Freesurfer Abridged

Setting Up

• Note: I am using FS_0184 (semcat) as an example; fill in the parts specific to FS_0184 with the parts specific to your subject

This is an abridged version of the more detailed list provided elsewhere in the wiki.

Structural

1. Slicer
   • In terminal, Slicer &
   • Load data brainmask.nii
   • Save data as brainmask.mgz
   • Results: creates brainmask.mgz file
2. Autorecon 1
   • In terminal, autorecon1_SC.sh FS_0184
   • Don't bother editing after this step; skip to autorecon 2
   • Result: populates mri folder (includes "T1.mgz")
3. Autorecon 2
   • In terminal, autorecon2_SC.sh FS_0184
   • Editing
     • One terminal, tksurfer FS_0184 lh inflated
     • In another terminal, tkmedit FS_0184 brainmask.mgz -aux T1.mgz -surfs
     • Look for any noticable spikes in the tksurfer brain and edit the brainmask (tkmedit). See wiki page for details; this is the most labor intensive part of anatomical so make sure you're doing it right.
   • Results: creates lh.cortex.label and rh.cortex.label in label folder
4. Autorecon 3
   • In terminal, autorecon3_SC.sh FS_0184
   • Editing
     • In terminal, tksurfer -annot aparc FS_0184 lh pial
     • If needed, reference tksurfer -annot aparc fsaverage lh pial
     • Repeat with right hemi
     • Make sure to EXPORT ANNOTATION! Do not "save overlay!"
     • Again, be sure to see the autorecon3 page
   • Result: populates label folder (includes "lh.aparc.annot" and "rh.aparc.annot")

Functional

1. Bold file preparation
   • Semcat processing
     • In terminal, matlab &
     • In matlab, startup_smp12
   • LDT processing
     • In terminal, matlab &
     • nii_4D_drop_vols('f.nii', [# vols to drop-- see #LINK>])
     • set_TR(2.047, [1 2 3 4 5 6], 'f.nii')
     • d=datenum('DD-Mmm-YYYY') (e.g. '05-Oct-1997')
     • subj_ID='0183'
     • FSTSExtractor('prefix', 'LDT_', 'subject', subj_ID, 'run', i, 'tr', 2.047, 'volumes_dropped', <initial # drop vols>, 'date', d)
       • This step must be completed for each f.nii file in each bold folder
       • IT IS VERY IMPORTANT to look at what volumes to drop. It will likely not be the same for each bold file, unless it is a newer LDT participant. You can find more information #LINK here.
2. Analysis
   • cd to subject folder
   • mkanalysis.sh
3. Contrasts
   • cd to subject folder
   • Run mkcontrast.sh
4. Pre-processing
   • Run preproc-sess -per-run -fwhm 6 -surface self lhrh -fsd bold -s FS_<subj_ID>
5. Selxavg
   • Run selxavg3-sess -s <subjectname> -analysis <analysis path>
   • Analysis path is where your LDT.?h.sm6 folders are.
6. (Optional) Clut and Subparcellation
   • Run clut.sh FS_<subj_ID>
   • Run subpar.py FS_<subj_ID>
7. Blobs