Transformations between coordinate systems

Much of the “new” functionality in neuromaps is hosted in the neuromaps.transforms module. The functionality in this module provides easy-to-use interfaces for transforming brain annotations between all of the four standard coordinate systems supported in neuromaps.

The functions take on two formats (depending on whether the MNI152 volumetric system is involved), which we discuss below.

Transforming from volumetric spaces

Curently neuromaps only supports transforming brain annotations from MNI152 volumetric space to one of the other three surface-based coordinate systems (though work in ongoing to integrate transformations in the other direction!). This transformation is achieved through a process known as “registration fusion” (Wu et al., 2018, Hum Brain Mapping). In order to transform data from MNI152 space to another space you can use any of the neuromaps.transforms.mni152_to_XXX functions, where XXX is replaced with the desired coordinate system.

For example, to transform data to the fsLR coordinate system:

>>> from neuromaps.datasets import fetch_annotation
>>> from neuromaps import transforms
>>> neurosynth = fetch_annotation(source='neurosynth')
>>> fslr = transforms.mni152_to_fslr(neurosynth, '32k')

The returned fslr object is a tuple of nib.GiftiImage objects corresponding to data from the (left, right) hemisphere. These data can be accessed via the .agg_data() method on the image objects:

>>> fslr_lh, fslr_rh = fslr
>>> print(fslr_lh.agg_data().shape)

It’s just as easy to transform the data to a different space:

>>> fsavg = transforms.mni152_to_fsaverage(neurosynth, '164k')
>>> fsavg_lh, fsavg_rh = fsavg
>>> print(fsavg_lh.agg_data().shape)

Note that you can also transform between different resolutions within the MNI152 coordinate system:

>>> import nibabel as nib
>>> ns2mm = nib.load(neurosynth)
>>> print(ns2mm.shape)
(91, 109, 91)
>>> ns3mm = transforms.mni152_to_mni152(neurosynth, '3mm')
>>> print(ns3mm.shape)
(91, 109, 91)

(This is frequently referred to as “resampling” and, indeed, we are really just calling out to nilearn.image.resample_to_img; we simply provide this interface to ensure consistency!)

Transforming to/from surface spaces

Transforming between surface-based coordinate systems works bidirectionally. That is, data in each surface system can be transformed to and from every other surface system. Here, the transformation functions take the form of neuromaps.transforms.XXX_to_YYY where XXX is the source system and YYY is the target system.

For example, to transform data from the fsaverage to the fsLR coordinate system:

>>> abagen = fetch_annotation(source='abagen')
>>> fslr = transforms.fsaverage_to_fslr(abagen, '32k')

As with the volumetric-to-surface transformation, the returned object is a tuple of nib.GiftiImage objects corresponding to data from the (left, right) hemisphere.

>>> fslr_lh, fslr_rh = fslr
>>> print(fslr_lh.agg_data().shape)

Note that, by default, all of the transformation functions assume the provided tuple contains data in the format (left hemisphere, right hemisphere) and performs linear interpolation when resampling data to the new coordinate system. However, the surface functions in neuromaps.transforms accept two optional keyword parameters that can modify these defaults!

Single-hemisphere data

What happens when you want to transform annotations for which only one hemisphere contains data? You can use the hemi keyword parameter to let the transformation functions know:

>>> abagen_lh = abagen[0]
>>> fslr = transforms.fsaverage_to_fslr(abagen_lh, '32k', hemi='L')

Note that the returned object is still a tuple—it just simply has one entry instead of two!

>>> fslr_lh, = fslr
>>> print(fslr_lh.agg_data().shape)

The hemi parameter accepts values ‘L’ and ‘R’ for the left and right hemispheres, respectively.

You can also use the hemi parameter if you want to provide bilateral data that is not in the (left, right) hemisphere format:

>>> abagen_reverse = (abagen[1], abagen[0])
>>> fslr_rh, fslr_lh = transforms.fsaverage_to_fslr(abagen_reverse, '32k', hemi=('R', 'L'))

Nearest-neighbors interpolation

By default the transformation functions in neuromaps.transforms use linear interpolation when resampling data; however, this is not ideal if the data that are being used are integer-valued (e.g., if the data represent a parcellation)—or if you would simply prefer not to use linear interpolation! In either case you can pass the method keyword parameter to the transform functions and specify that you would prefer 'nearest' neighbors interpolation instead:

>>> fslr_nearest = transforms.fsaverage_to_fslr(abagen, '32k', method='nearest')

Note that the only accepted values for method are 'linear' and 'nearest'.


We can use an instance of the neuromaps.parcellate.Parcellater class to parcellate our data. The Parcellater class expects a path to the parcellation files or a tuple of parcellation files (left and right). Note that hemisphere business can be handled with the hemi parameter. The Parcellater class also expects these files to contain a unique integer ID for each parcel (brain region), and it will ignore all IDs of 0. This means that if your parcellation is not in the right format, you will need to rework it using helper functions such as neuromaps.images.relabel_gifti() and neuromaps.images.annot_to_gifti() .

See nulls with parcellated data for another example parcellating, in the context of spatial nulls.

Here is an example of parcellating volumetric (MNI-152) data into the Lausanne-125 (234-node) atlas.

>>> from neuromaps.datasets import fetch_annotation
>>> from netneurotools import datasets as nntdata
>>> from neuromaps.parcellate import Parcellater

>>> glutamate = fetch_annotation(source='smart2019', desc='abp688', space='MNI152', den='1mm')
>>> lausanne = nntdata.fetch_cammoun2012(version='MNI152NLin2009aSym')
>>> parc = Parcellater(lausanne['scale125'], 'mni152')
>>> glutamate_parc = parc.fit_transform(glutamate, 'mni152')

Note that the Parcellater class needs to know the space you are working in (in this case, MNI-152).

Here is another example in which surface data (fslR) is parcellated into the Schaefer-400 atlas. Note that in this case the atlas is in dlabel.nii format. neuromaps requires tuples of gifti (*.gii) files but this can be handled using the neuromaps.images.dlabel_to_gifti() .

>>> from neuromaps.datasets import fetch_annotation
>>> from netneurotools import datasets as nntdata
>>> from neuromaps.parcellate import Parcellater
>>> from neuromaps.images import dlabel_to_gifti

>>> fc_grad = fetch_annotation(source='margulies2016', desc='fcgradient01', space='fsLR', den='32k')
>>> schaefer = nntdata.fetch_schaefer2018('fslr32k')['400Parcels7Networks']
>>> parc = Parcellater(dlabel_to_gifti(schaefer), 'fsLR')
>>> fc_grad_parc = parc.fit_transform(fcgradient, 'fsLR')

In these two examples, parcellations were fetched using netneurotools . But of course you can fetch your parcellation files from wherever you would normally get them. Just make sure they are in neuromaps format and check that your parcellation makes sense (i.e. looks the way it should) afterwards.