AFNI program: 3dSpaceTimeCorr
Output of -help
3dSpaceTimeCorr
v1.2 (PA Taylor, Aug. 2019)
This program is for calculating something *similar* to the (Pearson)
correlation coefficient between corresponding voxels between two data
sets, which is what 3dTcorrelate does. However, this is program
operates differently. Here, two data sets are loaded in, and for each
voxel in the brain:
+ for each data set, an ijk-th voxel is used as a seed to generate a
correlation map within a user-defined mask (e.g., whole brain,
excluding the seed location where r==1, by definition);
+ that correlation map is Fisher Z transformed;
+ the Z-correlation maps are (Pearson) correlated with each other,
generating a single correlation coefficient;
+ the correlation coefficient is stored at the same ijk-th voxel
location in the output data set;
and the process is repeated. Thus, the output is a whole brain map
of r-correlation coefficients for corresponding voxels from the two data
sets, generated by temporal and spatial patterns (-> space+time
correlation!).
This could be useful when someone *wishes* that s/he could use
3dTcorrelate on something like resting state FMRI data. Maybe.
Note that this program could take several minutes or more to run,
depending on the size of the data set and mask.
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+ USAGE: Load in 2 data sets and a mask. This computation can get pretty
time consuming-- it depends on the number of voxels N like N**2.
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+ COMMAND: two 4D data sets need to be put in (order doesn't matter),
and a mask also *should* be.
3dSpaceTimeCorr -insetA FILEA -insetB FILEB -prefix PREFIX \
{-mask MASK} {-out_Zcorr}
{-freeze_insetA_ijk II JJ KK}
{-freeze_insetA_xyz XX YY ZZ}
where:
-insetA FILEA :one 4D data set.
-insetB FILEB :another 4D data set; must have same spatial dimensions as
FILEA, as well as same number of time points.
-mask MASK :optional mask. Highly recommended to use for speed of
calcs (and probably for interpretability, too).
-prefix PREFIX :output filename/base.
-out_Zcorr :switch to output Fisher Z transform of spatial map
correlation (default is Pearson r values).
-freeze_insetA_ijk II JJ KK
:instead of correlating the spatial correlation maps
of A and B that have matching seed locations, with this
option you can 'freeze' the seed voxel location in
the input A dset, while the seed location in B moves
throughout the volume or mask as normal.
Here, one inputs three values, the ijk indices in
the dataset. (See next opt for freezing at xyz location.)
-freeze_insetA_xyz XX YY ZZ
:same behavior as using '-freeze_insetA_ijk ..', but here
one inputs the xyz (physical coordinate) indices.
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+ OUTPUT:
A data set with one value at each voxel, representing the space-time
correlation of the two input data sets within the input mask.
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+ EXAMPLE:
3dSpaceTimeCorr \
-insetA SUB_01.nii.gz \
-insetB SUB_02.nii.gz \
-mask mask_GM.nii.gz \
-prefix stcorr_01_02 \
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