In neuroconductor/fsbrain: Managing and Visualizing Brain Surface Data

Input data

Q: What kind of input data do I need for fsbrain?

The fsbrain software is designed to be used with the output of FreeSurfer. Running recon-all on your T1w MRI scan results in a directory structure full of different files and file types for each subject. The fsbrain library uses knowledge on this directory layout to load the proper data.

However, while designed primarily with FreeSurfer in mind, fsbrain is not limited to FreeSurfer output, see below.

Q: Which file formats are supported?

The fsbrain library uses freesurferformats to load a variety of neuroimaging file formats, including data exchange formats used by other brain imaging software. See the freesurferformats website for the full list.

Q: I want to load a single file that is somewhere on my harddisk, i.e., not within a standard recon-all output directory structure. How can I load it?

You can use freesurferformats directly to load the data, then pass it to fsbrain. See the next question for an example.

Q: Can I use fsbrain to visualize data from SPM12 / CAT12 or other software packages?

Yes, the computational anatomy toolbox (CAT12) for SPM writes surfaces in GIFTI format and the morphometry data in curv format, both formats are supported by fsbrain. After running CAT12 surface measure computation on your subject subject1, you should have the following files in the surf/ subdir:

• lh.central.subject1.gii
• lh.gyrification.subject1

Try the following to visualize the gyrification data for the left hemisphere in fsbrain:

lh_surf = freesurferformats::read_nisurface('~/data/subject1_spm12/surf/lh.central.subject1.gii');
lh_gyrification = freesurferformats::read.fs.curv('~/data/subject1_spm12/surf/lh.gyrification.subject1');
vis.data.on.subject('~/data/', 'subject1_spm12', lh_gyrification, NULL, surface=lh_surf);

You should be able to load data from a number of different neuroimaging software packages with freesurferformats, as it supports the very common NIFTI and GIFTI file formats.

Visualization

How can I set the output image resolution?

To increase the output resolution, you need to increase the size of the rgl rendering device. To do this globally, before you call any fsbrain rendering function:

fsbrain.set.default.figsize(1200, 1200);

Alternatively, you can control the size when calling an fsbrain visualization function by passing the same information in the optional rgloptions parameter, like this:

rgloptions = list('windowRect'=c(20, 20, 1800, 1200));
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', rgloptions=rgloptions);

How can I save high quality figures to PNG images?

Note that fsbrain renders images, which means the output is pixel-based (i.e., bitmap as opposed to vector graphics). To get high quality output, you need to increase the size of the rgl rendering device, as explained in the last question.

To save the plot to a file in PNG format, you can use an rglaction:

rgla = list('snapshot_png'='~/subject1_thickness.png');
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', rglactions=rgla);

This opens the plot in a window as usual and also saves it in PNG format to the file subject1_thickness.png in your home directory.

When visualizing data with outliers and long tails, the colors are not helpful. What to do?

If you have data with very few but extreme outliers, almost all of your plot will have a single color. This happens for example when plotting curvature data. You can of course first load the data using subject.morph.native, adjust it (transform it, remove outliers), and then plot it using vis.data.on.subject.

In many cases, it is easier to use rglactions to clip the data to the 5th to 95th percentile, which can be done with an rglaction. E.g.:

rgla = list('trans_fun'=clip.data);
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'curv', rglactions=rgla);

Can I limit the data range, including the min and max values displayed on the color bar?

Yes, this can be achieved in different ways:

Option 1: Use rglactions in combination with limit_fun:

rgla = list('trans_fun'=limit_fun(2,3));
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', rglactions = rgla);

This will limit your data to the range 2 to 3.

If you want the data values outside the given range to be plotted as background (in the color for NA values), use limit_fun_na instead of limit_fun.

Option 2: If you want more flexibility, e.g., you also need support for a larger data range than the real data range of your subject, e.g., when plotting group data to ensure that all subjects use the same colors for identical values, you should use the 'range' entry in 'makecmap_options' instead:

makecmap_options = list('range'=c(0,6));
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', makecmap_options = makecmap_options);

How can I change the colormap for the plots?

Pass a colormap function to any visualization function that supports the makecmap_options parameter, as entry colFn like illustrated below:

makecmap_options = list('colFn'=viridis::viridis);
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', makecmap_options=makecmap_options);

In that example, we used the popular viridis colormap. In R, it is available from the viridis package. If you don't have it, you can install it with:

install.packages('viridis');

Of course, you can use any colormap function you want, currently the only limitation is that it should accept an integer parameter: the requested number of colors.

The exact number of colors that will be requested depends on your data, and if the colormap you want only supports very few colors, you can use a wrapper function to interpolate. Here is an example for the very popular RColorBrewer package. Some of its colormaps have less than 10 colors, which is usually not enough for neuroimaging data. Here we wrap the 'Blues' palette, which has 9 colors:

colFn_many_blues = colorRampPalette(RColorBrewer::brewer.pal(9, name="Blues"));
makecmap_options = list('colFn'=colFn_many_blues);
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', makecmap_options=makecmap_options);

What are the best colormaps for my data?

Choosing a colormap can be a surprisingly complex question and there are many publications which discuss this topic. You may want to consider what kind of data you have and what property you want to highlight, how many colors you need, whether you want colorblind-friendly colors, how they look when printed in gray-scale, whether they look pleasing to you, and maybe many more dimensions.

The most important thing is to decide is whether you need a sequential, qualitative, or diverging palette for your data.

I am definitely not an expert, but here are some color functions I personally like and use with fsbrain:

colFn_sequential = viridis::viridis;
colFn_qualitative = function(n) { RColorBrewer::brewer.pal(n, name="Set2"); } # n <= 11
colFn_diverging = grDevices::colorRampPalette(RColorBrewer::brewer.pal(11, name="RdBu"));

They require the viridis and RColorBrewer packages to be installed. The qualitative map is fine if you do not have more than 11 different values.

If you have R >= 3.6, you may not need any extra packages: have a look at the grDevices::hcl.colors function. Here are some suggestions:

colFn_sequential = function(n) { grDevices::hcl.colors(n, palette = "viridis"); }
colFn_qualitative = function(n) { grDevices::hcl.colors(n, palette = "Dark 3"); }
colFn_diverging = function(n) { grDevices::hcl.colors(n, palette = "Blue-Red 3"); }

If you want a heatmap-style colormap (single hue sequential, yellow/red), try:

colFn_sequential_heat = function(n) { grDevices::hcl.colors(n, "YlOrRd"); }

Make sure to read the next entry as well if you are using a diverging colormap.

I am using a diverging colormap. When plotting the colorbar (legend), the neutral value is not at zero. How to fix it?

When using a diverging colormap, make sure to set the symm option to makecmap_options when using a visualization function, like this:

makecmap_options = list('colFn'=colFn_diverging, 'symm'=TRUE);

This ensures that the neutral color of the diverging colormap (usually white) is aligned with the zero mark in the colorbar/legend, by adapting the value range displayed on the colorbar.

The colorbar shows only very few different colors, why is that and how can I change it?

The impression that the numbers of colors in the colorbar is lower than in the rendered image is a consequence of the rendering process: the lighting (shadows, highlights) and the material properties (glossyness, partial transparency) have an effect on the appearance of colors in the rendered image.

You can set the parameter n in the makecmap_options (see above) to request more colors, which will lead to a smooth colorbar.

mkc = list('n'=200L);
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', makecmap_options=mkc);

This also means that more colors are used in the rendered image, but the effect will be less noticable.

Can I set a custom color for NA values in the plot, like the masked medial wall?

By default, NA values are rendered in white. You can change this using col.na in makecmap_options:

mkc = list('n'=200L, 'col.na'='orange');
vis.subject.morph.native('~/mysubjects_dir', 'subject1', 'thickness', cortex_only = TRUE, makecmap_options=mkc);

This is also useful when plotting clusters, just set all values which are not part of any cluster to NA.

Can I get a color legend when plotting atlas regions from an annotation? I want to know the region names for the colors.

Yes, see the answer to the next question for details.

Can I get a color legend when plotting a segmentation/parcellation (different brain regions), based on a color lookup table (like the FreeSurferColorLUT.txt file)?

Yes, use the vis.colortable.legend function. You can pass an annotation or a color lookup table, and it will create a plot that shows the colors and the structure (or region) names. The output will be a separate plot, so you can use standard R methods to save it in vector formats like PDF for best quality.

Hint: you can load a color lookup table with freesurferformats::read.fs.colortable.

There is too much whitespace between the different views of the brain. How can I reduce it?

While this is not possible in rgl, fsbrain provides the vislayout.from.coloredmeshes function to achieve this using Image Magick. You need to have the suggested 'magick' package installed for this to work. The function renders separate images, crops the output figures to remove the background, then merges the seperate cropped images into a final output image and saves it as a PNG file. Here is a usage example:

# To get coloredmeshes return value only, ignore the visualization:
cm = vis.subject.morph.native(sjd, sj, 'thickness', makecmap_options = list('n'=200), cortex_only = T);
# Produce high quality tight layout:
vislayout.from.coloredmeshes(cm);

Note that your output resolution settings (see question above) now count for each of the single images. This means that you will get quite high resolution output in combination with the tight layout. This makes the function ideal for producing plots for publications.

You can adjust various settings, e.g., change the rendering style, select different views, and save it to a custom file name in your home directory:

output_brain_img = "fsbrain_arranged.png";
vislayout.from.coloredmeshes(cm, view_angles = get.view.angle.names(angle_set='t9'), output_img = output_brain_img);

It is also possible to plot a separate colorbar image and combine that with the tight layout brainview image. Note that the settings for the colorbar are stored in the coloredmeshes, and can be adjusted by altering the initial call to vis.subject.morph.native (or whatever visualization function you use) above.

output_cbar_img = "fsbrain_cbar.png";
output_final_img = "fsbrain_merged.png";
coloredmesh.plot.colorbar.separate(cm, image.plot_extra_options = list('horizontal' = TRUE), png_options = list('filename'=output_cbar_img, 'width'=1800));
combine.colorbar.with.brainview.image(output_brain_img, output_cbar_img, output_final_img);

You may have to play a bit with the resolution settings of your brain images and the colorbar to get this right (the background cropping makes it hard to compute the exact values in advance).

You should also have a look at the new vis.export.from.coloredmeshes function.

When visualizing data on the inflated surface, the left and right hemisphere overlap and look garbled. How to fix that?

This happens due to the inflation. One can use the rglactions parameter to push the hemis apart. By default, it pushes them apart by the amount they overlap:

vis.subject.morph.native(sjd, sj, 'thickness', rglactions = list('shift_hemis_apart'=TRUE), surface='inflated', views='si');

You can also set a distance manually:

vis.subject.morph.native(sjd, sj, 'thickness', rglactions = list('shift_hemis_apart'=list('min_dist'=20)), surface='inflated', views='si');

If you need more customization options, have a look at the shift.hemis.apart function. If you pass a named list in the rglactions for 'shift_hemis_apart' (like in the previous example), the entries are passed on to that function.

Various topics

Can I integrate the plots produced by fsbrain into an R Markdown document (the R equivalent of a Python Jupyter notebook)?

Yes, see the example notebook files in the directory web of the fsbrain repository. The Rmd files are actually notebooks in R markdown format.

Can I use fsbrain to visualize arbitrary triangular meshes in R?

Yes, fsbrain is not limited to brain surface meshes, and a wide array of mesh file formats are supported. Keep in mind though that fsbrain works with triangular meshes. To visualize a mesh from a file, the easiest way is to use vis.fs.surface:

vis.fs.surface('~/Documents/my_mesh.ply');

Technical problems

I am getting the error 'invalid value specified for graphical parameter plt', why?

The full error most likely looks like this, or similar:

Error in par(new = TRUE, pty = "m", plt = smallplot, err = -1) : 
  invalid value specified for graphical parameter "plt"

If you experience this error, it most likely happened when you tried to plot something with a colorbar, and did not increase the device size (image resolution). The error occurs if there is no space for the colorbar plot, and the solution is to increase the resolution as explained in the answer to the question 'How can I set the output image resolution?' above.

I am getting the error 'Error in squash::cmap, found x values outside map range'

This can happen when visualizing morphometry data with function like vis.subject.morph.native. The full error most likely looks like this, or similar:

vis.subject.morph.native(sjd, sj, 'your_measure_here')
Error in squash::cmap(lh_morph_data, map = common_cmap) : 
  Found 2193 values outside map range.

Most likely you have Inf values in your data, most likely in the medial wall, just try plotting without it:

vis.subject.morph.native(sjd, sj, 'your_measure_here', cortex_only = T)

If this does not help, load the data and inspect it manually.

I am getting the warning 'RGL: unable to open X11 display' when loading fsbrain

The full message most likely looks like this:

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'.

This happens if you do not have X11, or no window can be opened. Possible reasons include that you are running R on a remote host using an SSH connection without X11 forwarding, or that you do not have XQuartz installed under MacOS.

I am trying to plot a very large image with fsbrain, but the output is not as large as expected. Why?

The output dimensions are limited by your screen resolution.

What to do about the error 'lazy-load database is corrupt' when loading fsbrain?

If you install the development version from github and try to load it without restarting R, you ma^y get this error:

library("fsbrain")
Error: package or namespace load failed for ‘fsbrain’ in get(method, envir = home):
lazy-load database '/Users/youruser/Library/R/3.6/library/fsbrain/R/fsbrain.rdb' is corrupt

To fix this, simply restart R.

neuroconductor/fsbrain documentation built on Sept. 16, 2020, 2:30 p.m.