Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets.
Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets.
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朝着可重复处理体积、纳米级神经成像数据集的可扩展框架迈进。
DOI：
10.1093/gigascience/giaa147
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发表时间：
2020-12-21
期刊：
影响因子：
9.2
通讯作者：
R Gray-Roncal W
中科院分区：
文献类型：
--
作者：
Johnson EC;Wilt M;Rodriguez LM;Norman-Tenazas R;Rivera C;Drenkow N;Kleissas D;LaGrow TJ;Cowley HP;Downs J;K Matelsky J;J Hughes M;P Reilly E;A Wester B;L Dyer E;P Kording K;R Gray-Roncal W
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. 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. 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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影响因子：
3.5
作者：
Gorgolewski K;Burns CD;Madison C;Clark D;Halchenko YO;Waskom ML;Ghosh SS
通讯作者：
Ghosh SS
影响因子：
64.8
作者：
Bock, Davi D.;Lee, Wei-Chung Allen;Kerlin, Aaron M.;Andermann, Mark L.;Hood, Greg;Wetzel, Arthur W.;Yurgenson, Sergey;Soucy, Edward R.;Kim, Hyon Suk;Reid, R. Clay
通讯作者：
Reid, R. Clay
影响因子：
48
作者：
Chung, Kwanghun;Deisseroth, Karl
通讯作者：
Deisseroth, Karl
DOI：
10.1073/pnas.1720862115
发表时间：
2018-03-06
影响因子：
11.1
作者：
Busse, Madleen;Mueller, Mark;Pfeiffer, Franz
通讯作者：
Pfeiffer, Franz
影响因子：
9.2
作者：
Kotliar, Michael;Kartashov, Andrey V.;Barski, Artem
通讯作者：
Barski, Artem