Find predefined reference spaces

Just as atlas and parcellation objects, siibra provides an instance table of preconfigured reference spaces that will be populated when accessed for the first time, and stay in your local file cache for future use. Reference spaces are purely semantic objects which define a brain coordinate system.

Associated to each reference space however are one or more reference templates, representing a concrete reference image or surface representation of the brain. These are demonstrated in the following example.

As for atlases and parcellations, siibra provides a registry of predefined reference spaces:

import siibra
siibra.spaces.keys

['BIGBRAIN_MICROSCOPIC_TEMPLATE_HISTOLOGY', 'FREESURFER_FSAVERAGE', 'FREESURFER_FSAVERAGE6', 'HCP32K', 'MNI_152_ICBM_2009C_NONLINEAR_ASYMMETRIC', 'MEBRAINS_POPULATION_BASED_MONKEY_TEMPLATE', 'MNI_COLIN_27', 'ALLEN_MOUSE_COMMON_COORDINATE_FRAMEWORK_V3', 'WAXHOLM_SPACE_OF_THE_SPRAGUE_DAWLEY_V1_01']

Fetching an object works in the same way as for e.g. siibra.atlases (see Atlases and brain parcellations)

space = siibra.spaces.get('icbm 2009c asym')
print(space)

MNI 152 ICBM 2009c Nonlinear Asymmetric

Typically however, we are only interested in the reference spaces supported by a given atlas. Atlases provide their own reference space table for this purpose, which includes the relevant subset of the spaces.

atlas = siibra.atlases.get('human')
dir(atlas.spaces)

['BIGBRAIN_MICROSCOPIC_TEMPLATE_HISTOLOGY', 'FREESURFER_FSAVERAGE', 'MNI_152_ICBM_2009C_NONLINEAR_ASYMMETRIC', 'MNI_COLIN_27', 'dataframe']

These can be used like any other registry object:

colin_space = atlas.spaces.get('colin27')
print(colin_space)

MNI Colin 27

We can also explicitly request a supported space object from the atlas, which has the same effect as accessing the Registry.

bigbrain_space = atlas.get_space('bigbrain')
print(bigbrain_space)

BigBrain microscopic template (histology)