Human Connectome Project Minimal Preprocessing Pipelines in Nipype
The HCP minimal preprocessing pipelines (Glasser, et al, 2013) and Nipype (Gorgolewski, et al, 2011) are both gifts given to the neuroimaging field. It would be a waste of resources not to combine the power of these two tools by using Nipype to implement the HCP pipelines.
From the Glasser, et al, 2013 abstract the HCP pipelines were developed “to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space.” In addition to this feat, the HCP pipelines output 90,000+ “brainordinate” resolution time series (that’s gray matter and subcortical structures) for resting state data in native subject space.
From http://nipy.sourceforge.net/nipype/ “[Nipype] is a Python project that provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow.” Nipype is capable of integrating a variety of software packages and even sending jobs smartly (read: parallelizable) across a cluster to reduce execution time.
So, learn some Nipype and some HCP pipelines from people that use them and let’s get to work. Thanks for reading!
- Eric Earl, OHSU Fair Neuroimaging Laboratory
- e-mail: email@example.com
- website: http://www.ohsu.edu/xd/education/schools/school-of-medicine/departments/basic-science-departments/behn/people/labs/fair-neuroimaging-lab/