values of gFCDs using Pearson’s correlation coefficient analysis.

Results

Compared with the SCH-no AVH group (n = 5), patients in the SCH-AVH group (n = 5) exhibited differences in RNA expression in RNA networks that were related to AVH severity, and displayed alterations in FC (reflected by gFCD differences) within the DMN (posterior cingulate and dorsal-medial prefrontal cortex), and in the right parietal lobe, left occipital lobe, and left temporal lobe. Peak lncRNA expression values were significantly related to peak gFCD T values within the DMN.

Conclusion

Among patients with schizophrenia, there are concomitant FC and genetic expression alterations associated with AVHs. Data from pattern recognition studies may inform the development of an objective index aimed at improving early diagnostic accuracy and treatment planning for patients with schizophrenia with and without AVHs.

Neuron Image Segmentation via Learning Deep Features and Enhancing Weak Neuronal Structures

Neuron morphology reconstruction (tracing) in 3D volumetric images is critical for neuronal research. However, most existing neuron tracing methods are not applicable in challenging datasets where the neuron images are contaminated by noises or containing weak filament signals. In this paper, we present a two-stage 3D neuron segmentation approach via learning deep features and enhancing weak neuronal structures, to reduce the impact of image noise in the data and enhance the weak-signal neuronal structures. In the first stage, we train a voxel-wise multi-level fully convolutional network (FCN), which specializes in learning deep features, to obtain the 3D neuron image segmentation maps in an end-to-end manner. In the second stage, a ray-shooting model is employed to detect the discontinued segments in segmentation results of the first-stage, and the local neuron diameter of the broken point is estimated and direction of the filamentary fragment is detected by rayburst sampling algorithm. Then, a Hessian-repair model is built to repair the broken structures, by enhancing weak neuronal structures in a fibrous structure determined by the estimated local neuron diameter and the filamentary fragment direction. Experimental results demonstrate that our proposed segmentation approach achieves better segmentation performance than other state-of-the-art methods for 3D neuron segmentation. Compared with the neuron reconstruction results on the segmented images produced by other segmentation methods, the proposed approach gains 47.83% and 34.83% improvement in the average distance scores. The average Precision and Recall rates of the branch point detection with our proposed method are 38.74% and 22.53% higher than the detection results without segmentation.

Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis

BACKGROUND AND OBJECTIVE

Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application of this technique to AD mouse brain enables us to investigate neuron changes during the progression of AD pathology. However, how to deal with the huge amount of data becomes the bottleneck.

METHODS

Using MOST technology, we acquired 3D whole-brain images of six AD mice, and sampled the imaging data of four regions in each mouse brain for AD progression analysis. To count the number of neurons, we proposed a deep learning based method by detecting neuronal soma in the neuronal images. In our method, the neuronal images were first cut into small cubes, then a Convolutional Neural Network (CNN) classifier was designed to detect the neuronal soma by classifying the cubes into three categories, "soma", "fiber", and "background".

RESULTS

Compared with the manual method and currently available NeuroGPS software, our method demonstrates faster speed and higher accuracy in identifying neurons from the MOST images. By applying our method to various brain regions of 6-month-old and 12-month-old AD mice, we found that the amount of neurons in three brain regions (lateral entorhinal cortex, medial entorhinal cortex, and presubiculum) decreased slightly with the increase of age, which is consistent with the experimental results previously reported.

CONCLUSION

This paper provides a new method to automatically handle the huge amounts of data and accurately identify neuronal soma from the MOST images. It also provides the potential possibility to construct a whole-brain neuron projection to reveal the impact of AD pathology on mouse brain.

Divergent Projection Patterns Revealed by Reconstruction of Individual Neurons in Orbitofrontal Cortex

The orbitofrontal cortex (OFC) is involved in diverse brain functions via its extensive projections to multiple target regions. There is a growing understanding of the overall outputs of the OFC at the population level, but reports of the projection patterns of individual OFC neurons across different cortical layers remain rare. Here, by combining neuronal sparse and bright labeling with a whole-brain florescence imaging system (fMOST), we obtained an uninterrupted three-dimensional whole-brain dataset and achieved the full morphological reconstruction of 25 OFC pyramidal neurons. We compared the whole-brain projection targets of these individual OFC neurons in different cortical layers as well as in the same cortical layer. We found cortical layer-dependent projections characterized by divergent patterns for information delivery. Our study not only provides a structural basis for understanding the principles of laminar organizations in the OFC, but also provides clues for future functional and behavioral studies on OFC pyramidal neurons.

Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks

The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron type classification in the whole rat brain is challenging, given the significant number of neuron types, limited reconstructed neuron samples, and diverse data formats. Here, we report that different types of deep neural network modules may well process different kinds of features and that the integration of these submodules will show power on the representation and classification of neuron types. For SWC-format data, which are compressed but unstructured, we construct a tree-based recurrent neural network (Tree-RNN) module. For 2D or 3D slice-format data, which are structured but with large volumes of pixels, we construct a convolutional neural network (CNN) module. We also generate a virtually simulated dataset with two classes, reconstruct a CASIA rat-neuron dataset with 2.6 million neurons without labels, and select the NeuroMorpho-rat dataset with 35,000 neurons containing hierarchical labels. In the twelve-class classification task, the proposed model achieves state-of-the-art performance compared with other models, e.g., the CNN, RNN, and support vector machine based on hand-designed features.

A neuron image segmentation method based Deep Boltzmann Machine and CV model

Neuron image segmentation has wide applications and important potential values for neuroscience research. Due to the complexity of the submicroscopic structure of neurons cells and the defects of the image quality such as anisotropy, boundary loss and blurriness in electron microscopy-based (EM) imaging, and one faces a challenge in efficient automated segmenting large-scale neuron image 3D datasets, which is an essential prerequisite front-end process for the reconstruction of neuron circuits. Here, a neuron image segmentation method by combining Chan-Vest (CV) model with Deep Boltzmann Machine (DBM) is proposed, and a generative model is used to model and generate the target shape, it take this as a prior information to add global target shape feature constraint to the energy function of CV model, and the shape priori information is fused to assist neuron image segmentation. We applied our method to two 3D-EM datasets from different types of nerve tissue and achieved the best performance consistently across two classical evaluation metrics of neuron segmentation accuracy, namely Variation of Information (VoI) and Adaptive Rand Index (ARI). Experimental results show that the fusion algorithm has high segmentation accuracy, strong robustness, and can characterize the sub-microstructure information of neuron images well.

A guidebook for DISCO tissue clearing

Histological analysis of biological tissues by mechanical sectioning is significantly time-consuming and error-prone due to loss of important information during sample slicing. In the recent years, the development of tissue clearing methods overcame several of these limitations and allowed exploring intact biological specimens by rendering tissues transparent and subsequently imaging them by laser scanning fluorescence microscopy. In this review, we provide a guide for scientists who would like to perform a clearing protocol from scratch without any prior knowledge, with an emphasis on DISCO clearing protocols, which have been widely used not only due to their robustness, but also owing to their relatively straightforward application. We discuss diverse tissue-clearing options and propose solutions for several possible pitfalls. Moreover, after surveying more than 30 researchers that employ tissue clearing techniques in their laboratories, we compiled the most frequently encountered issues and propose solutions. Overall, this review offers an informative and detailed guide through the growing literature of tissue clearing and can help with finding the easiest way for hands-on implementation.

3D Neuron Microscopy Image Segmentation via the Ray-Shooting Model and a DC-BLSTM Network

The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.

Neuronal Population Reconstruction From Ultra-Scale Optical Microscopy Images via Progressive Learning

Reconstruction of neuronal populations from ultra-scale optical microscopy (OM) images is essential to investigate neuronal circuits and brain mechanisms. The noises, low contrast, huge memory requirement, and high computational cost pose significant challenges in the neuronal population reconstruction. Recently, many studies have been conducted to extract neuron signals using deep neural networks (DNNs). However, training such DNNs usually relies on a huge amount of voxel-wise annotations in OM images, which are expensive in terms of both finance and labor. In this paper, we propose a novel framework for dense neuronal population reconstruction from ultra-scale images. To solve the problem of high cost in obtaining manual annotations for training DNNs, we propose a progressive learning scheme for neuronal population reconstruction (PLNPR) which does not require any manual annotations. Our PLNPR scheme consists of a traditional neuron tracing module and a deep segmentation network that mutually complement and progressively promote each other. To reconstruct dense neuronal populations from a terabyte-sized ultra-scale image, we introduce an automatic framework which adaptively traces neurons block by block and fuses fragmented neurites in overlapped regions continuously and smoothly. We build a dataset "VISoR-40" which consists of 40 large-scale OM image blocks from cortical regions of a mouse. Extensive experimental results on our VISoR-40 dataset and the public BigNeuron dataset demonstrate the effectiveness and superiority of our method on neuronal population reconstruction and single neuron reconstruction. Furthermore, we successfully apply our method to reconstruct dense neuronal populations from an ultra-scale mouse brain slice. The proposed adaptive block propagation and fusion strategies greatly improve the completeness of neurites in dense neuronal population reconstruction.

Continuous imaging of large-volume tissues with a machinable optical clearing method at subcellular resolution

Optical clearing methods are widely used for three-dimensional biological information acquisition in the whole organ. However, the imaging quality of cleared tissues is often limited by ununiformed tissue clearing. By combining tissue clearing with mechanical sectioning based whole organ imaging system, we can reduce the influence of light scattering and absorption on the tissue to get isotropic and high resolution in both superficial and deep layers. However, it remains challenging for optical cleared biological tissue to maintain good sectioning property. Here, we developed a clearing method named M-CUBIC (machinable CUBIC), which combined a modified CUBIC method with PNAGA (poly-N-acryloyl glycinamide) hydrogel embedding to transparentize tissue while improving its sectioning property. With high-throughput light-sheet tomography platform (HLTP) and fluorescent micro-optical sectioning tomography (fMOST), we acquired continuous datasets with subcellular resolution from intact mouse brains for single neuron tracing, as well as the fine vascular structure of kidneys. This method can be used to acquire microstructures of multiple types of biological organs with subcellular resolutions, which can facilitate biological research.

Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows

Knowledge about neuron morphology is key to understanding brain structure and function. There are a variety of software tools that are used to segment and trace the neuron morphology. However, these tools usually utilize proprietary formats. This causes interoperability problems since the information extracted with one tool cannot be used in other tools. This article aims to improve neuronal reconstruction workflows by facilitating the interoperability between two of the most commonly used software tools—Neurolucida (NL) and Imaris (Filament Tracer). The new functionality has been included in an existing tool—Neuronize—giving rise to its second version. Neuronize v2 makes it possible to automatically use the data extracted with Imaris Filament Tracer to generate a tracing with dendritic spine information that can be read directly by NL. It also includes some other new features, such as the ability to unify and/or correct inaccurately-formed meshes (i.e., dendritic spines) and to calculate new metrics. This tool greatly facilitates the process of neuronal reconstruction, bridging the gap between existing proprietary tools to optimize neuroscientific workflows.

Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions

Background

Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, single-neuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files.

Results

We propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise.

Conclusions

This method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.