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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.


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:



You can install ciftiTools from CRAN with:


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 --------
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(
  surfL_fname=surfL_fname, surfR_fname=surfR_fname,

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,
  surfL=surfL, surfR=surfR

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


See this link to view the tutorial vignette.

List of commonly-used functions

Basics: reading, plotting, writing

Manipulating CIFTI files

Manipulating "xifti" objects

Surface gemoetry


See NAMESPACE for a full list of all exported functions.


ciftiTools graphical summary
“xifti” object structure
Surfaces comparison. The “very inflated”, “inflated”, and “midthickness” surfaces are included in ciftiTools through the function load_surf. See the data acknowledgement section at the bottom of this README.


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:

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

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 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 light to dark: darker regions might be in a shadow, or their values might be higher. To skip shading, use the argument material=list(lit=FALSE) to view_xifti_surface.

Lit surface plot Unlit surface plot

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 are 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

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).