ArticleCell and tissue research2015
The importance of metadata to assess information content in digital reconstructions of neuronal morphology.
Article in Cell and tissue research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review.Neuroinformatics · 2024Pooled it
- Accelerating the continuous community sharing of digital neuromorphology data.FASEB bioAdvances · 2024Article
- Online conversion of reconstructed neural morphologies into standardized SWC format.Nature communications · 2023Article
- Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.Journal of neuroscience research · 2023Article
- Assisted neuroscience knowledge extraction via machine learning applied to neural reconstruction metadata on NeuroMorpho.Org.Brain informatics · 2022Article
- Large scale similarity search across digital reconstructions of neural morphology.Neuroscience research · 2022Article
- A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling.Neuroinformatics · 2022Article
- Efficient metadata mining of web-accessible neural morphologies.Progress in biophysics and molecular biology · 2022Article
- Comprehensive Estimates of Potential Synaptic Connections in Local Circuits of the Rodent Hippocampal Formation by Axonal-Dendritic Overlap.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Highlights from the Era of Open Source Web-Based Tools.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Review
- Prediction of Neural Diameter From Morphology to Enable Accurate Simulation.Frontiers in neuroinformatics · 2021Article
- An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology.Brain informatics · 2020Article
- Article
- Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs.BMC genomics · 2018Article
- An open repository for single-cell reconstructions of the brain forest.Scientific data · 2018Article
- Mining Big Neuron Morphological Data.Computational intelligence and neuroscience · 2018Review
- An ontology-based search engine for digital reconstructions of neuronal morphology.Brain informatics · 2017Article
- Win-win data sharing in neuroscience.Nature methods · 2017Article
- Sharing Neuron Data: Carrots, Sticks, and Digital Records.PLoS biology · 2015Article
- Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis.BMC bioinformatics · 2015Article
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Authors and funding
3 authors.
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Abstract
Digital reconstructions of axonal and dendritic arbors provide a powerful representation of neuronal morphology in formats amenable to quantitative analysis, computational modeling, and data mining. Reconstructed files, however, require adequate metadata to identify the appropriate animal species, developmental stage, brain region, and neuron type. Moreover, experimental details about tissue processing, neurite visualization and microscopic imaging are essential to assess the information content of digital morphologies. Typical morphological reconstructions only partially capture the underlying biological reality. Tracings are often limited to certain domains (e.g., dendrites and not axons), may be incomplete due to tissue sectioning, imperfect staining, and limited imaging resolution, or can disregard aspects irrelevant to their specific scientific focus (such as branch thickness or depth). Gauging these factors is critical in subsequent data reuse and comparison. NeuroMorpho.Org is a central repository of reconstructions from many laboratories and experimental conditions. Here, we introduce substantial additions to the existing metadata annotation aimed to describe the completeness of the reconstructed neurons in NeuroMorpho.Org. These expanded metadata form a suitable basis for effective description of neuromorphological data.
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