Hi Gang,
I am interested in a voxel-wise searching for regions with an imaging measure (call it variable M, could be activation, connectivity, or gray matter volume…) mediating the association between behavioral variables of B1 and B2. Specifically, I want to do a “mediation analysis”, examining if the following 3 things are true:
(1) in model B1=a+bB2+error1, b is significant
(2) in model B1=c+dM+error2, d is significant
(3) in model B1=e+fB2+gM+error3, g is significant; and f is significantly smaller than b
Based on descriptions in https://en.wikipedia.org/wiki/Mediation_(statistics)
and https://en.wikipedia.org/wiki/Sobel_test
Do you think that AFNI can easily handle this “mediation” analysis with a “Sobel test”? If so, how would you specifically approach it?

Let’s see if the following is what you’re looking for.

(1) in model B1=a+b*B2+error1, b is significant

You can do this yourself with any linear modeling program in R, Matlab, SAS, SPSS, etc.

(2) in model B1=c+d*M+error2, d is significant

First, create a 3D dataset for each subject with B1 with a command like:

3dcalc -prefix myB1 -a M-dataset -expr ‘b1’ (b1 is the B1-value for each subejct)

Then run model (2) with 3dttest++ by treating M as a covariate. You need to create a covariate file in a tabular format (read the 3dttest++ help for details).

(3) in model B1=e+fB2+gM+error3, g is significant; and f is significantly smaller than b

You can do (3) the same way as (2).

If you follow the above steps, the only dangling end is the last part (“f is significantly smaller than b”), which we can talk about once you reach there.

Let’s see if the following is what you’re looking
for.

(1) in model B1=a+b*B2+error1, b is significant

You can do this yourself with any linear modeling
program in R, Matlab, SAS, SPSS, etc.

(2) in model B1=c+d*M+error2, d is significant

First, create a 3D dataset for each subject with
B1 with a command like:

3dcalc -prefix myB1 -a M-dataset -expr ‘b1’ (b1
is the B1-value for each subejct)

Then run model (2) with 3dttest++ by treating M as
a covariate. You need to create a covariate file
in a tabular format (read the 3dttest++ help for
details).

(3) in model B1=e+fB2+gM+error3, g is
significant; and f is significantly smaller than b

You can do (3) the same way as (2).

If you follow the above steps, the only dangling
end is the last part (“f is significantly smaller
than b”), which we can talk about once you reach
there.

Greeting,

Apologies for reviving this old post but I am at the last step:

“If you follow the above steps, the only dangling
end is the last part (“f is significantly smaller
than b”), which we can talk about once you reach
there”

b is significant
d is significant
g is significant and f looks like it’s smaller now compared to b. Any thoughts on how to test the “f is significantly smaller
than b” hypothesis?

Any help will be appreciated,

Thank you,

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.