[Neuroimaging] using nibabel to convert a dtscalar parcellation (1-N for each greyordinate) to a 4d (binary slice for each parcel) dtseries

[Michiel Cottaar]

Hi Uri,

You will have to tell Cifti what is along this new axis you created. One way to do this is to use the cifti2 axes (https://nipy.org/nibabel/reference/nibabel.cifti2.html#module-nibabel.cifti2.cifti2_axes). After creating newdata this would look like:
>>> from nibabel import cifti2
>>> new_axis = cifti2.ScalarAxis([f"parcel {idx + 1}" for idx in range(N)])  # these are the names for all the parcels; you might want to replace them with something more meaningfull
>>> grayordinate_axis = dsobj.header.get_axis(1)  # get a description of the greyordinate space from the loaded CIFTI file
>>> newobj = nb.Cifti2Image(newdata, (new_axis, grayordinate_axis))  # creates a new Cifti2 header based on these axes
This newobj will have the shape of (N, 64984) for N parcels.

However, based on your description I'm not completely sure if this is what you want to be doing. The output will still be a 2D CIFTI file rather than a 4D NIFTI file. If you want the latter, just reshape the newdata to be 4D using something like `newdata.T.reshape(64984, 1, 1, N)` and create a new nb.Nifti1Image object from that data.

Good luck,

Michiel

On 24 Oct 2020, at 19:05, Uri Elias <uri.urie at gmail.com<mailto:uri.urie at gmail.com>> wrote:



Hi All,

Top level - I'd like to apply dual regression on rest fMRI based on parcellation where my rs-fMRI data in in CIFTI format.

For a dual-regression SW I've chosen FSL, also because it works on Cifti (according to manual).
For parcellation I've chosen the Schaefer parcellation, as it is Cifti-available and also partially based on rest-fMRI.

Now, the Cifti parcellation file is a dscalar file (that is - a single volume/surface), with each greyordinate belongs to one of N parcels. FSL (AFAIU) requires a 4D file as a reference
Working with nibabel, reformatting the matrix is easy - reading it with nibabel and do something like: for k in range(N):
    newdata[k,:] = (orgdata==k+1).astype(float)

Updating the Nifti header is also easy -
    newobj = nb.Cifti2Image(newdata, dscobj.header, dscobj.nifti_header)
    newobj.update_headers()

The problem with this way is the the Cifti header is not being updated -
    newobj.header.matrix.get_data_shape()
yeilds
    (1, 64984)

What's a good way of doing this? - either by sticking to the nibabel->FSL line right or by any alternative

Thanks all so much,
Uri

--
Uri Elias
Computational Neuropsychiatry Lab
Hadassah Hebrew University Medical School
[www.neuropsychiatrylab.com]<http://www.neuropsychiatrylab.com/>

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