The next thing to do is to project this ROI into a FreeSurfer surface. We recommend using the grey/white interface to seed tractography from the cortex: (assuming the ROI is in the left hemisphere)<br><br>
The next thing to do is to project this ROI into a FreeSurfer surface. We recommend using the grey/white interface to seed tractography from the cortex: (assuming the ROI is in the left hemisphere)<br><br>
mri_cor2label --i myroi2surf.mgh --surf john lh white --id 1 --l myroilabel <br>
mri_cor2label --i myroi2surf.mgh --surf john lh white --id 1 --l ./myroilabel <br>
</code>
</code>
<br>
<br>
This will create a file called <code>myroilabel.label</code> that you can use directly in probtrackx (see following section).
This will create a file called <code>myroilabel.label</code> that you can use directly in probtrackx (see following section). We recommend checking the label file by loading it onto a freesurfer surface using tksurfer. E.g.:
<br><br>
<code>
tksurfer john lh white
</code>
<br><br>Then File->Label->Load labels
<br><br>
<h4>3. Running probtrackx using surfaces</h4>
<h4>3. Running probtrackx using surfaces</h4>
All you need to do now, is to run probtrackx specifying four things: (1) the label file as a seed, (2) a description of the whole cortical surface for the corresponding hemisphere [e.g. surf.white.asc], (3) provide a transformation from conformed FreeSurfer space to diffusion space, and (4) a conformed FreeSurfer volume as a reference space:
All you need to do now, is to run probtrackx specifying four things: (1) the label file as a seed, (2) a description of the whole cortical surface for the corresponding hemisphere [e.g. surf.white.asc], (3) provide a transformation from conformed FreeSurfer space to diffusion space, and (4) a conformed FreeSurfer volume as a reference space:
<br><br>
<br><br>
<code>
<code>
probtrackx -x myroilabel.label --mesh=$SUBJECTS_DIR/john/surf/lh.white.asc --xfm=freesurfer2fa.mat --ref=$SUBJECTS_DIR/john/mri/nifti/brain [+all the other options]
probtrackx -x myroilabel.label --mesh=$SUBJECTS_DIR/john/surf/lh.white.asc --xfm=freesurfer2fa.mat --seedref=$SUBJECTS_DIR/john/mri/nifti/brain [+all the other options]
</code>
</code>
<br><br>
<br><br>
You can also run probtrackx using a nonlinear warpfield to get from freesurfer space to diffusion space (if you had used FNIRT in step 1 above):<br><br>
You can also run probtrackx using a nonlinear warpfield to get from freesurfer space to diffusion space (if you had used FNIRT in step 1 above):<br><br>
<code>
<code>
probtrackx -x myroilabel.label --mesh=$SUBJECTS_DIR/john/surf/lh.white.asc --xfm=freesurfer2fa_warp --invxfm=fa2freesurfer_warp --ref=$SUBJECTS_DIR/john/mri/nifti/brain [+all the other options]
probtrackx -x myroilabel.label --mesh=$SUBJECTS_DIR/john/surf/lh.white.asc --xfm=freesurfer2fa_warp --invxfm=fa2freesurfer_warp --seedref=$SUBJECTS_DIR/john/mri/nifti/brain [+all the other options]
</code>
</code>
<br><br>
<br><br>
Note: in this last case, we need both forward and backward transforms fa<-->freesurfer.
Note: in this last case, we need both forward and backward transforms fa<-->freesurfer.
<h4>4. Using some of the outputs</h4>
<h4>4. Using some of the outputs</h4>
When using classification targets in probtrackx, together with a surface-based seed, an output is created in the form of a matrix called matrix_seeds_to_all_targets. You can use this file to run find_the_biggest and produce label files for each of the hard-classified clusters. You can also use it to produce overlay files containing connectivity scores to each target.<br><br>
When using classification targets in probtrackx, together with a surface-based seed, you may use the probtrackx option <code>--seedcountastext</code>, in which case an output is created in the form of a matrix called matrix_seeds_to_all_targets. You can use this file to run find_the_biggest and produce label files for each of the hard-classified clusters. You can also use it to produce overlay files containing connectivity scores to each target.<br><br>
Running find_the_biggest using matrix_seeds_to_all_targets:<br><br>
Running find_the_biggest using matrix_seeds_to_all_targets:<br><br>
The output of this command will be a set of files called myclusters<i>.label, i.e. one label file per cluster. You can combine these to produce a single annotation file:
The output of this command will be a set of files called myclusters<i>.label, i.e. one label file per cluster. You can combine these to produce a single annotation file:
