Hi John,

While I feel as though my competence with the GLM is improving, I actually don’t have much ‘conventional’ stats experience, so I often run into issues trying to *translate* between what users are trying to communicate, and a numerical expression of the model & hypothesis.

In this case, I’m stuck on “full multiple regression”. What I *suspect* this phrase is supposed to be referring to is that all variables of interest are being regressed against the data simultaneously within a single model. If this is the case, then it’s the inclusion of all three variables within your *design matrix* that is providing the “full multiple regression”. The problem then is that this provides no information about your *hypothesis* regarding how the data may fit this model.

If by “partial correlations” you mean how your measurement (e.g. FD / FC / FDC within FBA) varies as a function of a particular variable, specifically within a model that in fact regresses against all variables at once, then yes: `[0 1 0 0]`

will give you the rate of change of measurement variable as a function of explanatory variable 1; similarly `[0 0 1 0]`

for variable 2 and `[0 0 0 1]`

for variable 3.

Using the `-negative`

option allows you to test for both positive values of (rate of change of measurement variable as a function of explanatory variable), and negative values of that rate-of-change, within a single execution of `fixelcfestats`

. This is equivalent to e.g. running it once with contrast `[0 1 0 0]`

, and then again with `[0 -1 0 0]`

; but doing it in a single execution is faster, as you only need to build the fixel-fixel connectivity matrix once. You however can’t yet test multiple distinct hypotheses within a single `fixelcfestats`

run; but that functionality is on its way.

Whether or not to de-mean variables in the design matrix can be context-dependent. Here, whether or not you de-mean influences the extent to which you can interpret directly the beta values that the GLM yields. However I might dodge that discussion for brevity this time around. The fundamental outcome of your experiment should not change depending on whether or not you de-mean; *unless* your explanatory variables vary wildly in magnitude (e.g. one variable has values of ~ 1e-6, another has values of ~ 1e+6), in which case demeaning the variables (& modulating to unity variance) may provide something akin to preconditioning for the GLM.

P.S. If, with Point 1, you were in fact referring to a hypothesis more akin to “Do *any* of these variables influence the observed measurement?”, then in GLM-speak this would be an F-test with matrix contrast. This is another capability that is not yet available in the public code but has been implemented and is on its way.

Rob