Assemble the Data (isxconcat-sess) (Volume)

The first step in the group analysis is to "assemble" the data. This means creating a single 4D file with where the 4th "time" dimension is actual all the subjects concatenated together in a common space. is a different command, depending upon whether the common space is volume- or surface-based.

To run the volume-based concatenation, run the command below. Note that the data from this command already exist in the group-analysis-tut directory.

isxconcat-sess -sf <sessid> -analysis <analysis name> -c <contrast name> -o <output dir>

In the output directory, you will see a series of files that start with "tal":

• tal.meanfunc.nii is a stack where each "time point" is the mean functional image of each subject sampled in the MNI305 space.
• tal.masks.nii are the binary masks for all the subjects
• tal.fsnr.nii are the functional SNR maps from each subject.
• tal.mask.nii is a single binary mask made from the intersection of the individuals.
• ffxdof.dat is the fixed-effects DOF across all subjects.
• sessid.txt is the list of sessions, the corresponding freesurfer subject name, and the DOF contributed by each subject.

In the new contrast name folder, you will see:

• tal.ces.nii - the contrast maps for each of the subjects
• tal.cesvar.nii are the variance of the contrast for each subject (i.e., the square of the standard error). This variance is needed for fixed-effects and weighted random-effects analysis.

Group GLM Analysis (Volume)

When you perform a group analysis, you are looking for effects of the task across the population. Now decide if you are running a Random Effects or Weighted Random Effects or Fixed Effects Analysis.

Random Effects (RFx, OLS) (Volume)

Weighted Random Effects (WRFx, WLS) (Volume)

Fixed Effects (FFx) (Volume)

Correction for Multiple Comparisons/Cluster Analysis (Volume)

With so many voxels in fMRI maps, it is very likely that many voxels will appear to be active purely by random chance (ie, a false positive). The is known as the "Problem of Multiple Comparisons". One way around this is to do a cluster analysis in which active voxels are eliminated unless they appear in a cluster, the idea being that false positives will not appear next to each other.