Hello Andrew - thank you very much for your previous input ! Much to my dismay, I indeed have to put any further analyses on embargo (after some discussion with my Supervisor on my initial set of findings...).

I have some other questions for you now re the simple cases - mainly to gain a more understanding of what the contrast is doing -

(1) When testing for group differences (2 groups) / main group effect whilst controlling for inflammation - what is the difference between design matrices (a) and (b) below ? Are they testing for the same hypothesis ie FC is Lower in Patient group when controlling for inflammation ? Is design (a) incorrect when testing for this hypothesis - is this testing for negative correlation between FC and Group, how would you interpret this association ?

design matrix (a)
1 1 0.3
1 0 2.2
1 1 0.3
contrast : [0 -1 0] , t-test
cols : intercept ; Group - HC(0) and Pts(1) ; inflammation

design matrix (b)
1 1 0 0.3
1 0 1 2.2
1 1 0 0.3
contrast : [0 1 -1 0], t-test
cols : intercept ; HC ; Pts ; inflammation


(2) I understand that in linear model the intercept = dependent variable when explanatory variable = 0. In my ANCOVA design matrix below for example, when testing for the effect on inflammation on FC, the intercept models global mean FC when inflammation is 0 - true ? Also, with contrast notation [0 0 -1] for below - is this controlling for group AND global mean FC ?? What does setting intercept contrast to 0 denote / doing ?

1 1 0.3
1 0 2.2
1 1 0.3
cols : intercept ; Group - HC(0) and Pts(1) ; inflammation


(3) Why and when is demeaning a variable (dependent and/or explanatory) necessary for NBS ? Is demeaning here equivalent to mean centering eg. standard scoring, Fisher r-z transformation, log transformation ? My FC matrices are already r-z transformed but the inflammation index is in mg/L ie both not in comparable scale - would/ should this have any effect on output ... ?? I repeated my analyses with log-transformed inflammation - did not see any difference ie still no significant network ?


(4) I would also like to try restricting testing to specific ROIs / a functional module. Based on you explanation below to another question on the forum - you have indicated that this would reduce number of multiple comparisons. Could this then yield a different outcome compared to when inputing full connectivity matrix ? If so / not - why ?

"If a connection is 0 for all subjects, then the NBS will automatically ignore that connection during statistical testing.
In other words, if the connectivity value for a given connection is zero in all connectivity matrices, that specific connection is ignored by default and the number of multiple comparisons reduced accordingly."


Hope my queries make sense - sorry, please and many thanks again - Athina.