An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology

Bijari, Kayvan; Akram, Masood A.; Ascoli, Giorgio A.

doi:10.1186/s40708-020-00103-3

“…On the other, it provides critical information regarding neuronal identity by encoding its somatic region and quantitative projection targets. This information, together with the specification of essential details regarding the brain specimen and imaging modality, fulfills the recommended requirements for standard metadata description of neuromorphological reconstructions 40 . Thus, the MorphoHub IT infrastructure is seamlessly compliant for effective pipelining with the NeuroMorpho.Org public data sharing platform, ensuring maximum impact through expanded community outreach 41 .…”

Section: Discussionmentioning

confidence: 94%

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Jiang

¹

,

Wang

²

,

Liu

³

et al. 2022

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Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed MorphoHub to address this challenge by optimizing both the data and workflow management. In particular, this work presents a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry. Our method also boosts data sharing and remote collaborative validation. We applied MorphoHub to a petabyte application dataset involving 62 whole mouse brains, and identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.necessary to analyze synaptic patterns in a neuron's arborization, while whole-brain scale is essential to delineate long projecting axonal arbors 8 . As a result, even for the mouse brain, a widely used model system of mammalian brains, a typical 3-D brain-image dataset will have tens of teravoxels in volume 9,10 . On the other hand, neurons have a very complicated tree-like shape, and are often labelled and visualized sparsely using chemical 11,12 , transgenic 13 or viral approaches 14,15 . The number of morphologically distinguishable neurons per brain is often limited. Therefore, to understand the vast complexity and variation of neurons, it is crucial to obtain a large collection of brain image datasets 16,17 . As each voxel is often stored as one or more bytes, the multimorphometry problem arises as a petabyte-computing challenge, and as a paramount task for current bioimage informatics applications and technologies [18][19][20][21] .There is a long history of reconstructing individual neurons' morphology with image analysis 22,23 . Subneuronal structures including somata, spines and boutons have also been segmented and analyzed from images 5,24-28 . This is a challenge of high community interest. A number of algorithms have been examined and compared against each other in public initiatives, e.g. DIADEM 29 or in the global collaborative BigNeuron initiative 30 . However, most existing methods are applicable only to smaller image datasets and partial neuronal structures. For individual mammalian brain datasets, technologies that can handle teravoxels of image volume to trace millimeters long neurite fibers emerged only recently, including TeraFly 31 , UltraTracer 32 , BigDataV...

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“…On the other, it provides critical information regarding neuronal identity by encoding its somatic region and quantitative projection targets. This information, together with the specification of essential details regarding the brain specimen and imaging modality, fulfills the recommended requirements for standard metadata description of neuromorphological reconstructions 40 . Thus, our IT infrastructure is seamlessly compliant for effective pipelining with the NeuroMorpho.Org public data sharing platform, ensuring maximum impact through expanded community outreach 41 .…”

Section: Discussionmentioning

confidence: 94%

MorphoHub: A Platform for Petabyte-Scale Multi-Morphometry Generation

Jiang

¹

,

Wang

²

,

Liu

³

et al. 2021

Preprint

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Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

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“…Presently, NeuroMorpho.Org is growing at a pace of .20,000 reconstructions per year, with a substantial rate increase since 2016 (Ascoli, 2019). This acceleration results from a combination of factors, including but not limited to community production of ever larger datasets thanks to continuous neurotechnology developments along with faster and more affordable computing (Nanda et al, 2015), transparent disclosures of data availability (Ascoli, 2015), rising interest in and openness toward data sharing in neuroscience (Ascoli, 2007;Ascoli et al, 2017), and the progressive automation of literature searches (Maraver et al, 2019) and metadata retrieval from scientific publications (Bijari et al, 2020).…”

Section: Allen Mouse Brain Connectivity Atlasmentioning

confidence: 99%

Highlights from the Era of Open Source Web-Based Tools

Anderson

¹

,

Harris

²

,

Ng

³

et al. 2021

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High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span subdisciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.

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An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology

Cited by 19 publications

References 38 publications

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

MorphoHub: A Platform for Petabyte-Scale Multi-Morphometry Generation

Highlights from the Era of Open Source Web-Based Tools

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