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Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

BACKGROUND: Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, 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...

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Autores principales: Johnson, Erik C, Wilt, Miller, Rodriguez, Luis M, Norman-Tenazas, Raphael, Rivera, Corban, Drenkow, Nathan, Kleissas, Dean, LaGrow, Theodore J, Cowley, Hannah P, Downs, Joseph, K. Matelsky, Jordan, J. Hughes, Marisa, P. Reilly, Elizabeth, A. Wester, Brock, L. Dyer, Eva, P. Kording, Konrad, R. Gray-Roncal, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751400/
https://www.ncbi.nlm.nih.gov/pubmed/33347572
http://dx.doi.org/10.1093/gigascience/giaa147
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author Johnson, Erik C
Wilt, Miller
Rodriguez, Luis M
Norman-Tenazas, Raphael
Rivera, Corban
Drenkow, Nathan
Kleissas, Dean
LaGrow, Theodore J
Cowley, Hannah P
Downs, Joseph
K. Matelsky, Jordan
J. Hughes, Marisa
P. Reilly, Elizabeth
A. Wester, Brock
L. Dyer, Eva
P. Kording, Konrad
R. Gray-Roncal, William
author_facet Johnson, Erik C
Wilt, Miller
Rodriguez, Luis M
Norman-Tenazas, Raphael
Rivera, Corban
Drenkow, Nathan
Kleissas, Dean
LaGrow, Theodore J
Cowley, Hannah P
Downs, Joseph
K. Matelsky, Jordan
J. Hughes, Marisa
P. Reilly, Elizabeth
A. Wester, Brock
L. Dyer, Eva
P. Kording, Konrad
R. Gray-Roncal, William
author_sort Johnson, Erik C
collection PubMed
description BACKGROUND: Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, 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 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. RESULTS: We developed an ecosystem of neuroimaging data analysis pipelines that use 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, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. CONCLUSIONS: 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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spelling pubmed-77514002020-12-29 Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets Johnson, Erik C Wilt, Miller Rodriguez, Luis M Norman-Tenazas, Raphael Rivera, Corban Drenkow, Nathan Kleissas, Dean LaGrow, Theodore J Cowley, Hannah P Downs, Joseph K. Matelsky, Jordan J. Hughes, Marisa P. Reilly, Elizabeth A. Wester, Brock L. Dyer, Eva P. Kording, Konrad R. Gray-Roncal, William Gigascience Technical Note BACKGROUND: Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, 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 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. RESULTS: We developed an ecosystem of neuroimaging data analysis pipelines that use 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, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. CONCLUSIONS: 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. Oxford University Press 2020-12-21 /pmc/articles/PMC7751400/ /pubmed/33347572 http://dx.doi.org/10.1093/gigascience/giaa147 Text en © The Author(s) 2020. Published by Oxford University Press GigaScience. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Johnson, Erik C
Wilt, Miller
Rodriguez, Luis M
Norman-Tenazas, Raphael
Rivera, Corban
Drenkow, Nathan
Kleissas, Dean
LaGrow, Theodore J
Cowley, Hannah P
Downs, Joseph
K. Matelsky, Jordan
J. Hughes, Marisa
P. Reilly, Elizabeth
A. Wester, Brock
L. Dyer, Eva
P. Kording, Konrad
R. Gray-Roncal, William
Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
title Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
title_full Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
title_fullStr Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
title_full_unstemmed Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
title_short Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
title_sort toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751400/
https://www.ncbi.nlm.nih.gov/pubmed/33347572
http://dx.doi.org/10.1093/gigascience/giaa147
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