I have been using 3dMEMA to calculate group statistics for a 2 group (Patient, Control) x 1 condition (Task) model and have yielded the best results using this method (vs 3dttest++ or 3dMVM), as my groups have a small N and unequal variances. I would now like to build this model to include 2 groups x 2 conditions (Task v1, Task v2). Both groups performed both tasks. Although I can do this in 3dMVM or 3dANOVA2, I would prefer to continue these analyses in 3dMEMA.

After looking through the 3dMEMA help document, it seems from Example 3 that I need to contrast my 2 conditions using 3dREML. Thus far, I have pre-processed my conditions separately using afni_proc.py and so only have stats corresponding to Task-Rest for each condition, but no stats maps for TaskV1-TaskV2. These tasks are essentially 2 different stimulus types, but were carried out in separate runs.

I am a novice AFNI user - but it seems as though I can use 3dDeconvolve to generate an input matrix file for 3dREMLfit to generate these statistical maps. I believe my script for the first step should look something like this, but am unsure what my input files should be or whether I should be concatenating these runs. The tasks have equivalent timing but were completed in separate runs (and have been pre-processed separately thus far). I have been using slide 3 from this document for guidance: [afni.nimh.nih.gov] but am still unsure what my input files should be given that I am including 2 runs/stimulus types.

3dDeconvolve -input ? -concat ? \
-num_stimts 2 \
-stim_times 1 '1D: 24 72 120 168 216' BLOCK(24,1) \
-stim_label 1 VF.20 \
-stim_times 1 '1D: 24 72 120 168 216' BLOCK(24,1) \
-stim_label 2 VF.60 \
-glt sym 'SYM: VF.20 -VF.60' glt_label 1 20v60 \
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
-x1D_uncensored X.nocensor.xmat.1D \
-fitts fitts.$subj \
-errts errts.${subj} \
-bucket stats.$subj

Can anyone provide guidance as to whether these runs should be concatenated prior entering into 3dDeconvolve and if so, if this is an automated step that can be incorporated into my afni_proc.py scripts or whether this needs to be done separately.

Thank you!