This vignette covers the advanced aesthetic refinement capabilities of ggbrain, allowing you to create publication-quality brain visualizations. We’ll cover image cleaning operations, custom color scales, combining multiple layers, adding region labels, and combining plots using patchwork.

Images used in this demo

The package includes several images for demonstration purposes:

# MNI 2009c anatomical underlay
underlay_2mm <- system.file("extdata", "mni_template_2009c_2mm.nii.gz", package = "ggbrain")

# Parametric modulator: entropy change following feedback in learning task
echange_overlay_2mm <- system.file("extdata", "echange_ptfce_fwep_0.05_2mm.nii.gz", package = "ggbrain")

# Signed reward prediction error following feedback 
pe_overlay_2mm <- system.file("extdata", "pe_ptfce_fwep_0.05_2mm.nii.gz", package = "ggbrain")

# Absolute reward prediction error
abspe_overlay_2mm <- system.file("extdata", "abspe_ptfce_fwep_0.05_2mm.nii.gz", package = "ggbrain")

# Schaefer 200-parcel atlas of cortex
schaefer200_atlas_2mm <- system.file("extdata", "Schaefer_200_7networks_2009c_2mm.nii.gz", package = "ggbrain")

# Labels for the Schaefer atlas
schaefer_labels <- read.csv(
  system.file("extdata", "Schaefer_200_7networks_labels.csv", package = "ggbrain"), 
  na.strings = c("", "NA")
) %>%
  rename(value = roi_num) # ggbrain requires a 'value' column for merging

Adjustments to the appearance of image layers

When rendering brain slices, particularly functional activation maps, the raw data often contains visual artifacts that can be distracting or aesthetically unappealing. ggbrain provides several tools for cleaning up the appearance of image layers.

Removing small specks (`remove_specks`)

At times, when slicing a given image (especially functional activations), it is possible that some clusters are very small, yielding small ‘specks’ on some rendered slices. These are visually unappealing and may merit removal. The remove_specks argument specifies the pixel threshold used to remove clusters smaller than a certain size. For example, remove_specks = 20 would remove any clusters smaller than 20 pixels in size from each slice on the rendered image.

base_plot <- ggbrain(bg_color = "gray80", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("z = 4", "z = 20")) +
  geom_brain(definition = "underlay")

# Without removing specks
p1 <- base_plot +
  geom_brain(definition = "overlay", fill_scale = scale_fill_distiller("z", palette = "Reds")) +
  render() + plot_annotation(title = "Without remove_specks")

# With remove_specks = 20
p2 <- base_plot +
  geom_brain(definition = "overlay", fill_scale = scale_fill_distiller("z", palette = "Reds"), 
             remove_specks = 20) +
  render() + plot_annotation(title = "With remove_specks = 20")

p1 / p2

Note that removing specks can, at the extreme, misrepresent the data, so use this feature judiciously.

Filling small holes (`fill_holes`)

Another common aesthetic issue is small holes inside clusters. These may reflect voxels that fall slightly below a statistical threshold. The fill_holes argument specifies the size of holes (in pixels) that should be filled on the rendered slices using nearest neighbor imputation.

# Without filling holes
p1 <- base_plot +
  geom_brain(definition = "overlay", fill_scale = scale_fill_distiller("z", palette = "Reds")) +
  render() + plot_annotation(title = "Without fill_holes")

# With fill_holes = 100
p2 <- base_plot +
  geom_brain(definition = "overlay", fill_scale = scale_fill_distiller("z", palette = "Reds"), 
             fill_holes = 100) +
  render() + plot_annotation(title = "With fill_holes = 100")

p1 / p2

For integer-valued or categorical images, the mode of the neighboring voxels is used for imputation rather than the mean.

Trimming threads (`trim_threads`)

The trim_threads argument iteratively removes pixels that have fewer than the specified number of neighboring pixels (including diagonals). This helps clean up thin “threads” or isolated pixels that can occur at the edges of clusters.

# Without trimming
p1 <- base_plot +
  geom_brain(definition = "overlay", fill_scale = scale_fill_distiller("z", palette = "Reds")) +
  render() + plot_annotation(title = "Without trim_threads")

# With trim_threads = TRUE (uses default of 3 neighbors)
p2 <- base_plot +
  geom_brain(definition = "overlay", fill_scale = scale_fill_distiller("z", palette = "Reds"), 
             trim_threads = TRUE) +
  render() + plot_annotation(title = "With trim_threads = TRUE")

p1 / p2

Combining image cleaning operations

For the best visual results, you can combine all three cleaning operations:

gg_clean <- base_plot +
  geom_brain(
    definition = "overlay", 
    fill_scale = scale_fill_distiller("z-stat", palette = "Reds"),
    remove_specks = 20,
    fill_holes = 100,
    trim_threads = TRUE
  )

plot(gg_clean)

Working with color scales

Custom color scales

You can use any ggplot2 scale_fill_* function to customize the color mapping:

gg_viridis <- ggbrain(bg_color = "gray80", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = echange_overlay_2mm)) +
  slices(c("x = 0", "y = 0", "z = 30")) +
  geom_brain(definition = "underlay") +
  geom_brain(
    definition = "overlay", 
    fill_scale = scale_fill_viridis_c("Entropy\nChange", option = "magma"),
    remove_specks = 15
  )

plot(gg_viridis)

Bisided color scales

For maps with both positive and negative values (e.g., signed prediction errors), use scale_fill_bisided() to create separate color scales:

gg_bisided <- ggbrain(bg_color = "gray80", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("x = 0", "y = 0", "z = 30")) +
  geom_brain(definition = "underlay") +
  geom_brain(
    definition = "overlay",
    fill_scale = scale_fill_bisided(
      name = "Signed PE",
      neg_scale = scale_fill_distiller(palette = "Blues", direction = 1),
      pos_scale = scale_fill_distiller(palette = "Reds"),
      symmetric = TRUE
    ),
    remove_specks = 20
  )

plot(gg_bisided)

Working with categorical images and labels

Mapping fills to label columns

When working with labeled images (e.g., atlases), you can map the fill color to label columns rather than numeric values. This requires adding labels to your image and specifying the appropriate aesthetic mapping:

# Create a subset of labels for Dorsal Attention Network
dan_labels <- schaefer_labels %>%
  filter(network == "DorsAttn") %>%
  mutate(
    dan_group = case_when(
      value %in% c(31, 32, 135, 136) ~ "MT+",
      value %in% c(33:40, 137:144) ~ "PPC",
      value %in% c(41:43, 145:147) ~ "Premotor"
    ),
    dan_group = ordered(dan_group, levels = c("MT+", "PPC", "Premotor"))
  )

# Define colors for DAN groups
dan_colors <- c("MT+" = "#E41A1C", "PPC" = "#377EB8", "Premotor" = "#4DAF4A")

gg_dan <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm)) +
  images(c(dan_atlas = schaefer200_atlas_2mm), labels = dan_labels) +
  slices(c("x = -30", "z = 50")) +
  geom_brain(
    definition = "underlay", 
    fill_scale = scale_fill_gradient(low = "grey30", high = "grey80"), 
    show_legend = FALSE
  ) +
  geom_brain(
    definition = "dan_atlas", 
    mapping = aes(fill = dan_group),
    fill_scale = scale_fill_manual("DAN Region", values = dan_colors),
    show_legend = TRUE,
    remove_specks = 10,
    fill_holes = 20
  )

plot(gg_dan)

Combining fills with outlines

A powerful technique is to combine filled regions with outlines to highlight different groupings. For example, you might fill regions by network and outline individual parcels:

gg_outlined <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm)) +
  images(c(dan_atlas = schaefer200_atlas_2mm), labels = dan_labels) +
  slices(c("x = -30", "z = 50")) +
  geom_brain(
    definition = "underlay", 
    fill_scale = scale_fill_gradient(low = "grey30", high = "grey80"), 
    show_legend = FALSE
  ) +
  geom_brain(
    definition = "dan_atlas", 
    mapping = aes(fill = dan_group),
    fill_scale = scale_fill_manual("DAN Region", values = dan_colors),
    show_legend = TRUE,
    remove_specks = 10,
    fill_holes = 20,
    unify_scales = TRUE
  ) +
  # Add outlines for individual Schaefer regions
  geom_outline(
    definition = "dan_atlas", 
    size = 1, 
    mapping = aes(group = MNI_Glasser_HCP_v1.0),
    outline = "cyan",
    remove_specks = 10
  )

plot(gg_outlined)

Adding region labels

Using geom_region_label_repel

The geom_region_label_repel() function adds text labels to regions, using ggrepel to prevent overlapping. This is extremely useful for labeling multiple regions:

# Add custom short labels for display
dan_labels_short <- dan_labels %>%
  mutate(
    short_label = sub("Anterior", "Ant.", MNI_Glasser_HCP_v1.0),
    short_label = sub("Posterior", "Post.", short_label),
    short_label = sub("Superior", "Sup.", short_label),
    short_label = sub("Inferior", "Inf.", short_label)
  )

gg_labeled <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm)) +
  images(c(dan_atlas = schaefer200_atlas_2mm), labels = dan_labels_short) +
  slices(c("x = -30", "z = 50")) +
  geom_brain(
    definition = "underlay", 
    fill_scale = scale_fill_gradient(low = "grey30", high = "grey80"), 
    show_legend = FALSE
  ) +
  geom_brain(
    definition = "dan_atlas", 
    mapping = aes(fill = dan_group),
    fill_scale = scale_fill_manual("DAN Region", values = dan_colors),
    show_legend = TRUE,
    remove_specks = 10,
    fill_holes = 20
  ) +
  geom_region_label_repel(
    image = "dan_atlas", 
    label_column = "value",
    min.segment.length = 0,
    size = 3,
    color = "black",
    force_pull = 0,
    force = 1.5,
    max.overlaps = Inf,
    box.padding = 0.5,
    label.padding = 0.15,
    min_px = 10  # Only label regions with at least 10 pixels on the slice
  )

plot(gg_labeled)

Adding annotations

Coordinate annotations

Use annotate_coordinates() to add slice coordinate labels to each panel:

gg_coords <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("x = 0", "y = 0", "z = 30")) +
  geom_brain(definition = "underlay") +
  geom_brain(
    definition = "overlay",
    fill_scale = scale_fill_bisided(),
    remove_specks = 15
  ) +
  annotate_coordinates(hjust = 1, color = "black", x = "right", y = "bottom", size = 3)

plot(gg_coords)

Orientation annotations

Use annotate_orientation() to add neurological orientation labels (L/R, A/P, S/I):

gg_orient <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("x = 0", "y = 0", "z = 30")) +
  geom_brain(definition = "underlay") +
  geom_brain(
    definition = "overlay",
    fill_scale = scale_fill_bisided(),
    remove_specks = 15
  ) +
  annotate_orientation(size = 4, color = "gray20")

plot(gg_orient)

Crosshair annotations

Use annotate_crosshairs() to drop crosshairs at specific world (xyz) coordinates. If the coordinate along the slicing axis is NA, the crosshair is shown on every slice for which the remaining axes match (within the nearest in-plane voxel). The tol argument (in mm) controls how closely a coordinate must match a slice along the slicing axis (default: 1 mm).

gg_cross <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("z = 20", "z = 40")) +
  geom_brain(definition = "underlay") +
  geom_brain(
    definition = "overlay",
    fill_scale = scale_fill_bisided(),
    remove_specks = 15
  ) +
  annotate_crosshairs(data.frame(x = 0, y = 0, z = NA), color = "white", linewidth = 0.6)

plot(gg_cross)

Creating reusable plot components

When creating multiple related plots, it’s efficient to define common components that can be reused. This is similar to creating a ‘theme’ for your brain plots:

# Define common plot elements
common_parts <- 
  images(c(underlay = underlay_2mm)) +
  geom_brain(
    definition = "underlay", 
    fill_scale = scale_fill_gradient(low = "grey30", high = "grey80"), 
    show_legend = FALSE
  ) +
  annotate_coordinates(hjust = 1, color = "black", x = "right", y = "bottom", size = 3)

Now you can use common_parts across multiple plots with different overlays or slice selections.

Combining ggbrain plots with patchwork

The patchwork package provides powerful tools for combining multiple plots. After rendering your ggbrain plots, you can use patchwork operators to arrange them.

Basic patchwork combinations

# Create two different brain plots
gg_axial <- ggbrain(bg_color = "gray90", text_color = "black", title = "Axial View") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("z = 20", "z = 40")) +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "overlay", fill_scale = scale_fill_bisided(), remove_specks = 20) +
  render()

gg_sagittal <- ggbrain(bg_color = "gray90", text_color = "black", title = "Sagittal View") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("x = -40", "x = 40")) +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "overlay", fill_scale = scale_fill_bisided(), remove_specks = 20) +
  render()

# Combine using patchwork
combined <- gg_axial / gg_sagittal +
  plot_annotation(
    title = "Reward Prediction Error Activation",
    tag_levels = "A",
    tag_suffix = ")",
    theme = theme(plot.title = element_text(size = 16, face = "bold"))
  ) &
  theme(plot.tag = element_text(face = "bold", size = 14))

combined

Advanced layout with shared legend

When combining multiple plots with similar color scales, you can extract and share a common legend:

# Create plots without legends
p1 <- ggbrain(bg_color = "gray90", text_color = "black", title = "Entropy Change") +
  images(c(underlay = underlay_2mm, overlay = echange_overlay_2mm)) +
  slices(c("x = 0", "z = 20")) +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "overlay", 
             fill_scale = scale_fill_viridis_c("z-stat"), 
             remove_specks = 15) +
  render()

p2 <- ggbrain(bg_color = "gray90", text_color = "black", title = "Prediction Error") +
  images(c(underlay = underlay_2mm, overlay = abspe_overlay_2mm)) +
  slices(c("x = 0", "z = 20")) +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "overlay", 
             fill_scale = scale_fill_viridis_c("z-stat"), 
             remove_specks = 15) +
  render()

# Combine with collected legends and tags
final_plot <- (p1 | p2) +
  plot_layout(guides = "collect") +
  plot_annotation(
    title = "Learning-Related Brain Activation",
    tag_levels = "A",
    theme = theme(plot.title = element_text(hjust = 0.5, size = 18, face = "bold"))
  )

final_plot

Saving ggbrain plots to file

To save your plots with consistent styling, use ggsave() or base R graphics devices. The bg argument ensures the background color extends to the full output device:

# Using ggsave (recommended)
ggsave("my_brain_plot.png", render(gg_labeled), width = 10, height = 6, dpi = 300, bg = "gray90")
ggsave("my_brain_plot.pdf", render(gg_labeled), width = 10, height = 6)

# Using base R devices
pdf("my_brain_plot.pdf", width = 10, height = 6, bg = "gray90")
plot(gg_labeled)
dev.off()

png("my_brain_plot.png", width = 10, height = 6, units = "in", res = 300, bg = "gray90")
plot(gg_labeled)
dev.off()

Filtering images for focused displays

You can filter an image to display only certain regions using the filter argument in images(). This is useful when you want to focus on specific ROIs or networks:

# Get specific parcel values for Premotor regions
premotor_parcels <- dan_labels %>%
  filter(dan_group == "Premotor") %>%
  pull(value)

# Create focused display of just Premotor regions
gg_premotor <- ggbrain(bg_color = "gray90", text_color = "black", title = "Premotor Regions") +
  images(c(underlay = underlay_2mm)) +
  images(
    c(premotor_atlas = schaefer200_atlas_2mm), 
    labels = dan_labels, 
    filter = premotor_parcels  # Only include Premotor parcels
  ) +
  slices(c("x = -45", "z = 50", "x = 50")) +
  geom_brain(
    definition = "underlay", 
    fill_scale = scale_fill_gradient(low = "grey30", high = "grey80"), 
    show_legend = FALSE
  ) +
  geom_brain(
    definition = "premotor_atlas", 
    mapping = aes(fill = dan_group),
    fill_scale = scale_fill_manual("Region", values = c("Premotor" = "#4DAF4A")),
    show_legend = TRUE,
    remove_specks = 10
  ) +
  annotate_coordinates(hjust = 1, color = "black", x = "right", y = "bottom", size = 3)

plot(gg_premotor)

Target resolution for higher quality displays

Use target_resolution() to upsample your display for smoother, higher-resolution figures:

# Without upsampling (native 2mm resolution)
p1 <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("z = 30")) +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "overlay", fill_scale = scale_fill_bisided(), remove_specks = 15) +
  render() + plot_annotation(title = "Native 2mm resolution")

# With upsampling to 1mm
p2 <- ggbrain(bg_color = "gray90", text_color = "black") +
  images(c(underlay = underlay_2mm, overlay = pe_overlay_2mm)) +
  slices(c("z = 30")) +
  target_resolution(1.0, interpolation = "cubic") +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "overlay", fill_scale = scale_fill_bisided(), remove_specks = 15) +
  render() + plot_annotation(title = "Upsampled to 1mm with cubic interpolation")

p1 | p2

Other considerations

Resampling images externally

Users could also consider resampling images externally using tools like AFNI’s 3dresample before loading into ggbrain. This will yield similar results to te

3dresample -input functional_overlay_3mm.nii.gz \
  -prefix functional_overlay_1mm.nii.gz \
  -master anatomical_template_1mm.nii.gz \
  -rmode NN

The hierarchy of a ggbrain plot

Every ggbrain plot is composed of:

Plot: The overall container for the visualization
- Panels (Slices): Each showing a 2D slice along one axis (x/sagittal, y/coronal, z/axial)
  - Layers: Raster data mapped to fill colors (underlays, overlays, atlas regions)
  - Outlines: Boundaries of clusters or regions
  - Annotations: Text or geometric marks (coordinates, orientation labels)
  - Region Labels: Text labels positioned at region centroids

Understanding this hierarchy helps when building complex, multi-layered visualizations.

Tips for publication-quality figures

Use consistent background colors across all panels and in your output device
Clean up specks and fill holes to improve visual appearance
Choose appropriate color scales - consider colorblind-friendly palettes
Add orientation and coordinate labels for interpretability
Use patchwork tags (A, B, C…) for multi-panel figures
Save as vector formats (PDF, SVG) for maximum quality and editability
Consider upsampling for smoother appearance in final figures

Aesthetic refinements of ggbrain plots