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.

difumo_maps = siibra.get_map(
    parcellation='difumo 64',
    space='mni152',
    maptype='statistical'
)
region = "fusiform posterior"
img = difumo_maps.fetch(region=region)

# let's look at the resulting query image
plotting.view_img(
    img,
    symmetric_cmap=False,
    cmap="magma"
)

/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 assigne an image, we first need to createa 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')

	input structure	centroid	volume	region	correlation	intersection over union	map value	map weighted mean	map containedness	input weighted mean	input containedness
8	1	(-43.61, -63.71, -12.64)	226	Area FG2 (FusG) left	0.641933	0.514049	0.868882	0.23183	0.778082	0.000261	0.602363
24	2	(42.9, -60.76, -14.91)	227	Area FG2 (FusG) right	0.73607	0.522092	0.838194	0.235593	0.771513	0.000278	0.617583