The aim of this example is to help improve your understanding of dual regression and to get experience looking at some results.
This example is based on tools available in FSL, and the file names and instructions are specific to FSL. However, similar analyses can be performed in other neuroimaging software packages.
Please download the dataset for this example here:
The dataset you downloaded contains an example output folder after running a dual regression analysis in FSL, including a group-level comparison. In the example below, we will go through the files that exist in the output directory, and discuss how to find and visualise significant results.
Open a command line terminal and change directory into the dual regression directory you have downloaded called 'Data_4.3' (using
cd). You can now list the contents of the directory using
ls for example type
ls groupICA15.dr. The directory contains the following files:
** = subject number, in the order of the list entered into the dual regression command.
++ = ICA component number, in the order of the group-ICA maps entered into the dual regression command.
?? = contrast number, in order of the contrasts that were entered into the general linear model used for the group-level analysis.
A dual regression analysis is used to map group-ICA results back into individual subjects data, e.g. in order to examine between-group difference in ICA networks. We are using data from 12 subjects including six patients with a tumor and six healthy controls.
The directory you have downloaded was created by running the following command (do not run this again):
dual_regression groupICA15/melodic_IC 1 \ design/unpaired_ttest.mat design/unpaired_ttest.con 5000 \ groupICA15.dr `cat input_files.txt`
The corrected p-value output images from stage 3 (actually 1-p, for
convenience of display) are in files
++ means any one of the 15 group-level components (number 00
to 14) and the
?? relates to the contrast number. To view the
results from the dual regression analysis, run:
fsleyes -std groupICA15/melodic_IC \ -un -cm red-yellow -nc blue-lightblue -dr 4 15 \ groupICA15.dr/dr_stage3_ic0007_tfce_corrp_tstat3.nii.gz \ -cm green -dr 0.95 1 &
Make sure you are viewing them over the appropriate volume
melodic_IC (i.e. set the volume of the
image to 7, which is the number of the results image we loaded). The dual
regression result is very small (because we only had 12 subjects and therefore
not much statistical power), to find it please go to voxel location [63 81 54].
You may want to change the minimum threshold at the top to 0.9 to show the
results at a slightly more lenient p-value. Note that the results are for
contrast 3 (
tstat3.nii.gz), which is the comparison of healthy
controls minus tumor patients.
We are grateful to Natalie Voets for the data that was used in this example.