Transform from Subject to MNI Space¶
Pycortex has built-in functionality for linearly transforming data to and from standard atlas spaces (e.g. MNI-152). This functionality is built on top of FSL.
This example shows how to create a transform from some subject functional space to MNI space, and how to apply that transform to a dataset.
import cortex # First let's do this "manually", using cortex.mni from cortex import mni import numpy as np np.random.seed(1234) # This transform is gonna be from one specific functional space for a subject # which is defined by the transform (xfm) s1_to_mni = mni.compute_mni_transform(subject='S1', xfm='fullhead') # s1_to_mni is a 4x4 array describing the transformation in homogeneous corods # Transform data from subject to MNI space # first we will create a dataset to transform data = cortex.Volume.random('S1', 'fullhead') # then transform it! mni_data = mni.transform_to_mni(data, s1_to_mni) # mni_data is a nibabel Nifti1Image mni_data_vol = mni_data.get_data() # the actual array, shape=(182,218,182) # That was the manual method. pycortex can also cache these transforms for you # if you get them using the pycortex database s1_to_mni_db = cortex.db.get_mnixfm('S1', 'fullhead') # this is the same as s1_to_mni, but will return instantly on subsequent calls
Total running time of the script: ( 0 minutes 0.000 seconds)