josephdviviano/ciftify

The tools of the Human Connectome Project (HCP) adapted for working with non-HCP datasets

ciftify

The tools of the Human Connectome Project (HCP) adapted for working with non-HCP datasets

ciftify is a set of three types of command line tools:

1. conversion tools : bash scripts adapted from HCP Minimal processing pipeline to put preprocessed T1 and fMRI data into an HCP like folder structure
2. ciftify tools : Command line tools for making working with cifty format a little easier
3. cifti_vis tools : Visualization tools, these use connectome-workbench tools to create pngs of standard views the present theme together in fRML pages.

Download and Install

Right now I haven't gotten around to figuring out a nicer install, so the easiest is to just clone the repo and set some enviroment variables:

• add the ciftify/bin to your PATH
• add the ciftify directory to your PYTHONPATH
• create a new environment variable (HCP_SCENE_TEMPLATES) that point to the location of the template scene files
• create a new environment variable (CIFTIFY_TEMPLATES) that points to the location of the data directory.
• create an environment variable for the location of your HCP_DATA

git clone https://github.com/edickie/ciftify.git
export PATH=$PATH:<ciftify/bin>
export PYTHONPATH=$PYTHONPATH:<ciftify>
export HCP_DATA=/path/to/hcp/subjects/data/
export HCP_SCENE_TEMPLATES=<ciftify>/data/scene_templates
export CIFTIFY_TEMPLATES=<ciftify>/data

Requirements

ciftify draws upon the tools and templates of the HCP minimally processed pipelines and therefore is dependant on them and there prereqs:

• HCP Minimal Processing Pipeline (any release) [https://github.com/Washington-University/Pipelines/releases]
• connectome-workbench (tested with version 1.1.1) [http://www.humanconnectome.org/software/get-connectome-workbench]
• FSL [http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/]
• freesurfer [https://surfer.nmr.mgh.harvard.edu/fswiki]
• ImageMagick (for cifti-vis image manipultion)

ciftify is mostly written in python 2 with the following package dependancies:

• numpy
• nibabel
• docopt
• pandas
• seaborn (only for PINT vis)

Conversion Tools

Scripts adapted from HCP Minimal processing pipeline to put preprocessed T1 and fMRI data into an HCP like folder structure

• fs2hcp
  • Will convert any freeserfer output directory into an HCP (cifti space) output directory
• func2hcp
  • Will project a nifti functional scan to a cifti .dtseries.nii in that subjects hcp analysis directory
  • The subject's hcp analysis directory is created by runnning fs2hcp on that participants freesurfer output
  • will do fancy outlier removal to optimize the mapping in the process and then smooth the data in cifti space
• cifity_a_nifti
  • Will project a nifti scan to cifti space (4D nifti -> .dtseries.nii or 3D nifti -> .dsclar.nii) with no fancy steps or smoothing
  • intended for conversion of 3D statistical maps (or 3D regions of interest) for visualization with wb_view

ciftify Tools

• ciftify_meants:
  • extracts mean timeseries(es) (similar to FSL' fslmeants) that can take nifti, cifti or gifti inputs
• ciftify_seed_corr:
  • builds seed-based correlation maps using cifti, gifti or nifti inputs
• ciftify_peaktable:
  • similar to FSL's clusterize, outputs a csv table of peak locations from a cifti statisical map
• ciftify_surface_rois:
  • a tool for building circular rois on the cortical surface. Multiple roi locations can be read at once from a csv table.
• ciftify_groupmask:
  • a tools for building a group mask for statiscal analyses using multiple .dtseries.nii files as the input

cifti_vis Tools

• citfi_vis_qc:
  • builds visual qc pages for verification of fs2hcp and func2hcp conversion
  • Note: these pages can also be used for qc of freesurfer's recon-all pipeline
  • (they easier to generate (i.e. no display needed) than freesurfer QAtools, and a little prettier too)
• cifti_vis_map:
  • generates picture of standard views from any cifti map (combined into on .html page)
  • One can loop over multiple files (i.e. maps from multiple subjects) and combine all outputs so that all subjects can viewed together in one index page.
  • can also take a nifti input which is internally converted to cifti using ciftify_a_nifti
• cifti_vis_RSN:
  • From a functional file input, Will run seed-based correlations from 4 ROIS of interest then generate pics of standard views
  • One can loop over multiple files (i.e. maps from multiple subjects) and combine all outputs so that all subjects can viewed together in one index page.
  • can also take a nifti input which is internally converted to cifti using ciftify_a_nifti

And also in the bin there is

These two are part of a work in progress (something I need to validate first) ciftify_PINT_vertices cifti_vis_PINT epi_hcpexport