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.
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.