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Neuroimage. 2019 Aug 15;197:818-826. doi: 10.1016/j.neuroimage.2019.04.078. Epub 2019 May 12.

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

Author information

1
Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada. Electronic address: erin.dickie@camh.ca.
2
Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA.
3
Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada.
4
Departments of Radiology and Neuroscience, Washington University School of Medicine, St Louis, USA.
5
Departments of Radiology and Neuroscience, Washington University School of Medicine, St Louis, USA; St. Luke's Hospital, Chesterfield, MO, USA.
6
Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.

Abstract

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.

PMID:
31091476
PMCID:
PMC6675413
[Available on 2020-08-15]
DOI:
10.1016/j.neuroimage.2019.04.078

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