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Neuroimaging Data Provenance Using the LONI Pipeline Workflow Environment Allan MacKenzie-Graham IPAW2008 Arash Payan Ivo Dinov John Van Horn Arthur W.

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Presentation on theme: "Neuroimaging Data Provenance Using the LONI Pipeline Workflow Environment Allan MacKenzie-Graham IPAW2008 Arash Payan Ivo Dinov John Van Horn Arthur W."— Presentation transcript:

1 Neuroimaging Data Provenance Using the LONI Pipeline Workflow Environment Allan MacKenzie-Graham IPAW2008 Arash Payan Ivo Dinov John Van Horn Arthur W. Toga

2 2 Provenance in Neuroimaging Tools used and data described must be adequately described and documented Tools used and data described must be adequately described and documented Determining data qualityDetermining data quality InterpretationInterpretation ReproducibilityReproducibility ReusabilityReusability InteroperabilityInteroperability 2

3 3 First Provenance Challenge 3 (Moreau et. al, 2007)

4 4 Provenance Systems 4 (Moreau et. al, 2007)

5 5 Goals of the LONI Provenance System Description Description DataData ProcessingProcessing Reproducibility Reproducibility Across platformsAcross platforms Across compilationsAcross compilations Across software versionsAcross software versions Ease of use Ease of use 5

6 6 Neuroimaging Data Provenance Neuroimaging data provenance ProjectSubjectSpeciesAgeSexAcquisitionScannerOrientationWeighting Field Strength TRTETI

7 7 Provenance Editor 7

8 8 LONI Pipeline 8

9 9 LONI Pipeline Module 9

10 10 Workflow Provenance 10

11 11 Executable Provenance Executable provenance EnvironmentOptions Input files Output files Binary provenance Binary configuration Configuration options System configuration Architecture Operating system CompilerLibraries Script provenance ShellScript Binary provenance

12 12 Alignlinear Provenance http://www.loni.ucla.edu/Software/Software_Detail.jsp?software_id=8 12

13 13 Reproducibility Across Platform Across Platform ICA workflowICA workflow Across compilations Across compilations MDA workflowMDA workflow 13

14 14 Independent Components Analysis

15 15 Different Architectures Yield Different Results

16 16 Minimum Deformation Atlas

17 17 Different Compilation Options Yield Different Results

18 18 Complex Neuroimaging Workflow 18

19 19 Future Directions Community involvement Community involvement provenance.loni.ucla.eduprovenance.loni.ucla.edu Make LONI Pipeline aware of provenance files Make LONI Pipeline aware of provenance files Read in provenance fileRead in provenance file Display executable provenanceDisplay executable provenance Append provenance informationAppend provenance information Write out provenance fileWrite out provenance file Visualize provenance files Visualize provenance files Interface similar to LONI PipelineInterface similar to LONI Pipeline Invoke LONI Pipeline to recreate file or modify processingInvoke LONI Pipeline to recreate file or modify processing Provenance Database Provenance Database Database of workflowsDatabase of workflows

20 20 Acknowledgements Arthur W. Toga Arthur W. Toga Director, Laboratory of Neuro ImagingDirector, Laboratory of Neuro Imaging Arash Payan Arash Payan Lead Developer, LONI PipelineLead Developer, LONI Pipeline Ivo D. Dinov Ivo D. Dinov Assistant Professor, Laboratory of Neuro ImagingAssistant Professor, Laboratory of Neuro Imaging John D. Van Horn John D. Van Horn Assistant Professor, Laboratory of Neuro ImagingAssistant Professor, Laboratory of Neuro Imaging


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