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June 15, 2022 12:29PM
Hi AFNI experts,

I really appreciate all your help as I move through amplitude modulation in AFNI! I have successfully (yay!) run individual level analyses for my participants, and am looking at the output to ensure I both understand it and have done analyses correctly. For a little more context, I have pasted a portion of my AFNI proc script below and denoted which events are amplitude modulated by a behavioral response (ranging from 1-5):

-regress_stim_labels Class \
Ant.Mean (AM)
FB.Mean (AM)
Resp.Mean (AM)
Ant.Nice (AM)
FB.Nice (AM)
Resp.Nice (AM)
Ant.UnpMean (AM)
FB.UnpMean (AM)
Resp.UnpMean (AM)
Ant.UnpNice (AM)
FB.UnpNice (AM)
Resp.UnpNice (AM)
Missing
-regress_stim_types 'AM2'
-regress_basis_multi 'dmBLOCK(1)' \
-regress_make_ideal_sum IDEAL_sum.1D
-regress_motion_file $topdir/derivatives/afni/confounds/sub-${subj}/sub-${subj}_task-seat_allruns_motion.1D \
-regress_motion_per_run
-regress_extra_ortvec $topdir/derivatives/afni/confounds/sub-${subj}/sub-${subj}_task-seat_allruns_aCompCor6.1D \
$topdir/derivatives/afni/confounds/sub-${subj}/sub-${subj}_task-seat_allruns_cosine.1D \
$topdir/derivatives/afni/confounds/sub-${subj}/sub-${subj}_task-seat_allruns_fd.1D \
-regress_extra_ortvec_labels aCompcor6 cosine fd \
-regress_opts_3dD


Looking at output has brought up 3 questions:

1) In examining the X.xmat.1D file, it has the following headers. I just want to confirm my understanding that index 11 for example, is the signal for my Ant.Mean event, modulated by the behavioral response (1-5) after it has been de-meaned WITHIN that condition.

index 0, group -1 , label Run#1Pol#0
index 1, group -1 , label Run#1Pol#1
index 2, group -1 , label Run#1Pol#2
index 3, group -1 , label Run#2Pol#0
index 4, group -1 , label Run#2Pol#1
index 5, group -1 , label Run#2Pol#2
index 6, group -1 , label Run#3Pol#0
index 7, group -1 , label Run#3Pol#1
index 8, group -1 , label Run#3Pol#2
index 9, group 1 , label Class#0
index 10, group 2 , label Ant.Mean#0
index 11, group 2 , label Ant.Mean#1
index 12, group 3 , label FB.Mean#0
index 13, group 3 , label FB.Mean#1
index 14, group 4 , label Resp.Mean#0
index 15, group 4 , label Resp.Mean#1
index 16, group 5 , label Ant.Nice#0
index 17, group 5 , label Ant.Nice#1
index 18, group 6 , label FB.Nice#0
index 19, group 6 , label FB.Nice#1
index 20, group 7 , label Resp.Nice#0
index 21, group 7 , label Resp.Nice#1
index 22, group 8 , label Ant.UnpMean#0
index 23, group 8 , label Ant.UnpMean#1
index 24, group 9 , label FB.UnpMean#0
index 25, group 9 , label FB.UnpMean#1
index 26, group 10 , label Resp.UnpMean#0
index 27, group 10 , label Resp.UnpMean#1
index 28, group 11 , label Ant.UnpNice#0
index 29, group 11 , label Ant.UnpNice#1
index 30, group 12 , label FB.UnpNice#0
index 31, group 12 , label FB.UnpNice#1
index 32, group 13 , label Resp.UnpNice#0
index 33, group 13 , label Resp.UnpNice#1
index 34, group 14 , label Missing#0
index 35, group 0 , label aCompcor6[0]#0
index 36, group 0 , label aCompcor6[1]#0
index 37, group 0 , label aCompcor6[2]#0
index 38, group 0 , label aCompcor6[3]#0
index 39, group 0 , label aCompcor6[4]#0
index 40, group 0 , label aCompcor6[5]#0
index 41, group 0 , label cosine[0]#0
index 42, group 0 , label cosine[1]#0
index 43, group 0 , label cosine[2]#0
index 44, group 0 , label fd[0]#0
index 45, group 0 , label mot_demean_r01[0]#0
index 46, group 0 , label mot_demean_r01[1]#0
index 47, group 0 , label mot_demean_r01[2]#0
index 48, group 0 , label mot_demean_r01[3]#0
index 49, group 0 , label mot_demean_r01[4]#0
index 50, group 0 , label mot_demean_r01[5]#0
index 51, group 0 , label mot_demean_r02[0]#0
index 52, group 0 , label mot_demean_r02[1]#0
index 53, group 0 , label mot_demean_r02[2]#0
index 54, group 0 , label mot_demean_r02[3]#0
index 55, group 0 , label mot_demean_r02[4]#0
index 56, group 0 , label mot_demean_r02[5]#0
index 57, group 0 , label mot_demean_r03[0]#0
index 58, group 0 , label mot_demean_r03[1]#0
index 59, group 0 , label mot_demean_r03[2]#0
index 60, group 0 , label mot_demean_r03[3]#0
index 61, group 0 , label mot_demean_r03[4]#0
index 62, group 0 , label mot_demean_r03[5]#0

2) this brings me to my second question, I read somewhere in the annals of the internet that when you want to compare conditions (let's say Ant.Mean - Ant.Nice) on the group-level, you may need to adjust the way in which your AM events on the individual level are being de-meaned. Specifically, since currently each AM event is being demeaned within each condition, for each person individually, you'd instead want to manually calculate the overall mean across subjects and runs, and manually apply that as the mean in your individual analyses instead. Is this true?

3) Some of my participants have low variability in behavioral responses across a condition. For example, they always respond "1" when the range is 1-5. My advisor and I were wondering if this is a problem for amplitude modulation analyses? Essentially, do subjects without variability need to be excluded?

THANK YOU SO MUCH!
Subject Author Posted

Amplitude Modulation: De-meaning, X.xmat.1D output, low variability

MeganQ June 15, 2022 12:29PM

Re: Amplitude Modulation: De-meaning, X.xmat.1D output, low variability

rick reynolds June 16, 2022 06:23PM

Re: Amplitude Modulation: De-meaning, X.xmat.1D output, low variability

MeganQ June 17, 2022 08:48AM



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