Neuroimaging

AFNI know-how

Preprocessing

1. Data import
   1. use to3d or dimon to convert raw images into AFNI BRIK format
   2. to3d_anat.py, to3d_func.py
   3. Data file organizations
1. creating group list
   1. getid.py
2. looking for the sequential order ID one or more subjects
   1. lookup_subj.py
   2. lookup_subj_many.py

Region-of-Interest (ROI)

• Creating a half hemisphere mask

#left hemisphere selected
3dcalc -a mask_group+tlrc. -expr 'step(x-0)' -prefix test

#intersection with brain mask, and save in rh_mask+tlrc
3dcalc -a mask_group+tlrc. -b test+tlrc -expr 'step(a*b)' \
       -prefix rh_mask

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Misc.

• Converting a single t-value to z-value

ccalc -expr 'fitt_t2z(t, n)' 
t: tvalue
n: degrees of freedom

• Converting t-value sub-brick to z-value sub-brick

3dcalc -a dset+tlrc[<t-map_id>] \
  -expr 'fitt_t2z(3, 10)' -prefix dset2 

• listing all subbriks in a BRIK file and add numerical index

3dinfo -label <BRIK filename> | sed 's/|/\n/g' | nl

BrainVoyager QX know-how

Group analysis

• Created individual vmps[1]
• Paired t-test on vmps[2]

Multiple comparison

• Cluster threshold estimation (CTE)
  • Forming valid mask[3]
  • Masking and cluster thresholding[4]
  • CTE in BVQX vs. other softwares (AFNI, SPM)[5]
  • Solution to "Mask Not Valid!!" problem in CTE

FreeSurfurer tutorials

• view data

tkmedit sub001 brain.finalsurfs.mgz -aux T1.mgz -surfs -aseg

• syntax
  • sub001: Subject ID
  • brain.finalsurfs.mgz: skull-stripped volume primarily used for troubleshooting (found in $SUBJECTS_DIR/good_output/mri).
  • aux T1.mgz : pre-skull-stripped volume loaded as 2nd volume (in $SUBJECTS_DIR/good_output/mri)
  • surfs : loads all surfaces (orig, white, and pial, for left and right hemispheres)
  • aseg : loads automatic volume segmentation called aseg.mgz