Testing the Dimensions with CIFTI Files

In cifti: Toolbox for Connectivity Informatics Technology Initiative ('CIFTI') Files

knitr::opts_chunk$set(echo = TRUE)

install_dir = tempfile()
dir.create(install_dir)

Load MSC data and test data from cifti package

Using download_cifti_data() and download.file to download the test data.

library(cifti)
download_cifti_data(outdir = install_dir)

Here we will download a NIfTI file from the MSC dataset:

mscfile = file.path(
  install_dir, 
  "sub-MSC01_ses-func01_task-rest_bold_32k_fsLR.dtseries.nii")
if (!file.exists(mscfile)) {
  # url = paste0(
  #   "https://openneuro.org/crn/datasets/ds000224/snapshots/00002/files/", 
  #   "derivatives:", "surface_pipeline:", "sub-MSC01:",
  #   "processed_restingstate_timecourses:",  "ses-func01:cifti:",
  #   "sub-MSC01_ses-func01_task-rest_bold_32k_fsLR.dtseries.nii")
  url = paste0("https://s3.amazonaws.com/openneuro/ds000224/ds000224_R1.0.2/",
               "uncompressed/derivatives/surface_pipeline/sub-MSC01/", 
               "processed_restingstate_timecourses/ses-func01/cifti/", 
               "sub-MSC01_ses-func01_task-rest_bold_32k_fsLR.dtseries.nii")
  res = download.file(url = url, 
    destfile = mscfile, mode = "wb")
  stopifnot(file.exists(mscfile))
}

test_dir = file.path(install_dir, "cifti-2_test_data")

ds <- read_cifti(
  file.path(test_dir,
            "Conte69.MyelinAndCorrThickness.32k_fs_LR.dscalar.nii")
  )
dt <- read_cifti(
  file.path(test_dir,
            "Conte69.MyelinAndCorrThickness.32k_fs_LR.dtseries.nii")
  )
pt <- read_cifti(
  file.path(test_dir,
            "Conte69.MyelinAndCorrThickness.32k_fs_LR.ptseries.nii")
  )
dl <- read_cifti(
  file.path(test_dir, 
            "Conte69.parcellations_VGD11b.32k_fs_LR.dlabel.nii") 
  )
msc <- read_cifti(mscfile, trans_data = FALSE)
msc_trans <- read_cifti(mscfile, trans_data = TRUE)

For reference: MSC 01 (65890 * 818) (using matlab ft_read_cifti), showing first 5 rows & columns

matfile = system.file("extdata", "msc01_10by10.csv.gz", package = "cifti")
mat = read.csv(matfile, header = FALSE)
msc01_refmat <- as.matrix(mat)
print(msc01_refmat[1:5,1:5])

MSC Sub-01 Session 1 (sub-MSC01_ses-func01_task-rest_bold_32k_fsLR.dtseries.nii)

read_cifti(fname = , trans_data = FALSE) header dimension shows 65890 * 818 read in file shows 65890 * 818 by setting trans_data to FALSE

msc$hdr
dim(msc$data)

msc$data[1:5,1:5]

• If transpose is not set to FALSE (default is TRUE), it will return a 818 * 65890 that is filled correctly, and user just have to t() the cii$data

t(msc_trans$data[1:5,1:5])

Data where dimensions are row x dense loads fine. Updated function loads them the same way as before.

Conte69.MyelinAndCorrThickness.32k_fs_LR.dscalar.nii

• Dense file "d", vertex level data
• Header shows dimension as rows * dense

ds$hdr
attributes(ds$BrainModel[[1]])
attributes(ds$BrainModel[[2]])
dim(ds$data)
head(ds$data)

Conte69.MyelinAndCorrThickness.32k_fs_LR.dtseries.nii

dt$hdr
attributes(dt$BrainModel[[1]])
attributes(dt$BrainModel[[2]])
dim(dt$data)
head(dt$data)

Conte69.MyelinAndCorrThickness.32k_fs_LR.ptseries.nii

• parcel level

pt$hdr
dim(pt$data)
head(pt$data)

Conte69.parcellations_VGD11b.32k_fs_LR.dlabel.nii (dense label on vertex level)

• dense label

dl$hdr
dim(dl$data)
head(dl$data)

cifti documentation built on Jan. 14, 2026, 1:07 a.m.