From finer to broader regions in the mouse reference atlas

Hi, I wonder how can I list all the finer areas that are considered as the broader Isocortex in the atlas?
More formally, I want to find all regions X that are considered as the Isocortex.

Thanks

You can use the python AllenSDK to accomplish this, there is an introductory example notebook here:
https://allensdk.readthedocs.io/en/latest/_static/examples/nb/reference_space.html#Constructing-a-structure-tree

The atlas is a hierarchy, so you’ll need to decide when you say “finer” how fine you mean. The atlas will go down to individual layers within individual sub-regions of visual cortex for example. There is no generally agreed upon description of “level” across the whole brain, but in cortex one level up from the leaf node (i.e. primary visual cortex layer 5) will be primary visual cortex, and then one step up from that will be visual areas, and then one step up from that is isocortex.

from allensdk.core.mouse_connectivity_cache import MouseConnectivityCache
# tell the cache class what resolution (in microns) of data you want to download
mcc = MouseConnectivityCache(resolution=10)

# use the structure tree class to get information about the isocortex structure
structure_tree = mcc.get_structure_tree()

isocortex = structure_tree.get_structures_by_name(['Isocortex'])[0]
iso_children=structure_tree.children([isocortex['id']])[0]
print([child['name'] for child in iso_children])

This will print for you

['Frontal pole, cerebral cortex', 'Somatomotor areas', 'Somatosensory areas', 'Gustatory areas', 'Visceral area', 'Auditory areas', 'Visual areas', 'Anterior cingulate area', 'Prelimbic area', 'Infralimbic area', 'Orbital area', 'Agranular insular area', 'Retrosplenial area', 'Posterior parietal association areas', 'Temporal association areas', 'Perirhinal area', 'Ectorhinal area']

But “Frontal pole, cerebral cortex” only has layers as children, whereas “Auditory Areas” has further subdivisions before getting to layers.

I hope that makes sense and helps.

Forrest, thanks a lot. This is very helpful.

Ruben