Working with annotations and labels in 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)))

##   [1]   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
##  [19]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35
##  [37]  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53
##  [55]  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71
##  [73]  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89
##  [91]  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107
## [109] 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
## [127] 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
## [145] 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
## [163] 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
## [181] 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
## [199] 198 199 200

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.