Atlas Visualization with Optimal Colours • neuroatlas

Introduction

Every atlas in neuroatlas can be visualised with a single call to plot(). Behind the scenes, plot.atlas() renders coloured parcels as volumetric slices using neuroim2’s plot_montage() and plot_ortho(), with colours assigned automatically by the roi_colors system.

library(neuroatlas)

Quick Start

atlas <- get_aseg_atlas()
plot(atlas)

The default view is a multi-slice montage (axial slices) with colours chosen by the rule_hcl algorithm — a fast, deterministic palette that uses network hues and hemisphere luminance differences.

For a three-plane orthogonal view:

plot(atlas, view = "ortho")

Colour Algorithms

neuroatlas ships four colour algorithms, each suited to different use cases. Pass the method argument to plot() to switch between them.

rule_hcl (default)

Deterministic and fast. Assigns hues per network with anterior-posterior gradients and hemisphere luminance offsets.

plot(atlas, method = "rule_hcl", nslices = 8)

maximin_view

Optimises perceptual separation between spatially neighbouring ROIs across slice views. Best for publication figures where adjacent parcels must be easily distinguished.

plot(atlas, method = "maximin_view", nslices = 8)

network_harmony

Network-aware: ROIs in the same network share analogous hue families while still maximising local separation. Requires the atlas to have a $network field (e.g. Schaefer atlases).

# Requires a Schaefer atlas with network metadata (network download)
schaefer <- get_schaefer_atlas(parcels = "200", networks = "7")
plot(schaefer, method = "network_harmony", nslices = 8)

embedding

Projects ROI features to 2D (PCA or UMAP) and maps polar angle to hue, yielding globally structured gradients.

plot(atlas, method = "embedding", nslices = 8)

Custom Colours

You can supply your own colours as a named character vector (names are region IDs) or as a tibble from atlas_roi_colors().

Named vector

my_cols <- setNames(rainbow(length(atlas$ids)), atlas$ids)
plot(atlas, colors = my_cols, nslices = 6)

Pre-computed tibble

color_tbl <- atlas_roi_colors(atlas, method = "maximin_view")
head(color_tbl)
#> # A tibble: 6 × 2
#>      id color  
#>   <dbl> <chr>  
#> 1    10 #14E2C6
#> 2    11 #EEB8C7
#> 3    12 #A8C3E3
#> 4    13 #F6BA4F
#> 5    16 #81C2FF
#> 6    17 #87CCBE
plot(atlas, colors = color_tbl, nslices = 6)

Programmatic Colour Access

The atlas_roi_colors() function is the bridge between atlas objects and the roi_colors_*() family. It extracts ROI centroids, builds a metadata tibble, and dispatches to the requested algorithm.

cols <- atlas_roi_colors(atlas, method = "rule_hcl")
cols
#> # A tibble: 17 × 2
#>       id color  
#>    <dbl> <chr>  
#>  1    10 #FC90AD
#>  2    11 #EAA06D
#>  3    12 #F19B7F
#>  4    13 #F99596
#>  5    16 #EB7DAD
#>  6    17 #FD8EB8
#>  7    18 #F79690
#>  8    26 #ED9E73
#>  9    28 #E98292
#> 10    49 #D97088
#> 11    50 #C28439
#> 12    51 #CC7D56
#> 13    52 #D3776A
#> 14    53 #DA6E93
#> 15    54 #D17963
#> 16    58 #C58241
#> 17    60 #D7737C

This tibble can be joined with other atlas metadata for downstream analyses.

Controlling Slice Count

Use nslices to control how many slices appear in the montage:

plot(atlas, nslices = 4)

Existing Visualisation Tools

For flatmap-style cortical visualisations (not volumetric), neuroatlas also provides:

ggseg_schaefer() — ggseg-based Schaefer flatmaps
plot_glasser() — Glasser atlas flatmaps

These are complementary to plot() and remain available as standalone functions.