BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

Peng, Hanchuan; Hawrylycz, Michael; Roskams, Jane; Hill, Sean; Spruston, Nelson; Meijering, Erik; Ascoli, Giorgio A.

doi:10.1016/j.neuron.2015.06.036

Cited by 220 publications

(187 citation statements)

References 26 publications

Supporting

Mentioning

182

Contrasting

Unclassified

Order By: Relevance

“…), x , y , z , radius, and parent identifier (used to describe the tree structure). SWC files are widely available and widely supported: SWC is the morphology format for BigNeuron, a project to advance the state of the art for 3D neuron reconstruction [103]. All morphologies submitted to the NeuroMorpho.Org repository [97] are converted into SWC format.…”

Section: Declarative Model Descriptionsmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

show abstract

Section: Declarative Model Descriptionsmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…It is unlikely that the connectome project can be tackled by a select few taking a top-down approach, as was possible in the elucidation of the human genome 54,55 . Instead, by tasking multiple groups with mapping discrete neural circuits, one can envision the draft of a connectome gradually emerging through stitching together these individual wiring diagrams (e.g., http://www.openconnectomeproject.org) 56–58 . When combined with long-working-depth objectives and high-throughput imaging (LSFM, e.g., CLARITY optimized light-sheet microscopy 15 ; and the custom-made, economical system presented here in Fig.…”

Section: Introductionmentioning

confidence: 99%

Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping

et al. 2015

View full text Add to dashboard Cite

To facilitate fine-scale phenotyping of whole specimens, we describe here a set of tissue fixation-embedding, detergent-clearing and staining protocols that can be used to transform excised organs and whole organisms into optically transparent samples within 1–2 weeks without compromising their cellular architecture or endogenous fluorescence. PACT (passive CLARITY technique) and PARS (perfusion-assisted agent release in situ) use tissue-hydrogel hybrids to stabilize tissue biomolecules during selective lipid extraction, resulting in enhanced clearing efficiency and sample integrity. Furthermore, the macromolecule permeability of PACT- and PARS-processed tissue hybrids supports the diffusion of immunolabels throughout intact tissue, whereas RIMS (refractive index matching solution) grants high-resolution imaging at depth by further reducing light scattering in cleared and uncleared samples alike. These methods are adaptable to difficult-to-image tissues, such as bone (PACT-deCAL), and to magnified single-cell visualization (ePACT). Together, these protocols and solutions enable phenotyping of subcellular components and tracing cellular connectivity in intact biological networks.

show abstract

“…based on GFP-expressing neurotropic viruses 101 ) that allow trans-synaptic tracing of neurons open doors for more detailed connectome studies. This work further shows that numerous, sometimes redundant, approaches (described in literature) are currently employed to analyse neuronal morphology, making it difficult to select the best method for a given dataset 102 . In order to compare the accuracy and the computational efficiency of these different methods, the BigNeuron project was launched in March 2015 12 .…”

Section: Discussionmentioning

confidence: 86%

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

Detrez

Verstraelen

Gebuis

et al. 2016

Focus on Bio-Image Informatics

View full text Add to dashboard Cite

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium-bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.2

show abstract

BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

Cited by 220 publications

References 26 publications

Reproducibility in Computational Neuroscience Models and Simulations

Reproducibility in Computational Neuroscience Models and Simulations

Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

Contact Info

Product

Resources

About