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Significant interaction without individual group effectsHi Alva,

Please, see below:


On 22 May 2015 at 01:09, Alva Tang <[log in to unmask]> wrote:
Dear FSL experts,

I am looking at differences in resting state networks using dual regression to see whether connectivity differs across 3 groups as a function of anxiety.  After setting up the design matrix with the mean-centered values of anxiety, I set up the contrasts below.


                                           normal       SGA     AGA     Anxiety_norm    Anxiety_SGA     Anxiety_AGA
Slope normal > SGA                    0  0      0       1       -1      0
Slope SGA > Slope norm        0  0      0       -1      1       0
Slope normal > slope AGA      0 0       0       1       0       -1
Slope AGA > slope normal      0 0       0       -1      0       1
Slope SGA > Slope AGA         0 0       0       0       1       -1
Slope AGA > Slope SGA         0 0       0       0       -1      1


1.  If a region is statistically significant for the first contrast, does it mean that region has increased connectivity for the normal > the SGA group in relation to anxiety, such that, anxiety has different effects on this region between groups?


Yes.
 
If this interpretation is correct, how do I visualize the direction of this effect in the program (to see whether increases or decreases in anxiety is related to the increased connectivity of this region); Is there a way to plot the anxiety scores and the activity of that region and across groups or would I extract these values then plot in another program?

There isn't a direct way to visualise. You'll have to run a few more contrasts to check the direction of each interaction EV (as opposed to the differences), and see if the signs of the statistics in the regions where you found significance are positive or negative. There is a lengthy thread in the mailing list discussing how it can be done, search for "Significant interaction without individual group effects" to see all. The first in the thread should be this one.
 


2.  To correct for multiple comparisons using false discovery rate, there's a paper (Veer et al., 2010) that inputted the tfce_corrp difference images and then spatially masked with the binary representation of the pooled group main effects images.  This was to decrease susceptibility to type 1 errors.  I am not understanding how the masking here contributes to a more stringent threshold or which masks to select?

I just found the paper. They seem to have used TFCE-corrected for the main group effects, and FDR for the between group-effects, and these were masked after doing FDR. I'm not reading the full paper, and perhaps there is justification for this somewhere. The way I would do is not test the main group effects at all (these are known to be different than zero), and test just the between-group differences, using then perhaps FDR. If masking, I'd do it before FDR. Maybe the main group effects were tested in order to produce a mask, which otherwise perhaps would haven't been available, I'm not sure.

All the best,

Anderson


 

If you could please help me, that would be much appreciated.  Thank you.

Alva