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Data released on October 26, 2016

Human Connectome Project Minimal Preprocessing Pipelines to Nipype

Demeter, D, V; Earl, E; Goddings, A; Gonzalez-Castillo, J; Gorgolewski, K, J; Ketz, N; Mihai, G; Mills, K; Reddan, M, C; Reineberg, A; Ruzic, L (2016): Human Connectome Project Minimal Preprocessing Pipelines to Nipype GigaScience Database. http://dx.doi.org/10.5524/100223 RIS BibTeX Text

The goal was to convert the Human Connectome Project (HCP) Minimal Preprocessing Pipelines into Nipype code. The HCP minimal preprocessing pipelines represent a significant advance in image processing pipelines in our time. They provide preprocessed volume and surface data in native and atlas space, for both functional and structural data. Nipype is an open source neuroimaging project for designing imaging pipelines which has been around since 2011 and provides many excellent features for provenance and reliability of processing pipelines. Together, these two pieces of software would allow for a more robust, more flexible synergy of pipeline design and operability.
More work is needed to truly contribute back to the HCP Pipelines. The greatest achievement of the hackathon project was forming a collaborative team of interested Nipype developers who were trained and are ready to continue collaborating across seven institutions. Future work will continue trying to achieve the original goals as stated, but may need an organizer to hold the team accountable to deadlines.

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Related manuscripts:

doi:10.1186/s13742-016-0147-0

Related datasets:

doi:10.5524/100223 IsPartOf doi:10.5524/100215

Additional information:

https://github.com/ericearl/hcp2nipype-hack2015/

Neuroscience, Imaging

http://gigadb.org/images/data/cropped/no_image.png

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