Ciftify: A framework for surface-based analysis of legacy MR acquisitions

Erin W. Dickie, Alan Anticevic, Dawn E. Smith, Timothy S. Coalson, Mathuvanthi Manogaran, Navona Calarco, Joseph D. Viviano, Matthew F. Glasser, David C. Van Essen, Aristotle N. Voineskos

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


The preprocessing pipelines of the Human Connectome Project (HCP) were made publicly available for the neuroimaging community to apply the HCP analytic approach to data from non-HCP sources. The HCP analytic approach is surface-based for the cerebral cortex, uses the CIFTI “grayordinate” file format, provides greater statistical sensitivity than traditional volume-based analysis approaches, and allows for a more neuroanatomically-faithful representation of data. However, the HCP pipelines require the acquisition of specific images (namely T2w and field map) that historically have often not been acquired. Massive amounts of this ‘legacy’ data could benefit from the adoption of HCP-style methods. However, there is currently no published framework, to our knowledge, for adapting HCP preprocessing to “legacy” data. Here we present the ciftify project, a parsimonious analytic framework for adapting key modules from the HCP pipeline into existing structural workflows using FreeSurfer's recon_all structural and existing functional preprocessing workflows. Within this framework, any functional dataset with an accompanying (i.e. T1w) anatomical data can be analyzed in CIFTI format. To simplify usage for new data, the workflow has been bundled with fMRIPrep following the BIDS-app framework. Finally, we present the package and comment on future neuroinformatics advances that may accelerate the movement to a CIFTI-based grayordinate framework.

Original languageEnglish
Pages (from-to)818-826
Number of pages9
StatePublished - Aug 15 2019


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