hello again!
first i have to thank rick for all the help with my afni_proc.py code. after the pre-processsing and first step i have to do group analysis now. here are some of my questions!

I. i set 7 conditions in afni_proc.py by the “-regress_stim_labels”, which are “M1, M6, N1, N6, W1, W6, A”, and i set 7 kinds of “-gltsym”, which were labeled as 1-7, to make next steps more convenient, but i can’t see the files from which i can find signs of these conditions or contrasts except the ideal_M1.1D, ideal_M2.1D etc. i know that in fact all these conditions or contrasts are included in the files. could you please tell me how to set the data of one condition in group analysis code?

II. i checked the pdfs in afni-handouts of group analysis, and i found that the 3dMVM suitable for my experiment. my experiment is a 2(gender: M, F)*4(M1, M6, W6, A), and perhaps i want age as an co-variate is 3dMVM a better choice? (but i can’t find the 3dMVM in “program help” page in the afni website(i can learn it by “3DMVM -help” but i prefer on web page, so if you don’t want to add that to your website, that’s totally fine.).)

i know that in fact all these conditions or contrasts are included in the files. could you please
tell me how to set the data of one condition in group analysis code?

You can use

3dinfo -verb yourFile

to check the sub-brick labels and indices.

my experiment is a 2(gender: M, F)*4(M1, M6, W6, A), and perhaps i want age as an co-variate is 3dMVM a better choice?

Yes, 3dMVM is the way to go for your situation.

i can’t find the 3dMVM in “program help” page in the afni website

here’s the feedback after using “3dinfo -verb”(just one condition as an example):
Number of values stored at each pixel = 43
– At sub-brick #0 ‘Full_Fstat’ datum type is float: 0 to 14.8134
statcode = fift; statpar = 7 624
– At sub-brick #1 ‘A#0_Coef’ datum type is float: -58.5926 to 87.1219
– At sub-brick #2 ‘A#0_Tstat’ datum type is float: -6.53053 to 5.5621
statcode = fitt; statpar = 624
– At sub-brick #3 ‘A_Fstat’ datum type is float: 0 to 42.6478
statcode = fift; statpar = 1 624
as this lines shows, there are three kinds of sub-bricks(Coef, Tstat and Fstat) for each condition, so which should i choose when writting the group analysis command?

my experiment has 7 conditions(A M1 M6 W1 W6 N1 N6). if that except the simple contrasts like “M6-M1”, i also want to analyse some other values, like “(M6-M1)-(W1-W6)”, in group ananlysis, what should i do?

there are three kinds of sub-bricks(Coef, Tstat and Fstat) for each condition, so which should i
choose when writting the group analysis command?

It should be the effect estimate (or beta value), which is coded as ‘Coef’ in the sub-brick label.

my experiment has 7 conditions(A M1 M6 W1 W6 N1 N6). if that except the simple contrasts like
“M6-M1”, i also want to analyse some other values, like “(M6-M1)-(W1-W6)”, in group ananlysis,
what should i do?

There are a couple of approaches, but it would be much easier to simply use those 7 conditions as input, build an overall model at the group analysis with, for example, 3dMVM, and obtain all the contrast tests through, for example, -gltCode in 3dMVM.

so if i want to see what’s different between “M6-M1” and “W1-W6”, how should i set the model? is “-gltCode 1 ‘stimuli : M6 -M1 -W1 +W6’” ok? that looks just so weird.

the feedback says:
Error in glfList[[ii]][sq, jj] ← as.numeric(sepTerms[seq(1, length(sepTerms), :
被替换的项目不是替换值长度的倍数
Calls: process.MVM.opts → gl_Constr → glfConstr
此外: Warning message:
In glfConstr(code[[n]], lop$dataStr) : 强制改变过程中产生了NA
停止执行
what does that mean?(i use the ubuntu chinese version. lucky that you understand chinese)

and here’s my MVM command (with just part of the table):
3dMVM
-prefix output.MVM_BI.test
-jobs 4
-bsVars ‘genderStanHand+age’
-wsVars stimuli
-qVars age
-SC
-num_glt 14
-gltLabel 1 M6_M1 -gltCode 1 'stimuli : 1M6 -1M1’
-gltLabel 2 M6_W6 -gltCode 2 'stimuli : 1M6 -1W6’
-gltLabel 3 M6_N6 -gltCode 3 'stimuli : 1M6 -1N6’
-gltLabel 4 N6_W6 -gltCode 4 'stimuli : 1N6 -1W6’
-gltLabel 5 W1_W6 -gltCode 5 'stimuli : 1W1 -1W6’
-gltLabel 6 N6_N1 -gltCode 6 'stimuli : 1N6 -1N1’
-gltLabel 7 M1_A -gltCode 7 'stimuli : 1M1 -1A’
-gltLabel 8 diff_of_difficult-easy -gltCode 8 'stimuli : 1M6 -1M1 -1W1 +1W6’
-gltLabel 9 diff_between_gonogoNoddball -gltCode 9 'stimuli : 1M6 -1M1 -1N6 +1N1’
-gltLabel 10 diff_between_inhibitionNselection -gltCode 10 'stimuli : 1M6 -2M1 +A’
-gltLabel 11 gender_diff_of_M6_M1 -gltCode 11 'gender : 1male -1female stimuli : 1M6 -1M1’
-gltLabel 12 gender_diff_of_M6_W6 -gltCode 12 'gender : 1male -1female stimuli : 1M6 -1W6’
-gltLabel 13 gender_diff_of_N6_W6 -gltCode 13 'gender : 1male -1female stimuli : 1N6 -1W6’
-gltLabel 14 gender_diff_of_M1_A -gltCode 14 'gender : 1male -1female stimuli : 1M1 -1*A’
-dataTable
Subj gender StHand age stimuli InputFile
S1 male left 21 A stats/stats.sub01_REML+tlrc’[A#0_Coef]’
S1 male left 21 M1 stats/stats.sub01_REML+tlrc’[M1#0_Coef]’
S1 male left 21 N1 stats/stats.sub01_REML+tlrc’[N1#0_Coef]’
S1 male left 21 W1 stats/stats.sub01_REML+tlrc’[W1#0_Coef]’
S1 male left 21 M6 stats/stats.sub01_REML+tlrc’[M6#0_Coef]’
S1 male left 21 N6 stats/stats.sub01_REML+tlrc’[N6#0_Coef]’
S1 male left 21 W6 stats/stats.sub01_REML+tlrc’[W6#0_Coef]’
S2 male left 23 A stats/stats.sub02_REML+tlrc’[A#0_Coef]’
S2 male left 23 M1 stats/stats.sub02_REML+tlrc’[M1#0_Coef]’
S2 male left 23 N1 stats/stats.sub02_REML+tlrc’[N1#0_Coef]’
S2 male left 23 W1 stats/stats.sub02_REML+tlrc’[W1#0_Coef]’
S2 male left 23 M6 stats/stats.sub02_REML+tlrc’[M6#0_Coef]’
S2 male left 23 N6 stats/stats.sub02_REML+tlrc’[N6#0_Coef]’
S2 male left 23 W6 stats/stats.sub02_REML+tlrc’[W6#0_Coef]’
S3 female left 19 A stats/stats.sub03_REML+tlrc’[A#0_Coef]’
S3 female left 19 M1 stats/stats.sub03_REML+tlrc’[M1#0_Coef]’
S3 female left 19 N1 stats/stats.sub03_REML+tlrc’[N1#0_Coef]’
S3 female left 19 W1 stats/stats.sub03_REML+tlrc’[W1#0_Coef]’
S3 female left 19 M6 stats/stats.sub03_REML+tlrc’[M6#0_Coef]’
S3 female left 19 N6 stats/stats.sub03_REML+tlrc’[N6#0_Coef]’
S3 female left 19 W6 stats/stats.sub03_REML+tlrc’[W6#0_Coef]’
S4 male left 21 A stats/stats.sub04_REML+tlrc’[A#0_Coef]’
S4 male left 21 M1 stats/stats.sub04_REML+tlrc’[M1#0_Coef]’
S4 male left 21 N1 stats/stats.sub04_REML+tlrc’[N1#0_Coef]’
S4 male left 21 W1 stats/stats.sub04_REML+tlrc’[W1#0_Coef]’
S4 male left 21 M6 stats/stats.sub04_REML+tlrc’[M6#0_Coef]’
S4 male left 21 N6 stats/stats.sub04_REML+tlrc’[N6#0_Coef]’
S4 male left 21 W6 stats/stats.sub04_REML+tlrc’[W6#0_Coef]’

really sorry for the disrupting of that dumb question caused by my carelessness! now the problem has already been solved.
by the way, i have one more question, though. can i somehow do some correlation analysis between the scale scores (or maybe reaction-time) and the MRI data with AFNI?
And i really appreciate all the patirent help you provided, no matter how dumb my questions are!

can i somehow do some correlation analysis between the scale scores
(or maybe reaction-time) and the MRI data with AFNI?

With the current data or something else? Could you provide more details about the situation?

The
National Institute of Mental Health (NIMH) is part of the National Institutes of
Health (NIH), a component of the U.S. Department of Health and Human
Services.