Working with annotations and labels in ggbrain

In ggbrain: Create Images of Volumetric Brain Data in NIfTI Format Using 'ggplot2' Syntax

knitr::opts_chunk$set(echo = TRUE)
library(ggbrain)
library(ggplot2)
library(patchwork)
library(RNifti)

# MNI 2009c anatomical underlay
underlay_3mm <- system.file("extdata", "mni_template_2009c_3mm.nii.gz", package = "ggbrain")

# Schaefer 200-parcel atlas of cortex
schaefer200_atlas_3mm <- system.file("extdata", "Schaefer_200_7networks_2009c_3mm.nii.gz", package = "ggbrain")

Categorical images in ggbrain

Many visualizations of brain data rely on continuous-valued images containing intensities or statistics. For example, we might wish to visualize the z-statistics of a general linear model.

Yet, there are often images that contain integers, where unique values represent a priori regions of interest or clusters identified using familywise error correction methods. Brain atlases are a common example of integer-valued images. Here we demonsrate the cortical parcellation developed by Schaefer and colleagues (2018).

Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28, 3095-3114.

As you can see, this version of the atlas contains 200 cortical parcels.

schaefer_img <- readNifti(schaefer200_atlas_3mm)
sort(unique(as.vector(schaefer_img)))

At a basic level, we can visualize this image in the same way as continuous images, as described in ![ggbrain_introduction.html].

gg_obj <- ggbrain() +
  images(c(underlay = underlay_3mm, atlas = schaefer200_atlas_3mm)) +
  slices(c("z = 30", "z=40")) +
  geom_brain(definition = "underlay") +
  geom_brain(definition = "atlas")

plot(gg_obj)

As we can see, however, the continuous values represent discrete parcels in the atlas. Thus, we may wish to use a categorical/discrete color scale to visualize things.

ggbrain documentation built on March 31, 2023, 7:11 p.m.