I ran upon a strange problem. Let me provide you with an overview first:
A resting-state analysis (preprocessing) was done for a resting-state run (1 run per subject; 25 subjects overall). This session also included running SSwarper.
Now I would like to apply the same script for a task-based session (6 runs per subject instead of 1 run as above). Note: why do I apply a “resting-state” script on a task-based run? The reason simply is that I am interested in the brain’s dynamics, such as Power Law Exponent (PLE), and I would like to compare the real resting-state run with the task-runs (processed as if they were resting-state runs). This then allows me to properly compare variables like the PLE between both sessions.
To save time and as you know, it is no longer required to run SSwarper again for the second sessions’ preprocessing, as SSwarper was already done before. This is my script for the task-based runs (processed as “resting-state”, i.e., without the GLM).
for subject in Subject1 Subject2 Subject3 Subject4 Subject5 Subject6 Subject7 Subject8 Subject9 Subject10 Subject11 Subject12 Subject13 Subject14 Subject15 Subject16 Subject17 Subject18 Subject19 Subject20 Subject21 Subject22 Subject23 Subject24 Subject25
-blocks despike tshift align tlrc volreg mask blur regress
-align_opts_aea -cost nmi
-volreg_tlrc_warp -tlrc_base MNI152_2009_template_SSW.nii.gz
-regress_apply_mot_types demean deriv
-regress_bandpass 0.02 0.2
What is the problem? The problem is that even after 8-12 hours of running AFNI_proc it still computes the first subject (I already tried it twice). All applications beside the terminal are closed (2013 MacBook, 2.4 GHz dual core i5, 4 GB Ram). I know that this laptop is very slow compared to today’s standards. But ±10 hours per subject and it is still preprocessing? Running AFNI_proc for 1 subject took around an hour for the resting-state run.
Is there a mistake in my preprocessing script? I would be very happy if you could share your ideas.