Yes, you can correct the affine by using the StatefulTractogram and the affine of the data that generated the tracks or the original tractogram https://dipy.org/documentation/1.0.0./examples_built/streamline_formats/#exa... On Thu, Nov 21, 2019 at 12:28 PM Giulia Berto' wrote:

Hi,

I am trying to use the atlas of 80 bundles you distribute here https://figshare.com/articles/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375... https://figshare.com/articles/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375..., but I am encountering some issues.

It is written that the files are in MNI space ICBM 2009a, which has affine equal to array([[ 1., 0., 0., -98.], [ 0., 1., 0., -134.], [ 0., 0., 1., -72.], [ 0., 0., 0., 1.]])

However, all the trk files have affine equal to the identity matrix. Is there a reason why you stored in the header of the trk files the identity matrix? This for example prevents their correct visualization in TrackVis.

[image: ifof_r_dipy_trackvis.png]

Another question: the file whole_brain_MNI.trk, besides having the same issue with the affine, has 144678 streamlines. Is it the same whole brain tractogram Yeh is referring to in their paper? In that paper they obtained a tractogram of 550000 streamlines. Or is it maybe the union of the 80 bundles?

Thank you a lot for your help, best regards, Giulia

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