Getting started with ggseg

If you work with neuroimaging data, you’ve probably spent time wrestling region-level results into brain plots. ggseg handles that plumbing for you. It stores brain atlas geometries and plots them as ggplot2 layers, so you get brain figures with the same code you’d use for any other ggplot.

library(ggseg)
library(ggplot2)

What’s in a brain atlas

A brain_atlas object bundles four things: a name, a type (cortical, subcortical, or tract), region geometry, and an optional colour palette.

dk()
#> 
#> ── dk ggseg atlas ──────────────────────────────────────────────────────────────
#> Type: cortical
#> Regions: 35
#> Hemispheres: left, right
#> Views: inferior, lateral, superior, medial
#> Palette: ✔
#> Rendering: ✔ ggseg
#> ✔ ggseg3d (vertices)
#> ────────────────────────────────────────────────────────────────────────────────
#>    hemi                            region                      label
#> 1  left banks of superior temporal sulcus                lh_bankssts
#> 2  left         caudal anterior cingulate lh_caudalanteriorcingulate
#> 3  left             caudal middle frontal     lh_caudalmiddlefrontal
#> 4  left                   corpus callosum          lh_corpuscallosum
#> 5  left                            cuneus                  lh_cuneus
#> 6  left                        entorhinal              lh_entorhinal
#> 7  left                          fusiform                lh_fusiform
#> 8  left                 inferior parietal        lh_inferiorparietal
#> 9  left                 inferior temporal        lh_inferiortemporal
#> 10 left                 isthmus cingulate        lh_isthmuscingulate
#>            lobe
#> 1      temporal
#> 2     cingulate
#> 3       frontal
#> 4  white matter
#> 5     occipital
#> 6      temporal
#> 7      temporal
#> 8      parietal
#> 9      temporal
#> 10    cingulate
#> ... with 60 more rows

The dk atlas (Desikan-Killiany) ships with the package. Call plot() for a quick look:

plot(dk())

Quick overview of the Desikan-Killiany cortical atlas.

Two helpers pull out the names you’ll need for matching data:

library(ggseg.formats)

atlas_regions(dk())
#>  [1] "banks of superior temporal sulcus" "caudal anterior cingulate"        
#>  [3] "caudal middle frontal"             "corpus callosum"                  
#>  [5] "cuneus"                            "entorhinal"                       
#>  [7] "frontal pole"                      "fusiform"                         
#>  [9] "inferior parietal"                 "inferior temporal"                
#> [11] "insula"                            "isthmus cingulate"                
#> [13] "lateral occipital"                 "lateral orbitofrontal"            
#> [15] "lingual"                           "medial orbitofrontal"             
#> [17] "middle temporal"                   "paracentral"                      
#> [19] "parahippocampal"                   "pars opercularis"                 
#> [21] "pars orbitalis"                    "pars triangularis"                
#> [23] "pericalcarine"                     "postcentral"                      
#> [25] "posterior cingulate"               "precentral"                       
#> [27] "precuneus"                         "rostral anterior cingulate"       
#> [29] "rostral middle frontal"            "superior frontal"                 
#> [31] "superior parietal"                 "superior temporal"                
#> [33] "supramarginal"                     "temporal pole"                    
#> [35] "transverse temporal"
atlas_labels(dk())
#>  [1] "lh_bankssts"                 "lh_caudalanteriorcingulate" 
#>  [3] "lh_caudalmiddlefrontal"      "lh_corpuscallosum"          
#>  [5] "lh_cuneus"                   "lh_entorhinal"              
#>  [7] "lh_frontalpole"              "lh_fusiform"                
#>  [9] "lh_inferiorparietal"         "lh_inferiortemporal"        
#> [11] "lh_insula"                   "lh_isthmuscingulate"        
#> [13] "lh_lateraloccipital"         "lh_lateralorbitofrontal"    
#> [15] "lh_lingual"                  "lh_medialorbitofrontal"     
#> [17] "lh_middletemporal"           "lh_paracentral"             
#> [19] "lh_parahippocampal"          "lh_parsopercularis"         
#> [21] "lh_parsorbitalis"            "lh_parstriangularis"        
#> [23] "lh_pericalcarine"            "lh_postcentral"             
#> [25] "lh_posteriorcingulate"       "lh_precentral"              
#> [27] "lh_precuneus"                "lh_rostralanteriorcingulate"
#> [29] "lh_rostralmiddlefrontal"     "lh_superiorfrontal"         
#> [31] "lh_superiorparietal"         "lh_superiortemporal"        
#> [33] "lh_supramarginal"            "lh_temporalpole"            
#> [35] "lh_transversetemporal"       "rh_bankssts"                
#> [37] "rh_caudalanteriorcingulate"  "rh_caudalmiddlefrontal"     
#> [39] "rh_corpuscallosum"           "rh_cuneus"                  
#> [41] "rh_entorhinal"               "rh_frontalpole"             
#> [43] "rh_fusiform"                 "rh_inferiorparietal"        
#> [45] "rh_inferiortemporal"         "rh_insula"                  
#> [47] "rh_isthmuscingulate"         "rh_lateraloccipital"        
#> [49] "rh_lateralorbitofrontal"     "rh_lingual"                 
#> [51] "rh_medialorbitofrontal"      "rh_middletemporal"          
#> [53] "rh_paracentral"              "rh_parahippocampal"         
#> [55] "rh_parsopercularis"          "rh_parsorbitalis"           
#> [57] "rh_parstriangularis"         "rh_pericalcarine"           
#> [59] "rh_postcentral"              "rh_posteriorcingulate"      
#> [61] "rh_precentral"               "rh_precuneus"               
#> [63] "rh_rostralanteriorcingulate" "rh_rostralmiddlefrontal"    
#> [65] "rh_superiorfrontal"          "rh_superiorparietal"        
#> [67] "rh_superiortemporal"         "rh_supramarginal"           
#> [69] "rh_temporalpole"             "rh_transversetemporal"

Your first brain plot

geom_brain() works like any ggplot2 geom. Pass it an atlas and it draws the regions:

ggplot() +
  geom_brain(atlas = dk(), show.legend = FALSE)

Default brain plot using geom_brain() with the dk atlas.

Arranging views

Brain atlases have multiple views (lateral, medial) and hemispheres. position_brain() controls how they’re laid out, using formula syntax borrowed from facet_grid():

ggplot() +
  geom_brain(
    atlas = dk(),
    position = position_brain(hemi ~ view),
    show.legend = FALSE
  )

Brain views arranged using formula syntax in position_brain().

Or reorder views with a character vector:

ggplot() +
  geom_brain(
    atlas = dk(),
    position = position_brain(c(
      "right lateral",
      "right medial",
      "left lateral",
      "left medial"
    )),
    show.legend = FALSE
  )

Brain views arranged using a character vector to specify order.

See vignette("positioning-views") for the full set of layout options, including grid layouts for subcortical and tract atlases and per-view zoom.

Showing only certain hemispheres or views

ggplot() +
  geom_brain(atlas = dk(), view = "lateral", show.legend = FALSE)

Brain plot showing only lateral views.

ggplot() +
  geom_brain(atlas = dk(), hemi = "left")

Brain plot showing only the left hemisphere.

For subcortical atlases, views are slice identifiers. Check what’s available with ggseg.formats::atlas_views():

atlas_views(aseg())
#> [1] "axial_3"   "axial_4"   "axial_5"   "sagittal"  "axial_6"   "coronal_1"
#> [7] "coronal_2"

Plotting your own data

Create a data frame with at least one column that matches the atlas (typically region or label). Pass it to geom_brain() through the data argument and it joins to the atlas automatically:

library(dplyr)

some_data <- tibble(
  region = c(
    "transverse temporal",
    "insula",
    "precentral",
    "superior parietal"
  ),
  p = sample(seq(0, 0.5, 0.001), 4)
)

ggplot() +
  geom_brain(
    atlas = dk(),
    data = some_data,
    position = position_brain(hemi ~ view),
    aes(fill = p)
  ) +
  scale_fill_viridis_c(option = "cividis", direction = -1) +
  theme_void()

Brain plot coloured by external p-values using a viridis scale.

See vignette("external-data") for more on data preparation and matching.

Faceting

ggplot2 faceting works as you’d expect. Group your data by the faceting variable and geom_brain() replicates the full atlas in each panel:

some_data <- tibble(
  region = rep(
    c(
      "transverse temporal",
      "insula",
      "precentral",
      "superior parietal"
    ),
    2
  ),
  p = sample(seq(0, 0.5, 0.001), 8),
  group = c(rep("A", 4), rep("B", 4))
)

ggplot() +
  geom_brain(
    atlas = dk(),
    data = group_by(some_data, group),
    position = position_brain(hemi ~ view),
    aes(fill = p)
  ) +
  facet_wrap(~group)

Brain plots faceted by group, each panel showing the full atlas.

Highlighting specific regions

To colour a few regions with their atlas colours, use two columns: one for the join (region) and one for the fill aesthetic:

data <- data.frame(
  region = atlas_regions(dk())[1:3],
  reg_col = atlas_labels(dk())[1:3]
)

palette <- atlas_palette(dk())[1:3]

ggplot() +
  geom_brain(atlas = dk(), data = data, aes(fill = reg_col)) +
  scale_fill_brain_manual(palette)

Highlighting selected regions using the atlas colour palette.

Scales and themes

Atlas colour palettes are applied automatically by geom_brain(). For custom palettes, use scale_fill_brain_manual().

Built-in themes strip axes and grids for cleaner figures:

ggplot() +
  geom_brain(atlas = dk()) +
  theme_brain()

Going further

For full control over the data before it reaches ggplot2 — adding region labels, layering other sf geoms, or using geom_sf() directly — see vignette("geom-sf").

Finding more atlases

ggseg ships with dk() (cortical), aseg() (subcortical), suit() (cerebellar flamap), and tracula() (white matter tracts). Many more are available through the ggsegverse r-universe:

install.packages("ggsegYeo2011", repos = "https://ggsegverse.r-universe.dev")