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")
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.
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