Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline
The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols [1]. It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools....
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2009-07-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/neuro.11.022.2009/full |
| _version_ | 1818144890111918080 |
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| author | Ivo Dinov John Van Horn Kamen Lozev Rico Magsipoc Petros Petrosyan Zhizhong Liu Allan MacKenzie-Graha Paul Eggert Douglass S Parker Douglass S Parker Arthur W Toga |
| author_facet | Ivo Dinov John Van Horn Kamen Lozev Rico Magsipoc Petros Petrosyan Zhizhong Liu Allan MacKenzie-Graha Paul Eggert Douglass S Parker Douglass S Parker Arthur W Toga |
| author_sort | Ivo Dinov |
| collection | DOAJ |
| description | The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols [1]. It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides 3 layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer’s Disease Neuroimaging Initiative [2], we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available o |
| first_indexed | 2024-12-11T11:54:45Z |
| format | Article |
| id | doaj.art-fb5ebb6fee1741b5aca92b669cd2ca5a |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-11T11:54:45Z |
| publishDate | 2009-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-fb5ebb6fee1741b5aca92b669cd2ca5a2022-12-22T01:08:16ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962009-07-01310.3389/neuro.11.022.2009609Efficient, distributed and interactive neuroimaging data analysis using the LONI pipelineIvo Dinov0John Van Horn1Kamen Lozev2Rico Magsipoc3Petros Petrosyan4Zhizhong Liu5Allan MacKenzie-Graha6Paul Eggert7Douglass S Parker8Douglass S Parker9Arthur W Toga10UCLAUCLAUCLAUCLAUCLAUCLAUCLAUCLAUCLAUCLAUCLAThe LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols [1]. It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides 3 layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer’s Disease Neuroimaging Initiative [2], we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available ohttp://journal.frontiersin.org/Journal/10.3389/neuro.11.022.2009/fullNeuroimagingsoftware toolsdata provenanceLONI Pipelineresourcestool integration |
| spellingShingle | Ivo Dinov John Van Horn Kamen Lozev Rico Magsipoc Petros Petrosyan Zhizhong Liu Allan MacKenzie-Graha Paul Eggert Douglass S Parker Douglass S Parker Arthur W Toga Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline Frontiers in Neuroinformatics Neuroimaging software tools data provenance LONI Pipeline resources tool integration |
| title | Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline |
| title_full | Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline |
| title_fullStr | Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline |
| title_full_unstemmed | Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline |
| title_short | Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline |
| title_sort | efficient distributed and interactive neuroimaging data analysis using the loni pipeline |
| topic | Neuroimaging software tools data provenance LONI Pipeline resources tool integration |
| url | http://journal.frontiersin.org/Journal/10.3389/neuro.11.022.2009/full |
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