Note
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Assign modes in activation maps to brain regions
Statistical parcellations maps can also assign regions to image volumes. If
given a Nifti1Image
object as input, the assignment method will interpret
it as a measurement of a spatial distribution. It will first split the image
volume into disconnected components, i.e. any subvolumes which are clearly
separated by zeros. Then, each component will be compared to each statistical
maps in the same way that the Gaussian blobs representing uncertain points are
processed in Assigning coordinates to brain regions.
We start again by selecting the Julich-Brain probabilistic maps from the human atlas, which we will use for the assignment.
import siibra
from nilearn import plotting
Select a probabilistic parcellation map to do the anatomical assignments.
julich_pmaps = siibra.get_map(
parcellation="julich 2.9",
space="mni152",
maptype="statistical"
)
As an exemplary input signal, we use a statistical map from the 64-component functional mode parcellation (DiFuMo 64) by Thirion et al.
/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.
This “fake functional map” has two modes, one in each hemisphere. We now assign cytoarchitectonic regions to this functional map. Since we are here usually interested in correlations of the modes, we filter the result by significant (positive) correlations. To assign an image, we first need to create Volume which has to have a space defined.
volume = siibra.volumes.volume.from_nifti(img, difumo_maps.space, "fusiform posterior")
with siibra.QUIET: # suppress progress output
assignments = julich_pmaps.assign(volume)
assignments.query('correlation >= 0.35')
Total running time of the script: (0 minutes 17.438 seconds)
Estimated memory usage: 645 MB