Hi Cristina,

This is interesting, because when there are no significant results, often the researcher becomes disappointed. But when there are too many significant results, such as throughout the whole brain, the researcher also becomes disappointed. So it feels as if one had to calibrate the results so that the researcher becomes satisfied in having some circumscribed regions about which stories could be said.

Far from trying to solve this problem here, if the design is correct and the images were all processed correctly, then it seems there is reasonable evidence that the behavioural variable is associated with FA throughout the whole brain. If this isn't something interesting, then perhaps consider using a global measure of FA (such as the global average) as an additional EV in the model.

Another possibility could be whether there could be another factor, other than age, sex, and education as already included in the model, that could be influencing both things, i.e,, both SDMT and FA, in your population. You'd have to try to identify this factor and include it in the model.

Hope this helps.

All the best,

Anderson


On 11 February 2016 at 03:27, C. Roman <[log in to unmask]> wrote:
FSL Experts,

I am writing to follow-up on a posting regarding multiple regression in TBSS. Similar to the 2009 posting (https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0903&L=FSL&P=R65741&1=FSL&9=A&J=on&d=No+Match%3BMatch%3BMatches&z=4), I have just run a multiple regression with tbss on skeletonised FA data in a single group of 22 subjects using 4 EVs. My primary EV is a neuropsychological/behavioral variable and I would like to control for age, sex, and education (other 3 EVs). I created a design matrix using the FSL GLM with my variable of interest (EV1) and 3 covariates (EV2-EV4), which have been manually demeaned. I hoped that this would show significant relationships between white matter and my behavioral variable with the 3 covariates controlled for.  This was using randomise_parallel and the TFCE option. When I display the tbss_tfce_corrp_tstat contrast image in fslview with the -b 0.95,1 threshold, however, most of the skeletonised data appears to correlate with the target variable. This is the case even when I use a more conservative 0.99,1 threshold.

I highly doubt that the entire brain is correlated with my behavioral variable, so I was hoping to get some guidance on what I might be doing wrong. I have attached snapshots of my GLM setup and contrast tab.

Any guidance you can provide would be GREATLY appreciated.

Thank you so much for your time,
Cristina