ciftiTools

R-CMD-check AppVeyor build status Coveralls test coverage

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. Greyordinate 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.

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, brainstructures="all", 
  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

See this link to view the tutorial vignette.

Illustrations

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.

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:

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

Citation

You can cite our pre-print at https://arxiv.org/abs/2106.11338.

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).