ciftiTools

CIFTI files contain brain imaging data in “grayordinates,” which represent the gray matter as cortical surface vertices (left and right) and subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). ciftiTools provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the “dscalar,” “dlabel,” and “dtseries” intents. Grayordinate data is read in as a "xifti" object, which is structured for convenient access to the data and metadata, and includes support for surface geometry files to enable spatially-dependent functionality such as static or interactive visualizations and smoothing.

Citation

If you use ciftiTools, please cite our paper:

Pham, D. D., Muschelli, J., & Mejia, A. F. (2022). ciftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R. NeuroImage, 250, 118877.

You can also obtain citation information from within R like so:

citation("ciftiTools")

Installation

You can install ciftiTools from CRAN with:

install.packages("ciftiTools")

Additionally, most of the ciftiTools functions require the Connectome Workbench, which can be installed from the HCP website.

Quick start guide

# Load the package and point to the Connectome Workbench --------
library(ciftiTools)
ciftiTools.setOption("wb_path", "path/to/workbench")

# Read and visualize a CIFTI file -------------------------------
cifti_fname <- ciftiTools::ciftiTools.files()$cifti["dtseries"]
surfL_fname <- ciftiTools.files()$surf["left"]
surfR_fname <- ciftiTools.files()$surf["right"]

xii <- read_cifti(
  cifti_fname, 
  surfL_fname=surfL_fname, surfR_fname=surfR_fname,
  resamp_res=4000
)

view_xifti_surface(xii) # or plot(xii)
# view_xifti_volume(xii) if subcortex is present

# Access CIFTI data ---------------------------------------------
cortexL <- xii$data$cortex_left
cortexL_mwall <- xii$meta$medial_wall_mask$left
cortexR <- xii$data$cortex_right
cortexR_mwall <- xii$meta$medial_wall_mask$right
# subcortVol <- xii$data$subcort
# subcortLabs <- xii$meta$subcort$labels
# subcortMask <- xii$meta$subcort$mask
surfL <- xii$surf$cortex_left
surfR <- xii$surf$cortex_right

# Create a `"xifti"` from data ----------------------------------
xii2 <- as.xifti(
  cortexL=cortexL, cortexL_mwall=cortexL_mwall,
  cortexR=cortexR, cortexR_mwall=cortexR_mwall,
  #subcortVol=subcortVol, subcortLabs=subcortLabs,
  #subcortMask=subcortMask,
  surfL=surfL, surfR=surfR
)

# Write a CIFTI file --------------------------------------------
write_cifti(xii2, "my_cifti.dtseries.nii")

Vignette/Demo

See this link to view the tutorial vignette.

List of commonly-used functions

Basics: reading, plotting, writing

• ciftiTools.setOption: Necessary to point to the Connectome Workbench each time ciftiTools is loaded.
• read_cifti: Read in a CIFTI file as a "xifti" object.
• view_xifti Plot the cortex and/or subcortex. Has many options for controlling the visualization.
• write_cifti: Write a "xifti" object to a CIFTI file.

Manipulating CIFTI files

• resample_cifti: Resample to a different resolution.
• separate_cifti: Separate a CIFTI file into GIFTI and NIFTI files.
• smooth_cifti: Smooth the data along the surface.
• (Use run_wb_cmd to execute Connectome Workbench commands from R)

Manipulating "xifti" objects

• apply_xifti: Similar to base::apply.
• combine_xifti: Combine "xifti"s with non-overlapping brain structures.
• convert_xifti: Convert between dlabel, dscalar, and dtseries.
• impute_xifti: Impute data values from neighboring vertices/voxels.
• merge_xifti: Concatenate "xifti"s.
• move_from_mwall: Convert the medial wall mask to a data value, deleting the mask.
• move_to_mwall: Mask out a particular data value.
• newdata_xifti: Replace the data values.
• resample_xifti: Resample to a different resolution.
• scale_xifti: Similar to base::scale.
• select_xifti: Rearrange the columns to reorder, take a subset, or repeat them.
• smooth_xifti: Smooth the data along the surface.
• transform_xifti: Apply a vectorizable function.

Surface gemoetry

• load_surf: Load a surface geometry included in the package.
• read_surf: Read in a GIFTI surface geometry file as a "surf" object.
• write_surf: Write a "surf" object to a GIFTI surface geometry file.

Parcellations

• apply_parc: Apply a function to each parcel separately.
• load_parc: Load a parcellation included in the package.

See NAMESPACE for a full list of all exported functions.

Illustrations

FAQ

Why is a CIFTI file that has been read in called a "xifti"?

The "xifti" object is a general interface for not only CIFTI files, but also GIFTI and NIFTI files. For example, we can plot a surface GIFTI:

gii_surf <- read_surf(ciftiTools.files()$surf["left"])
xii <- as.xifti(surfL=gii_surf)
plot(xii)

We can also convert metric GIFTI files and/or NIFTI files to CIFTI files (or vice versa) using the "xifti" object as an intermediary.

How do I convert to CIFTI Format…

…from NIFTI volumetric cortical data?

The cortex data will have to be projected to the surface. If the data are in BIDS format, then fMRIprep can be used to convert the volumetric cortex data to fsaverage surface space. Note that ciftiTools::remap_cifti can be used after to convert from fsaverage to fs_LR surface space, since some CIFTI-related packages assume the data are registered to fs_LR. fMRIprep is a wrapper around FreeSurfer commands; alternatively, commands from FreeSurfer can be used directly. Relevant commands include mri_vol2surf, fslregister, mris_preproc, and mris_convert. The result of mris_convert will be in GIFTI format, which can be converted into CIFTI format with ciftiTools::as.cifti.

More options include those from the Connectome Workbench, ciftify, DeepPrep, and Nilearn. We also welcome input from the community about additional ways for converting from volumetric data to CIFTI format.

…from NIFTI volumetric subcortical data?

Use the Connectome Workbench command -cifti-create-dense-from-template.

How do I visualize cortical data without applying shading to the mesh geometry?

The 3D shading may make certain plots more difficult to interpret, if the color scale varies from dark to light: darker regions might be in a shadow, or their values might be lower. To skip shading, use the argument material=list(lit=FALSE) to view_xifti_surface.

How do I get VoxelIndicesIJK or the MNI coordinates for the subcortex?

For a "xifti" object xii with subcortical data, the mask of data locations is saved in xii$meta$subcort$mask. To obtain the array coordinates of the in-mask locations, use which(xii$meta$subcort$mask, arr.ind=TRUE) - 1. This matrix has each subcortical voxel along the rows, and its I, J, and K array coordinates along the three columns. 1 is subtracted because the coordinates should begin with 0 rather than 1. It’s equivalent to the original CIFTI metadata entry VoxelIndicesIJK. To convert array coordinates to MNI coordinates, multiply by the transformation matrix xii$meta$subcort$trans_mat:

VoxIJK <- which(xii$meta$subcort$mask, arr.ind=TRUE) - 1
VoxIJK <- cbind(VoxIJK, 1) # for 4th col of transform mat (translation)
VoxXYZ <- t(xii$meta$subcort$trans_mat[seq(3),] %*% t(VoxIJK)) # MNI coords

Why do the labels/orders of Limbic A and Limbic B in the Yeo 17 parcellation appear swapped, relative to this Figure from an earlier paper?

The copy of the Yeo 17 parcellation included in ciftiTools is correct. An earlier figure for the Yeo 17 parcellation had mistakenly swapped these labels, but this error has since been corrected. See the “Important note” from this page: https://github.com/ThomasYeoLab/CBIG/blob/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/README.md#version-12-schaefer2018_roisparcels_717networks_order

Related R extensions

• NIFTI files: oro.nifti, RNifti
• GIFTI files: gifti
• CIFTI files: cifti can read in any CIFTI file, whereas ciftiTools provides a user-friendly interface for CIFTI files with the dscalar, dlabel, and dtseries intents only.
• Other structural neuroimaging files: fsbrain
• xml files: xml2
• Interactive 3D rendering: rgl

Data acknowledgement

The following data are included in the package for convenience:

Example CIFTI files provided by NITRC.

Cortical surfaces provided by the HCP, according to the Data Use Terms:

Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Several parcellations provided by Thomas Yeo’s Computational Brain Imaging Group (CBIG):

1. Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106, 1125–1165 (2011).
2. Schaefer, A. et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex 28, 3095–3114 (2018).
3. Kong, R. et al. Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. Cerebral Cortex (2021).