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

[Uri Elias]

Thanks!

I may indeed try that, but before - and because you were doubtful about
this sort of pipeline - I'll try to go over again -

I want to get an "individualized atlas" by taking one and using it as a
reference to dual regression (on a rest state time series), all this over
Cifti. Does that make sense? If so, what's the best way of doing so?





On Sun, Oct 25, 2020 at 2:08 PM Michiel Cottaar <
michiel.cottaar at ndcn.ox.ac.uk> wrote:

> 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> 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
> [image: www.neuropsychiatrylab.com] <http://www.neuropsychiatrylab.com/>
>
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-- 
Uri Elias

Computational Neuropsychiatry Lab

Hadassah Hebrew University Medical School
[image: www.neuropsychiatrylab.com] <http://www.neuropsychiatrylab.com/>

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