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The importance of metadata to assess information content in digital reconstructions of neuronal morphology


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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The authors are grateful to Namra Ansari, Julian Burke, Meghan Eyerman, and Lauretta Wilkerson for their literature mining efforts. This work is supported by NIH R01 NS39600 from NINDS, ONR MURI 14101-0198, and Keck NAKFI to G.A.A.

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Correspondence to Giorgio A. Ascoli.

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Parekh, R., Armañanzas, R. & Ascoli, G.A. The importance of metadata to assess information content in digital reconstructions of neuronal morphology. Cell Tissue Res 360, 121–127 (2015). https://doi.org/10.1007/s00441-014-2103-6

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  • Neuron morphology
  • Metadata
  • Digital reconstruction
  • Data standards
  • Completeness