This vignette provides an overview of basic usage of the ggbrain package. The package seeks to generate 2D volumetric plots of brain data stored in the NIfTI format. Additional details about the structure of NIfTI data are here: The ggbrain package uses ggplot2 to generate its plots, allowing users to access the wide array of popular features in ggplot2, including annotations and fine control over coloration and themes.

Note that this package is unrelated to the ggBrain package developed by Aaron Fisher in 2014. I had developed most of the current ggbrain package and had named it prior to discovering Aaron’s work. The earlier ggBrain package was also not released publicly to CRAN and has not been updated much over the years, whereas ggbrain is hosted on CRAN and also offers a wider array of functionality.

1 Images that will be used in the demo

The package includes a few images that can be used for demonstrating its capabilities. The locations of the images on your machine are listed below.

print(c(underlay_3mm, decision_onset_3mm, echange_overlay_3mm, pe_overlay_3mm, abspe_overlay_3mm))
## [1] "/private/var/folders/y0/pkh_h_511bn27zjj8m0w2wx00000gn/T/RtmpAD6adc/Rinsta14a21951453/ggbrain/extdata/mni_template_2009c_3mm.nii.gz"   
## [2] "/private/var/folders/y0/pkh_h_511bn27zjj8m0w2wx00000gn/T/RtmpAD6adc/Rinsta14a21951453/ggbrain/extdata/decision_onset_zstat_3mm.nii.gz" 
## [3] "/private/var/folders/y0/pkh_h_511bn27zjj8m0w2wx00000gn/T/RtmpAD6adc/Rinsta14a21951453/ggbrain/extdata/echange_overall_zstat_3mm.nii.gz"
## [4] "/private/var/folders/y0/pkh_h_511bn27zjj8m0w2wx00000gn/T/RtmpAD6adc/Rinsta14a21951453/ggbrain/extdata/pe_ptfce_fwep_0.05.nii.gz"       
## [5] "/private/var/folders/y0/pkh_h_511bn27zjj8m0w2wx00000gn/T/RtmpAD6adc/Rinsta14a21951453/ggbrain/extdata/abspe_ptfce_fwep_0.05.nii.gz"

2 Installing the package

You can install the latest public version of the ggbrain package using install.packages():


You can also to install the development version. It may may have new features and bugfixes, though it may also have new bugs or include works in progress.


3 Building a ggbrain plot layer by layer

One can create an empty ggbrain object using the ggbrain function. This is akin to creating an empty ggplot object using ggplot().

gg_obj <- ggbrain()

At this point, trying to plot the ggbrain yields a warning given that no data or geometric layers have been added.

## Warning in self$render(guides): No brain layers added to this object yet. Use +
## geom_brain() or + geom_outline() to add.

Consistent with the ggplot2 approach, additional elements can be added using the + operator, as we elaborate below. The main thing to know at this point is that you need to add all ggbrain-specific elements before adding typical ggplot components such as theme().

Also note that the order of the addition operations for images and slices (images and slices, respectively) is not important. The example order below – adding images, slices, layers, and labels – is conceptually intuitive, but could be reordered without altering the plut. As in ggplot2, the order of all geom_* layers does matter, such that geom_* layers that are added later are rendered on top of layers added earlier. For example, the geom_brain() layer for an underlay image should be added before an overlay.

3.1 Add images

A ggbrain plot must have at least one 3D image stored in NIfTI format. These images are read into ggbrain using the RNifti package. All images for a given plot must have the same image dimensions (otherwise, an error will occur). At present, ggbrain provides no internal support for interpolation to combine images with different resolutions. We encourage you to use 3dresample from AFNI for this purpose (see details in the ‘Other considerations’ section below).

The file names containing relevant images can be passed in as arguments to ggbrain or they can be appended using images() In either, it is important to provide a name for the image that is used internally to refer to the image in contrasts, labels, or other operations. If you do not provide a name, you will see a warning:

Warning message:
In private$set_images(images, fill_holes, clean_specks) :
  The images vector does not contain any names. This may lead to weird behaviors downstream if 'underlay' and 'overlay' are requested.
gg_obj <- gg_obj + images(c(underlay = underlay_3mm))

Note that the image name of ‘underlay’ has a special meaning in the package. Specifically, using this name will set a default color scale that is grayscale and will not show the legend of this scale in the plot. You don’t have to use ‘underlay’ but it can make things a bit simpler if you have a conventional plot that shows an anatomical scan underneath functional activations.

You can add many images at once by providing a named vector:

gg_obj <- ggbrain() + 
  images(c(underlay = underlay_3mm, echange = echange_overlay_3mm))

Or you can add many images in independent images steps. Note that the order of addition is not important.

gg_obj <- ggbrain() +
  images(c(underlay = underlay_3mm)) +
  images(c(echange = echange_overlay_3mm))

3.2 Adding slices

After adding images to the ggbrain object, the next step is to specify which slices should be displayed on the plot. This is achieved using the slices() function. Following standard neuroimaging terminology, we can add axial, sagittal, and coronal slices.

The order of the addition operations, whether within a single slices() call or in a sequence of calls like slices("x=12") + slices(c("y=15", "z=10")), determines the order of panels on the rendered plot from upper left to lower right (following the standard facet_wrap() conventions in ggplot2). Thus, consider the order in which you wish the panels to appear so that the slice additions follow this order.

3.2.1 Adding slices by spatial position

The most conventional approach is add slices based on their spatial position along one of three cardinal axes, x/sagittal, y/coronal, or z/axial. You can determine the locations of interest using an interactive image viewer such as afni or fsleyes. Or, as described below, you could add a montage of images, add the coordinate labels on the panels, then choose the coordinates of the slices that are most relevant.

You can add one or more slices using slices() by passing a character vector of positions.

gg_obj <- gg_obj + slices(c("x = 10", "y = 70", "z = 15"))

This will add one sagittal slice (x=10), one coronal (y=70), and one axis (z=15).

3.2.2 Adding slices by quantile

If you want to use standardized coordinates in terms of percentiles, rather than spatial coordinates, you can append % at the end of the slices() specification. This will find the position of the corresponding slice along the axis between 0% and 100%. These percentiles are computed based on the slices that are not entirely empty (i.e., not all zero).

gg_obj <- gg_obj + slices(c("x = 25%", "y = 50%", "z = 90%"))

3.2.3 Adding slices with additional aesthetic details

If you would like to alter specific aesthetic elements of the slices such as the panel title, border, or axis labels, these can be added as arguments to slices(). Aesthetic arguments that will alter the appearance of the relevant slices include:

  • title: The panel title
  • bg_color: The background color of the panel
  • text_color: The color of text on the panel
  • border_color: The color of a border around the panel
  • border_size: The size (thickness) of the border around the panel. Default is NULL (none).
  • xlab: The x axis label of the panel
  • ylab: The y axis label of the panel
  • theme_custom: A ggplot2 theme() (or list of theme calls) added to this panel that customizes its appearance.
gg_obj <- gg_obj +
    c("x = 25%", "x = 50%"),
    bg_color = "gray25", xlab = "x axis", theme_custom = theme(axis.text.x = element_text(size = 24))

3.2.4 Adding montages

If you would like a set of slices along one of the image planes to form a montage, you can use the montage() convenience function to specify the inputs to slices(). The arguments are

  • plane: “sagittal”, “coronal”, or “axial” (“x”, “y”, and “z” also work)
  • n: the number of slices to render along this plane
  • min: the lowest quantile to be included (between 0 and 1). Default: 0.1
  • max: the highest quantile to be included (between 0 and 1). Default: 0.9
  • min_coord: the lowest spatial position to be included. Mutually exclusive with quantile approach. Default: NULL
  • max_coord: the highest spatial position to be included. Mutually exclusive with quantile approach. Default: NULL

The code below adds 10 axial slices between the 10th and 90th percentiles.

gg_obj <- gg_obj + slices(montage("axial", 10, min = 0.1, max = 0.8))

This code adds 5 sagittal slices between the x = -10 and x = 10 (millimeters, in image coordinates).

gg_obj <- gg_obj + slices(montage("sagittal", 5, min_coord = -10, max_coord = 10))

3.3 Adding brain overlay layers to a plot

Now that we have defined the images to read and the slices to render, we need to map the numeric values in the slices to the points on the plot. This is achieved by geom_brain(), which is essentially an extension of geom_raster() from ggplot2. The numeric values in the slices fill squares on the plot, essentially forming ‘pixels’.

As with standard ggplot2 objects, the fill color of these pixels is determined by a scale_fill_* scale, allowing you to have fine control over the coloration.

Extending geom_raster(), the geom_brain() layer supports the following standards:

  • interpolate: if TRUE, geom_raster smooths the layer, rather than rendering discrete squares.
  • alpha: A number between 0 and 1 controlling the alpha transparency of the layer
  • show_legend: If TRUE (default), a legend mapping the numeric values to a color scale is included in the panel
  • fill: A color to be used to fill all non-NA pixels. In ggplot2 terms, this is ‘setting’ the color of all pixels, as opposed to ‘mapping’ the color to the numeric value via a scale.
  • mapping: The aesthetic mapping between the data and the raster layer. In general, the default of aes(fill=value) should be retained for parametric layers (i.e., where the color of pixels should map to a continuous scale). But as detailed in a subsequent section, the fill can mapped to label columns in a categorical image (e.g., atlas).

Relative to standard ggplot2 conventions, the use of geom_brain() requires that you specify details about the fill color scale when the layer is added. This results from the fact that ggbrain objects re-use the ‘fill’ geometry when layers are overlaid. For example, if you have an anatomical underlay with a grayscale fill and a statistical overlay with a color fill, each layer needs to have its own fill scale (with a scale_fill_* object). This is achieved internally using the ggnewscale package.

The ‘take-home message’ is that you need to specify fill scale information as part of the geom_brain() call. Here are the key arguments:

  • limits: a two-element vector that if provided, sets the upper and lower bounds on the scale
  • breaks: a function to draw the breaks on the scale. The default is integer_breaks(), which draws a few integer-valued breaks (usually, 5) between the min and max of the scale.
  • unify_scale: if TRUE (default), the color scale across panels (slices) will be equated. If FALSE, each panel will apply the color scale independently such that the min and max are slice-dependent.
  • fill_scale: a scale_fill_* object use for mapping the numeric values in the data to the fill channel on the plot. Standard scales such as scale_fill_viridis_c() can be used for continuous layers. Note that the package also provides a scale_fill_bisided() option that, if added, creates separate color scales for the positive and negative pixels on the plot. This is a conventional approach in displays of fMRI activation maps where, for example, positive modulation is mapped to warm colors (reds) while negative modulation is mapped to cool colors (blues). Note that scale_fill_bisided() has arguments of neg_scale and pos_scale that allow the scales used for negative and positive values, respectively, to be specified directly.

3.3.1 Defining an image layer

The geom_brain() function includes an argument called definition. This controls the numeric values that are selected for display on the layer. The simplest definition is to name the image to be displayed. Recall that when you use images(), you provide names for the images. These are used as a reference by ggbrain to interpret your layer definitions.

For example, here I want to render the underlay and overlay layers with their numeric values:

# define a reusable object that has the same images and slices
gg_base <- ggbrain(bg_color = "gray80", text_color = "black") +
  images(c(underlay = underlay_3mm, overlay = echange_overlay_3mm)) +
  slices(c("x = 10", "y = 70", "z = 15"))
gg_obj <- gg_base +
  geom_brain(definition = "underlay", fill_scale = scale_fill_gradient(low = "grey8", high = "grey62"), show_legend = FALSE) +
  geom_brain(definition = "overlay", fill_scale = scale_fill_bisided(), show_legend = TRUE)

Now that we have geom_* layers in our ggbrain object, we can finally plot our figure to see the result.


3.3.2 Defining subset layers

Oftentimes, we wish to modify the display by selecting a subset of values to display. One of the most common applications is thresholding of voxelwise statistics so that small/nonsignificant values are omitted. To achieve this, geom_brain() supports standard subsetting syntax used in R. Here is an example of thresholding the overlay layer so that the absolute (z) statistic is above 2.5:

gg_obj <- gg_base +
  geom_brain(definition = "overlay[abs(overlay) > 2.5]", fill_scale = scale_fill_bisided(), show_legend = TRUE)


Note how ‘overlay’ is treated as an R object that is subset using square brackets. The subsetting expression inside the brackets uses standard R syntax and can reference other images currently in the ggbrain object. For example, you could display overlay values only where the underlay is greater than 10:

gg_obj <- gg_base +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "overlay[underlay > 10]", fill_scale = scale_fill_bisided(), show_legend = TRUE)