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