The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery

Hider, Robert; Dm, Kleissas; Pryor, Derek; Gion, Timothy; Rodríguez, Luis M.; Matelsky, Jordan; Gray-Roncal, William; Wester, Brock A.

doi:10.1101/217745

Cited by 13 publications

(25 citation statements)

References 16 publications

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“…For ease of use, DICED is a Python wrapper that makes a DVID store behave as if it were a NumPy array [74]. bossDB uses microservices (AWS Lambda) to fulfill cutout requests from an image in Amazon S3 cloud storage [75]. ndstore shares common roots with bossDB [76], but uses Amazon EC2 servers rather than microservices.…”

Section: Distributed Chunk-wise Processing Of Large Imagesmentioning

confidence: 99%

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Lee¹,

Turner

Macrina

et al. 2019

Current Opinion in Neurobiology

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Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes.

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Section: Distributed Chunk-wise Processing Of Large Imagesmentioning

confidence: 99%

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Lee¹,

Turner

Macrina

et al. 2019

Current Opinion in Neurobiology

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“…A single SBFSEM volumetric reconstruction may create an image stack comprising thousands of micrographs, which can occupy hundreds of gigabytes, and this would represent only a fraction of the data needed to describe the reach of one pyramidal neuron . For large‐scale ultrastructural studies, automated analysis tools and data storage solutions are needed to facilitate the processing of large imaging datasets collected from many individuals across broad swaths of the brain . While obstacles in access to tissue samples, image collection time, and data storage are nontrivial, we highlight these imaging techniques because of their unique potential for providing direct evidence of structural disruptions producing pathology.…”

Section: Imaging Brain Structure At Subcellular Resolution With Scannmentioning

confidence: 99%

“…151 For large-scale ultrastructural studies, automated analysis tools and data storage solutions are needed to facilitate the processing of large imaging datasets collected from many individuals across broad swaths of the brain. 156 While obstacles in access to tissue samples, image collection time, and data storage are nontrivial, we highlight these imaging techniques because of their unique potential for providing direct evidence of structural disruptions producing pathology. Further, we hope that appraisal of both the promise and limitations associated with these approaches can accelerate their development and eventual application.…”

Section: Imaging Brain Structure At Subcellular Resolution With Scannmentioning

confidence: 99%

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

Stein

2019

Psychiatry Clin Neurosci

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Imaging genetics aims to identify genetic variants associated with the structure and function of the human brain. Recently, collaborative consortia have been successful in this goal, identifying and replicating common genetic variants influencing gross human brain structure as measured through magnetic resonance imaging. In this review, we contextualize imaging genetic associations as one important link in understanding the causal chain from genetic variant to increased risk for neuropsychiatric disorders. We provide examples in other fields of how identifying genetic variant associations to disease and multiple phenotypes along the causal chain has revealed a mechanistic understanding of disease risk, with implications for how imaging genetics can be similarly applied. We discuss current findings in the imaging genetics research domain, including that common genetic variants can have a slightly larger effect on brain structure than on risk for disorders like schizophrenia, indicating a somewhat simpler genetic architecture. Also, gross brain structure measurements share a genetic basis with some, but not all, neuropsychiatric disorders, invalidating the previously held belief that they are broad endophenotypes, yet pinpointing brain regions likely involved in the pathology of specific disorders. Finally, we suggest that in order to build a more detailed mechanistic understanding of the effects of genetic variants on the brain, future directions in imaging genetics research will require observations of cellular and synaptic structure in specific brain regions beyond the resolution of magnetic resonance imaging. We expect that integrating genetic associations at biological levels from synapse to sulcus will reveal specific causal pathways impacting risk for neuropsychiatric disorders.

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“…Large neuroimaging datasets are distinct from many canonical big data solutions because researchers typically analyze a few (often one) very large datasets instead of many individual images. Custom storage solutions [19,51] exist, but often require tools, knowledge, and access patterns that are disparate from those used by many neuroscience laboratories. SABER provides tools to connect to specialized neuroimaging databases which integrate into CWL tool pipelines.…”

Section: Cloud Computation and Storagementioning

confidence: 99%

“…SABER introduces canonical pipelines for EM and XRM, specified in CWL, with a library of dockerized tools. These tools are deployed using the workflow execution engine Apache Airflow [16] using Amazon Web Services (AWS) Batch to scale compute resources with imaging data stored in the volumetric database bossDB [19]. Metadata, parameters, and tabular results are logged using the neuroimaging database Datajoint [20].…”

Section: Introductionmentioning

confidence: 99%

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

Johnson

Wilt

Rodríguez

et al. 2019

Preprint

Self Cite

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Emerging neuroimaging datasets (collected through modalities such as Electron Microscopy, Calcium Imaging, or X-ray Microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational expertise or resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. We developed an ecosystem of neuroimaging data analysis pipelines that utilize open source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, that connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets, but may be applied to similar problems in other domains.

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The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery

Cited by 13 publications

References 16 publications

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

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