Colorizing a map with neurotransmitter receptor densities

Parcellation maps can be colorized with a dictionary of values. This is useful to visualize data features in 3D space. Here we demonstrate it for average densities of the GABAA receptor.

from nilearn import plotting
import siibra
# We start by selecting an atlas and the Julich-Brain parcellation.
atlas = siibra.atlases.MULTILEVEL_HUMAN_ATLAS
julich_brain = atlas.get_parcellation('julich 3')

As in the previous example, we extract all receptor density features linked to Julich-Brain.

fingerprints = siibra.features.get(julich_brain, siibra.features.molecular.ReceptorDensityFingerprint)
fingerprints[0].data

	mean	std
receptor
AMPA	498	120
kainate	1366	273
NMDA	366	127
GABAA	789	362
GABAB	1961	397
M1	405	229
M2	107	44
M3	707	335
alpha4beta2	31	8
alpha1	441	265
alpha2	400	265
5-HT1A	732	669
5-HT2	370	74
D1	72	36

Colorizing a map requires a dictionary that maps region objects to scalar values. We build a dictionary mapping Julich-Brain regions to average receptor densities measured for GABAA. Note: For each receptor fingerprint, from the regions it is anchored to, we consider those that belong to the julich brain atlas, and store the mean receptor density for each of their leaf nodes.

receptor = 'GABAA'
mapping = {
    c: fp.data['mean'][receptor]
    for fp in fingerprints if receptor in fp.receptors
    for r in fp.anchor.regions
    if r in julich_brain
    for c in r.leaves
}

Now colorize the Julich-Brain maximum probability map and plot it.

colorized_map = julich_brain.get_map(space='mni152').colorize(mapping).fetch()
plotting.view_img(
    colorized_map, cmap='magma',
    title=f"Average densities available for {receptor}", symmetric_cmap=False
)

/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/numpy/core/fromnumeric.py:784: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
  a.partition(kth, axis=axis, kind=kind, order=order)

Alternatively, we can display this map on a surface mesh using nilearn. Note that, you can switch between the hemispheres or variants (inflated or pial) from the plot itself.

plotting.view_img_on_surf(colorized_map, cmap='magma', symmetric_cmap=False, surf_mesh="fsaverage6")

Added README.md to /home/runner/nilearn_data


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