In mandymejia/ciftiTools: Tools for Reading, Writing, Viewing and Manipulating CIFTI Files

library(knitr)
knitr::opts_chunk$set(autodep = TRUE, cache = FALSE)

ciftiTools is an R package for working with CIFTI-2 format brain imaging data. Used in conjunction with GIFTI surface geometry files, CIFTI files enable surface-based analysis of gray matter data, which has several advantages over traditional volumetric/voxel-based analysis. Because of this, the CIFTI-2 format is used by recent neuroimaging studies including the Human Connectome Project (HCP). ciftiTools supports reading, writing, visualizing, resampling, and other operations for CIFTI files with the ".dscalar.nii", ".dtseries.nii", and ".dlabel.nii" intents. Several of these operations are made possible by the Connectome Workbench.

To get started, the first time you use ciftiTools, install it from either CRAN with install.packages() or Github with devtools::install_github(). Here, we will use the CRAN version.

# Check if package installed. If not, install it.
if(!require('ciftiTools', quietly=TRUE)){
  install.packages('ciftiTools')
  # devtools::install_github('mandymejia/ciftiTools') # development version
}

Now we load the ciftiTools package.

library(ciftiTools)

Next, we indicate where to find the Connectome Workbench. This can be the full path to the Connectome Workbench executable file, or the path to its containing folder, in which case ciftiTools will locate the full path. Here, we will use the latter:

# Replace '~/Desktop/workbench' with the actual path to 
#   the Connectome Workbench folder on your computer.
#   If successful, the path to the Workbench executable will be printed.
ciftiTools.setOption('wb_path', '~/Desktop/workbench')

In this vignette, we will use example data included in the ciftiTools package. The files are originally from NITRC:

• The "MyelinAndCorrThickness" dtseries and dscalar files contain the same data: "MyelinMap_BC_decurv" and "corrThickness". (A dtseries file usually contains fMRI BOLD timeseries data, but that would be too large to include in the package.)
• The dlabel file contains three cortical parcellations
• The "ones" dscalar file is the only file to include subcortical voxels. All its data values are 1.
• To reduce the size of ciftiTools, the dscalar and dlabel CIFTIs were resampled to 6k and the "ones" dscalar was resampled to 1k.

We will also use GIFTI files containing inflated surface geometry.

cifti_fnames <- ciftiTools.files()$cifti
surfL_fname <- ciftiTools.files()$surf["left"]
surfR_fname <- ciftiTools.files()$surf["right"]

Reading and Writing

Reading

CIFTI files organize the gray matter of the brain into "grayordinates": vertices representing the left and right cortical surfaces, and voxels representing the subcortical gray matter structures and the cerebellum. A CIFTI file consists of two parts: (1) an XML header which contains all the metadata including medial wall locations, subcortical structure labels, and the subcortical volumetric mask; and (2) a NIFTI format matrix representing all the grayordinate data. These components are read in together with read_cifti:

basename(cifti_fnames["dtseries"])
xii <- read_xifti(cifti_fnames["dtseries"])

The result of read_cifti is a "xifti" object. We can get an overview of it using its summary S3 method:

xii # same as `summary(xii)`

By default, read_cifti only reads in the left and right cortex data. The subcortical data can be included by using the argument brainstructures="all". Other brainstructure combinations can be specified too, e.g. brainstructures=c("left", "subcortical"). The full set of choices for brainstructures is any combination of "left", "right" and "subcortical", or "all" for all three.

"xifti" objects are lists with entries data (the grayordinate data matrix, separated by brainstructure), surf (surface geometry), and meta (metadata, most of which is from the NIFTI XML header). surf distinguishes a "xifti" from a CIFTI file: the left and right cortical surface geometries are not included in CIFTI files, so they must be read from separate surface GIFTI files (ending in surf.gii). The surface must be compatible: the number of vertices must be the same, and each vertex in the CIFTI data must correspond to the vertex location in the corresponding GIFTI surface file. In this way, a "xifti" represents a combination of a CIFTI file with compatible GIFTI files for the cortical mesh.

We can add GIFTI surface geometries with add_surf:

xii <- add_surf(xii, surfL=surfL_fname, surfR=surfR_fname)
xii

Alternatively, we could have provided the surface geometries at the outset of reading the CIFTI file:

xii2 <- read_xifti(cifti_fnames["dtseries"], surfL_fname=surfL_fname, surfR_fname=surfR_fname)
all.equal(xii, xii2) # same result

To only read the CIFTI header, use info_cifti. Let's read the header of the dscalar file:

xii_info <- ciftiTools::info_cifti(cifti_fnames["dscalar"])
str(xii_info, nchar.max=50) # shows header structure

To read in only certain columns of a CIFTI file, use the idx argument:

read_xifti(cifti_fnames["dtseries"], idx=2) # second column only

Writing

When a "xifti" object is written to files, the CIFTI components are placed in a CIFTI file and the surface geometries, if any, are placed in GIFTI files.

out_dir <- "output"

write_xifti(
  xii, 
  file.path(out_dir, "my_cifti.dtseries.nii"), 
  file.path(out_dir, "my_L.surf.gii"), file.path(out_dir, "my_R.surf.gii")
)

With separate_cifti, a CIFTI can be separated and written into its component parts: the cortical data can be written to GIFTI metric or label files, and the subcortical data can be written to a NIFTI file. In addition, any ROIs or labels will also be written to files. The files are automatically named unless a new file name is provided.

# Use default names for everything except left cortex
separated_fnames = separate_cifti(
  cifti_fnames["dscalar_ones"], brainstructures="all", 
  cortexL_fname="my_left_cortex.func.gii", write_dir = out_dir
)
# Files written to `out_dir`, or current working dir. if not specified
basename(separated_fnames)

Separated files can be read into R with the oro.nifti, RNifti, and gifti packages, and combined into a "xifti" object with as.xifti.

Visualization

The cortical plots in ciftiTools are made possible by the rgl package. To prepare the R Markdown document for knitting we need to do the following:

library(rgl)
rgl::setupKnitr()

# Sometimes the first OpenGL window does not render properly.
rgl::open3d(); rgl::close3d()

Now let's take a look!

Surface visualization

view_xifti_surface(xii) displays the cortical data on the surface mesh. This function has several primary arguments:

• color_mode specifies the nature of the data values: "sequential", "qualitative" and "diverging". If it is not provided, a default mode that makes sense for the data will be used.
• colors specifies the color palette to use. If it is not provided, a default palette that makes sense for the color_mode is used.
• idx controls which column(s) to display.
• widget and fname control the output type. If fname is not provided, an interactive plot is created: by default, an OpenGL window if the length of idx is one, and an embedded HTML widget if the length of idx is greater than one. widget can be used to override this default. On the other hand, if fname is provided, static image files (png) for each idx are created, unless fname ends in .html in which case an interactive html file will be saved. Lastly, both OpenGL windows and HTML widgets can be embedded in R Markdown documents for knitting; refer to the source code of this vignette to see how this works.
• surfL and surfR specify the surface geometry to plot the data on. If not provided, the surfaces in the "xifti" object is used. But if those are also unavailable, the "inflated" surfaces included in ciftiTools are used.

Let's see an example using each color_mode option. Note how the included surfaces are used in the first plot, but if none are present as in the second and third plots, the default surfaces are automatically used for visualization. (In our case, the included surfaces are the default surfaces.) We'll also make the second plot interactive by requesting display of two idx. Try clicking and dragging around the second plot to rotate, and scrolling to zoom in and out. Note that the first and third plots are OpenGL window snapshots, and that the second plot is an embedded HTML widget.

# Normally `cex.title` doesn't need to be set, as it defaults to a good choice.
#   But when knitting static images this way, the default becomes a bit too big
#   based on how knitting works.
view_xifti_surface(xii, idx=1, zlim=c(1,2), title='color_mode = "sequential"', cex.title=1.3)

xii <- read_cifti(cifti_fnames["dscalar"]) # no GIFTI included, so the default inflated surface is used.
view_xifti_surface(
  xii, idx=1:2, zlim=c(0,5), color_mode = "diverging",
  title='color_mode = "diverging"', cex.title=1.3
)

view_xifti_surface(
  read_cifti(cifti_fnames["dlabel"]), 
  # Interactively, a color legend that displays the label names will also be printed.
  legend_ncol=5, 
  title='color_mode = "qualitative"', cex.title=1.3
)

Volume visualization

view_xifti_volume(xii) displays the subcortical data in slices. To view interactively in an interactive session, set interactive=TRUE. By default, a series of slices is displayed overlaid on the MNI template. The orientation and numbers of slices can be adjusted. A .png or .pdf file can be written with fname. The same color arguments from view_xifti_surface work too: colors, color_mode and zlim.

# cifti_fnames["dscalar_ones"] is the only file with subcortical data
xii <- read_cifti(cifti_fnames["dscalar_ones"], brainstructures="subcortical")
view_xifti_volume(xii)

# For information only, since papaya viewer cannot be embedded in knitted file
view_xifti_volume(xii, interactive = TRUE)

The S3 method plot(xii) will display all the data present in the xifti using view_xifti_surface, view_xifti_volume, or both. If both plots are made, the color mode and range will be shared between the two plots.

Tips and tricks

NA values will be uncolored. You can use this to, for example, only color values meeting a certain threshold.

xii <- read_cifti(cifti_fnames["dscalar"])
# Convert to z-scores
xii <- scale_xifti(xii)
# Threshold
xii <- transform_xifti(xii, function(x){ifelse(x<2, NA, x)})
view_xifti_surface(
  xii, title='MyelinMap_BC_decurv: z > 2', 
  cex.title=1.3, zlim=c(2,3), NA_color="#505560"
)

Something to note is that the shadows from 3D rendering will darken colors in some folded regions of the brain. Another consideration is that the colors are smoothed continuously. In some situations, these default 3D shading properties may be undesirable. The shadows and material arguments can be used to adjust aspects of 3D shading.

view_xifti_surface(
  xii, title='MyelinMap_BC_decurv: z > 2, less shadows', 
  cex.title=1.3, zlim=c(2,3), NA_color="#505560",
  shadows=0
)

view_xifti_surface(
  xii, title='MyelinMap_BC_decurv: z > 2, exact colors', 
  cex.title=1.3, zlim=c(2,3), NA_color="#505560",
  material=list(lit=FALSE, smooth=FALSE)
)

Resampling and smoothing

Resampling

ciftiTools can resample CIFTI files to a lower resolution. Here, we resample the 32k dtseries file to 2k. (The number refers to the count of vertices on a single hemisphere.) We also provide the surfaces and resample them in conjunction.

resampled_xii_fname <- "my_new_resampled.dtseries.nii"
resampled_surfL_fname <- "my_resampled_surfL.surf.gii"
resampled_surfR_fname <- "my_resampled_surfR.surf.gii"

xii_2k <- resample_cifti(
  cifti_fnames["dtseries"], resampled_xii_fname,
  resamp_res = 2000,
  surfL_fname, surfR_fname,
  resampled_surfL_fname, resampled_surfR_fname,
  write_dir = out_dir
)
basename(xii_2k)

Resampling can also be performed while reading a CIFTI file into R.

read_cifti(cifti_fnames["dscalar"], resamp_res=2000)

Smoothing

Use smooth_cifti to perform smoothing. Like resampling, this function works on both CIFTI files and "xifti" objects.

smoothed_xii_fname <- "my_smoothed_cifti.dtseries.nii"

# Smoothing a CIFTI file
smooth_cifti(
  cifti_fnames["dtseries"], file.path(out_dir, smoothed_xii_fname),
  surf_FWHM=2, vol_FWHM=2,
  surfL_fname=surfL_fname, surfR_fname=surfR_fname,
  subcortical_zeroes_as_NA=TRUE
)

# Visualizing the smoothed file.
# Let's demonstrate the ability to use RColorBrewer palettes!
plot(
  read_cifti(file.path(out_dir, smoothed_xii_fname)), 
  surfL=surfL_fname, surfR=surfR_fname, 
  zlim=c(1,2), color_mode="diverging", colors="Spectral"
)

Manipulation & Math

You can treat the "xifti" as a data matrix with the base R functions as.matrix, nrow, and ncol.

xiiL <- read_xifti(cifti_fnames["dscalar"], brainstructures="left")
dim(as.matrix(xiiL))

apply_xifti applies a function along the rows or columns of a "xifti". The base R apply function also works on "xifti" objects. The difference is that when applying a function along the rows, the former will return a "xifti" whereas the latter will return a data matrix. For example, we can compute the mean at each data location like so:

gmeans <- apply_xifti(xiiL, 1, mean)
gmeans

And we can compute quantiles of each measurement like so:

cquants <- apply_xifti(xiiL, 2, quantile, c(.1, .5)) # Quantiles of each column
cquants

combine_xifti combines multiple "xifti"s with different brainstructures.

xiiR <- read_xifti(cifti_fnames["dscalar"], brainstructures="right")
xii <- combine_xifti(xiiL, xiiR)
xii

convert_xifti converts the intent of a "xifti".

convert_xifti(xii, "dtseries") # Convert from dscalar to dtseries (don't use it)

merge_xifti concatenates data matrices from multiple "xifti"s column-wise.

xii <- merge_xifti(xii, xii) # Columns are repeated twice now.

remove_xifti removes a brainstructure(s) from a "xifti".

xii <- remove_xifti(xii, "cortex_left") # Now only the right cortex data is included.
xii

select_xifti subsets or re-orders the columns of the data matrix of a "xifti".

xii$meta$cifti$names <- paste("Column", seq(4))
xii <- select_xifti(xii, c(4,3,2)) # Reverse column order & drop the first.
xii

S3 methods allow for univariate transformations of "xifti" objects as well as arithmetic operations of multiple "xifti" objects.

max(as.matrix(xii))
xii <- 1 - exp(xii) / (xii * 2 + 3)
max(as.matrix(xii))

Working with surfaces

ciftiTools also includes functionality for working with surface geometry GIFTI files separately from any data. Surfaces that are read in are "surf" objects:

# Reading
surf <- read_surf(surfL_fname)
surf

These can be written back to GIFTI files, visualized, and resampled. Resampling can be performed on the "surf" objects or the surface GIFTI files directly:

# Writing
write_surf_gifti(surf, file.path(out_dir, "my.L.surf"))

# Visualizing
plot(surf)

# Resample a `"surf"` object
surf <- resample_surf(surf, 2000, "left")
# Resample a GIFTI file
resample_gifti(surfL_fname, file.path(out_dir, "my.L.2k.surf.gii"), "left", resamp_res=2000)

As mentioned earlier, the inflated surface is included as a GIFTI file and can be accessed with ciftiTools.files(). It is used as default for visualizing data with view_xifti_surface. But two more surfaces are available through the function load_surf:

xii <- as.xifti(
  surfL = load_surf("left", "very inflated"),
  surfR = load_surf("right", "midthickness")
)
plot(xii, title = "Left very inflated | Right midthickness")

Lastly, let's demonstrate the ability to plot vertices and edges. (This can also be done when plotting "xifti" objects with data, using the same arguments.)

# Recall that surf was resampled to 2k
plot(surf, vertex_size=3, vertex_color="blue")

plot(surf, edge_color="black")

Mesh operations

ciftiTools includes several functions for operations that depend on the vertex adjacencies of the surface mesh.

edit_mask_surf can erode, dilate, or extract the border vertices of a binary mask. (Here we'll also demonstrate how to use view_comp to display multiple plots simultaneously, without the interactivity of a widget.)

xii <- load_parc("Schaefer_400")
xii <- remove_xifti(xii, "cortex_right")
parc_name <- rownames(xii$meta$cifti$labels[[1]])[4]
xii <- apply_xifti(xii, 1, function(x){ifelse(x==4, 1, 0)})
xii_d3 <- edit_mask_surf(
  xii$data$cortex_left[,1], mwall=rep(TRUE, 32492), 
  surf=load_surf("left"),
  do="dilate", depth=3
)
xii <- newdata_xifti(xii, cbind(as.matrix(xii), xii_d3))
xii <- convert_xifti(xii, "dlabel", colors="blue") # 17networks_LH_DefaultA_PCC_1

fnames <- paste0(c(tempfile(), tempfile(), tempfile()), ".png")
plot(xii, title=parc_name, idx=1, fname=fnames[1], legend_fname=fnames[3])
plot(xii, title="Dilated mask", idx=2, fname=fnames[2], legend_fname=fnames[3])
plt <- view_comp(fnames[seq(2)], fname=fnames[3], nrow=1)
knitr::include_graphics(plt, dpi=180)

even_vert_samp obtains a subset of vertices sampled evenly across the mesh. It works by downsampling the mesh and then identifying the original vertices closest to the vertices on the downsampled mesh.

# Load a left-hemisphere surface.
surfL <- load_surf()

# Get 500 vertices evenly sampled on the mesh.
v <- even_vert_samp(surfL, 500)

# Get a mask of the 32k vertices indicating which are in the sample.
v2 <- seq(nrow(surfL$vertices)) %in% v

# Make a xifti having "1" for in-mask vertices, "0" elsewhere.
xii <- as.xifti(as.matrix(v2*1), surfL=surfL)

plot(convert_xifti(xii, "dlabel"))

Working with parcellations

Loading included parcellations

ciftiTools includes several commonly-used parcellations which can be loaded in as a single-column dlabel "xifti" with load_parc. Refer to ?load_parc for their corresponding references. Any other parcellations can be read in with read_cifti.

parc <- load_parc("Schaefer_400")
# parc <- read_cifti("my_parcellation.dlabel.nii")

plot(parc)

parc_add_subcortex will add new label levels and "parcels" data for each subcortical structure in the MNI template.

# Before `parc_add_subcortex`
max(parc) # 400 parcels
summary(parc)

parc <- parc_add_subcortex(parc)
max(parc) # 400 cortex parcels + 19 subcortex parcels
summary(parc) # After `parc_add_subcortex`

Applying a parcellation to data

apply_parc will apply a function to one "xifti" across each parcel specified by another "xifti". A common example is computing the average timeseries of each parcel:

parc <- load_parc("Yeo_7")
parc_tab <- parc$meta$cifti$labels[[1]]
rbind(head(parc_tab), tail(parc_tab)) # Keys 0-51 (52 total)

dat <- read_cifti(ciftiTools.files()$cifti["dtseries"])
dim(dat)

pmean <- apply_parc(
  dat, parc, 
  FUN=colMeans, # get average timeseries
  na.rm=TRUE # medial wall vals in `dat` will be NA
)
dim(pmean) # 52 parcels x 2 data columns

For real timeseries data, cor(t(pmean)) would give the parcels x parcels functional connectivity matrix.

apply_parc also has an argument which converts the result to a "xifti" object. Each locations' value will be the value of its corresponding parcel.

# Obtain the min/max, quartile, and mean value
#   of the second data column, within each parcel.
psumm <- apply_parc(
  select_xifti(dat, idx=2),
  parc, mwall_value=0, 
  FUN=summary,
  return_as="xifti"
)

# Plot the median.
plot(psumm, idx=3, title=psumm$meta$cifti$names[3], colors="magma")

Citing ciftiTools

A citation for the package itself can be printed with:

citation("ciftiTools")

Refer to the README for citation information for the surfaces, parcellations, and other data included in ciftiTools, as well as the Connectome Workbench. Also check the DESCRIPTION file to get a list of R packages used, including rgl and papayar.

mandymejia/ciftiTools documentation built on Feb. 28, 2024, 11:20 a.m.