Common Space
============

Build common space
------------------
To evaluate the consistency of each measure at the voxel level, we build a common space based on our population.
Common space is generated from b0 images resulting from Tractoflow using ANTs as follow:

.. code-block:: bash

  buildtemplateparallel.sh -d 3 -o b0_template -g 0.2 -c 2 -j 4 -s CC -r 1 -k 1 -m 100x70x50 -t GR b0*nii.gz

.. note::

   As we used the b0 output from tractoflow, b0 images are already
   pre-processing (including skull stripping, resampling, distorsion correction,...),
   see Tractoflow description here https://tractoflow-documentation.readthedocs.io.
   All individual subject measure maps are aligned in the common diffusion space using 
   the nonlinear registration of each b0 resampled native map. 


Average measure maps
--------------------

To generate mean image for each measure, we run scilpy python script as follow:

.. code-block:: bash

  # Mean images (example for FA map)
  scil_image_math.py mean *fa*.nii.gz mean_fa_healthy_control.nii.gz
  
  
Resulting average maps are avalaible section `Averaged measures in common space <https://high-frequency-mri-database-supplementary.readthedocs.io/en/latest/results/average_maps.html>`_.