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14 results for “Neuron reconstruction”
Source and Tissue Agnostic Reconstruction of Neurons
<p>Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and brain tissues used for training -- severely limiting their usability by the broader neuroscience community. To fill this gap, we describe a novel connectomics challenge for source- and tissue-agnostic reconstruction of neurons (STARN), which favors broad generalization over fitting specific datasets. We first demonstrate that current state-of-the-art approaches to neuron segmentation perform poorly on the challenge. We further describe a novel convolutional recurrent neural network module that combines short-range horizontal connections within a processing stage and long-range top-down connections between stages. The resulting architecture establishes the state of the art on the STARN challenge -- improving the prospect for computer vision to allow for widespread fully-automated connectomics analysis.</p> <p>Here, we address the poor generalization of computer vision systems in connectomics with a novel challenge: the source- and tissue-agnostic reconstruction of neurons. This challenge presents a "training" dataset consisting of five publicly available and annotated tissue volumes representing a variety of organisms and imaging configurations (CREMI, FIB-25, and SNEMI3D); evaluation is performed on an independent volume <a href="https://www.nature.com/articles/nature09818">[1]</a> annotated by our group. We will demonstrate that the STARN challenge defeats state-of-the-art systems for neuron reconstruction.</p>
Dense skeleton reconstructions of neurons in the larval zebrafish olfactory bulb
<p>Large-scale reconstructions of neuronal populations are critical for structural analyses of neuronal cell types and circuits. Dense reconstructions of neurons from image data require ultrastructural resolution throughout large volumes, which can be achieved by automated volumetric electron microscopy (EM) techniques. We used serial block face scanning EM (SBEM) and conductive sample embedding to acquire an image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of 9.25 × 9.25 × 25 nm<sup>3</sup> (Wanner et al., 2016). Skeletons of 1,022 neurons, ~98% of all neurons in the OB, were reconstructed by manual tracing and efficient error correction procedures. An ergonomic software package, PyKNOSSOS, was created in Python for data browsing, neuron tracing, synapse annotation, and visualization. PyKNOSSOS is available for free download (https://github.com/adwanner/PyKNOSSOS). The reconstructions as provided for download here allow for detailed analyses of morphology, projections and subcellular features of different neuron types. The high density of reconstructions enables geometrical and topological analyses of the OB circuitry. Image data can be accessed and viewed through the neurodata web services (http://www.neurodata.io/wanner16). Raw data and reconstructions can be visualized in PyKNOSSOS.</p> <p> </p> <p>PyKNOSSOS: https://github.com/adwanner/PyKNOSSOS</p> <p>Wanner AA, Genoud C, Masudi T, Siksou L, Friedrich RW (2016) Dense EM-based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb. Nat Neurosci 19:816-825.<br /> http://www.nature.com/neuro/journal/v19/n6/full/nn.4290.html</p>
Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings
<p>Supplementary Data for<strong> "Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings", Hong et al.</strong></p>
3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset2
<p>wide type planarian 6G10 staining taken with TLSM</p>
NeuroFly Neuron Reconstruction Dataset
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Single-neuron Reconstruction of the Macaque Primary Motor Cortex
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>Here are 26 reconstructed neuron SWC files of the primary motor cortex from a one-year-old cynomolgus monkey.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>In this study, we achieved visualization of neurons through sparse labeling and volumetric imaging with synchronized on-the-fly-scan and readout (VISoR) technique. Following whole-brain 3D reconstruction (<a href="https://github.com/SMART-pipeline/Volume-reconstruction" target="_blank" rel="noopener noreferrer">GitHub - SMART-pipeline/Volume-reconstruction</a>), we employed Lychnis for single-neuron reconstruction (<a href="https://github.com/SMART-pipeline/Lychnis-tracing" target="_blank" rel="noopener noreferrer">GitHub - SMART-pipeline/Lychnis-tracing</a>). Within Lychnis, the visualization toolkit (VTK) and virtual finger are utilized for 3D rendering and interactive labeling purposes.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>The complete image datasets (raw and processed) of macaque brains exceed 1 petabyte, rendering it impractical to upload the entirety to a public data repository. This part of datasets generated and/or analyzed during the current study is available from the corresponding authors upon reasonable request.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Data from: Reconstruction of genetically identified neurons imaged by serial-section electron microscopy
Resolving patterns of synaptic connectivity in neural circuits currently requires serial section electron microscopy. However, complete circuit reconstruction is prohibitively slow and may not be necessary for many purposes such as comparing neuronal structure and connectivity among multiple animals. Here, we present an alternative strategy, targeted reconstruction of specific neuronal types. We used viral vectors to deliver peroxidase derivatives, which catalyze production of an electron-dense tracer, to genetically identified neurons, and developed a protocol that enhances the electron-density of the labeled cells and while retaining quality of the ultrastructure. The high contrast of the marked neurons enabled two innovations that dramatically speed data acquisition: targeted high-resolution reimaging of regions selected from rapidly-acquired lower resolution reconstruction, and an unsupervised segmentation algorithm. This pipeline reduces imaging and reconstruction times by at least two orders of magnitude, facilitating directed inquiry of circuit motifs.
Data from: Reconstruction of genetically identified neurons imaged by serial-section electron microscopy
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Dataset for PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron
<p dir="auto">If you find our work useful in your research, please consider citing our paper:</p> <div> <pre><code>Cai Lin, Fan Taiyu, Qu Xuzhong, Zhang Ying, Gou Xianyu, Ding Quanwei, Feng Weihua, Cao Tingting, Lv Xiaohua, Liu Xiuli, Huang Qing, Quan Tingwei, Zeng Shaoqun (2024) PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron eLife 13:RP102840 https://doi.org/10.7554/eLife.102840.2</code></pre> </div>
Whole-brain Neuron Reconstructions for Building Neuronal Connections at Multi-levels
<p>The dataset for construct connection networks of single neurons, including 1,877 neuron reconstructions, predicted bouton data, and 18,370 neurons. All neuronal morphologies were resampled with a spacing of 1 µm between consecutive points to ensure uniform reconstruction of the structures. The generated connectomes are also archived on the repository.</p> <p>1877-CELL_METADATA.xlsx: metadata of 1,877 neurons;</p> <p>SEU_1um.zip: 1,877 neuron reconstructions (full morphology);</p> <p>DEN-18370.zip: 1,877 neuron reconstructions (dendritic morphology);</p> <p>predicted_bouton_locations.zip: predicted bouton data (only coordinates and the corresponding neurons);</p> <p>jupyter tutorial: to generate connectivity matices of connectomes, and perform related analysis;</p> <p>arbor-net connections: arbor-net, single-neuron connectivity;</p> <p>bouton-net connections: bouton-net, single-neuron connectivity;</p> <p>all bouton from F1877: processed bouton locations, including their coordinates and brain regions;</p> <p>PACs from F1877: PACs from F1877 dataset, including coordinates, soma locations, and region of locations;</p> <p>SEU_net_w_corticalLayers and SEU_net_wo_corticalLayers: SEU-net brain region connectivity w or wo cortical layers.</p>
Reconstructed neuron meshes from the Ciona connectome
<p>This file contains the reconstructed <em>Ciona</em> neuron meshes from the Ciona connectome. (<a href="https://doi.org/10.7554/eLife.16962">Ryan 2016</a>) The meshes are packaged to load in Unity 2021.3.</p>
Gapr for Large-scale Collaborative Single-Neuron Reconstruction: reconstruction results
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Gapr for Large-scale Collaborative Single-Neuron Reconstruction: test data
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3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset1
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.