Beauchamp:SurfaceAveraging

Creating Surface Average of Functional Data

Averaging on the surface has many advantages over averaging in the volume but requires more processing steps. What is needed for each subject is a surface that is folded and aligned to the individual subject anatomy but that has the same number of nodes in each subject (this way a node-wise group map can be created) AND has the nodes aligned to a template brain (this way any given node has the same anatomical location in each subject). Each subject's functional data is mapped to that subject's standardized surface and then node-based group maps are created. The process must be performed separately for the left and right hemispheres.

SUMA automatically creates surfaces that have the desired properties, the std.141 brains. First, we transfer the functional data from each subject to the standard brain with a command line like this:

set s = OB
set sd = /Volumes/data/BCM/{$s}/fs/SUMA
set fd = /Volumes/data/BCM/{$s}/AFNI
cd $fd 
3dVol2Surf -overwrite -spec {$sd}/std.141.fs_both.spec -surf_A std.141.lh.smoothwm.gii  -sv fs_SurfVol_Alnd_Exp+orig -oob_value 0 \
-grid_parent "stats.OB2+orig[MouthFull#0_Coef,EyeFull#0_Coef]" -map_func mask -out_niml {$sd}/{$s}_EM_lh.niml.dset

Although surface registration between subjects is more accurate than volume registration, it is still good to spatially smooth the functional data to account for anatomical variability. It is best to only smooth once, on the surface, so remove any other smoothing steps in the preprocessing. 5 mm is a reasonable amount of smoothing; experiment with smaller amounts.

 SurfSmooth -met HEAT_07  -fwhm 5 -input {$gd}/{$s}_lh_EM.niml.dset -i {$sd}/std.141.lh.smoothwm.gii -output {$gd}/{$s}_lh_sm_EM.niml.dset

After we create surface functional files for each subject, we can use AFNI's group analysis tools exactly as with volume datasets. For instance,

3dttest++  -overwrite -paired -prefix MvsE.niml.dset \
-setA MS_EM.niml.dset'[0]' MU_EM.niml.dset'[0]' MX_EM.niml.dset'[0]' \
MZ_EM.niml.dset'[0]' 	NB_EM.niml.dset'[0]' 	ND_EM.niml.dset'[0]' NF_EM.niml.dset'[0]' NH_EM.niml.dset'[0]' NV_EM.niml.dset'[0]' OA_EM.niml.dset'[0]' \
MT_EM.niml.dset'[0]' 	MW_EM.niml.dset'[0]' 	MY_EM.niml.dset'[0]' NA_EM.niml.dset'[0]' NC_EM.niml.dset'[0]' NE_EM.niml.dset'[0]' NG_EM.niml.dset'[0]' \
NU_EM.niml.dset'[0]' 	NW_EM.niml.dset'[0]' 	OB_EM.niml.dset'[0]' \
-setB MS_EM.niml.dset'[1]' MU_EM.niml.dset'[1]' MX_EM.niml.dset'[1]' \
MZ_EM.niml.dset'[1]' 	NB_EM.niml.dset'[1]' 	ND_EM.niml.dset'[1]' NF_EM.niml.dset'[1]' NH_EM.niml.dset'[1]' NV_EM.niml.dset'[1]' OA_EM.niml.dset'[1]' \
MT_EM.niml.dset'[1]' 	MW_EM.niml.dset'[1]' 	MY_EM.niml.dset'[1]' NA_EM.niml.dset'[1]' NC_EM.niml.dset'[1]' NE_EM.niml.dset'[1]' NG_EM.niml.dset'[1]' \
NU_EM.niml.dset'[1]' 	NW_EM.niml.dset'[1]' 	OB_EM.niml.dset'[1]'