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. 2008 Aug 1;42(1):178-95.
doi: 10.1016/j.neuroimage.2008.04.186. Epub 2008 Apr 25.

Provenance in neuroimaging

Allan J Mackenzie-Graham  1 , John D Van Horn, Roger P Woods, Karen L Crawford, Arthur W Toga
Affiliations

Provenance in neuroimaging

Allan J Mackenzie-Graham et al. Neuroimage. .

Abstract

Provenance, the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging. Knowledge about the origin and history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high fidelity replication and re-use. We have drafted a mechanism for describing provenance in a simple and easy to use environment, alleviating the burden of documentation from the user while still providing a rich description of an image's provenance. This combination of ease of use and highly descriptive metadata should greatly facilitate the collection of provenance and subsequent sharing of data.

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Figures

Figure 1
Figure 1
Provenance Editor. A screen capture of the Provenance Editor demonstrating a subset of the information extracted from a DICOM header file and a number of user modifiable tags.
Figure 2
Figure 2
Pipeline Module Definition. A screen capture of a LONI Pipeline module definition for the Alignlinear (AIR 5.2.5) executable.
Figure 3
Figure 3
An Independent Components Analysis Workflow. A screen capture of a LONI Pipeline workflow for the calculation of the independent components of a series of BOLD images.
Figure 4
Figure 4
Different Architectures Yield Different Results. A, The result of an independent components analysis of a series of BOLD functional MRI brain images using workflow A. The lower right panel shows the intensity distribution of the image. The scale is 105 voxels. B, The difference between images A and C rendered in false color to highlight differences. The lower right panel shows the intensity distribution of the image. The scale is 105 voxels. C, The result of an independent components analysis of a series of BOLD functional MRI brain images using workflow B. The lower right panel shows the intensity distribution of the image. The scale is 105 voxels.
Figure 5
Figure 5
A Non-linear Alignment Workflow. A screen capture of a LONI Pipeline workflow for the non-linear alignment of two images.
Figure 6
Figure 6
Different Compilation Options Yield Different Results. A, A T2-weigthed mouse brain image that has been aligned to a minimum deformation atlas using workflow C. The lower right panel shows the intensity distribution of the image. The scale is 105 voxels. B, The difference between panels A and C rendered in false color to highlight differences. The lower right panel shows the intensity distribution of the image. The scale is 105 voxels. C, A T2-weigthed mouse brain image that has been aligned to a minimum deformation atlas using workflow D. The lower right panel shows the intensity distribution of the image. The scale is 105 voxels.
Figure 7
Figure 7
Complex Heterogeneous Pipeline. A screen capture of a complex, heterogeneous LONI Pipeline workflow demonstrating that any number of executables can be described in the Pipeline and therefore represented by the provenance schema.
See this image and copyright information in PMC

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