Thanks for that Doug,

[I was hoping i could do the parametric modulation in the contrast weights -- SPM style of doing this after the betas were fit].

Regarding the X.mat question, sorry I wasn't clear. I can see it has the nuisance regressors and constant terms, but is that x.mat used to estimate the betas?

In the beta estimation which scenario is happening?

1. concatenate all the model regressors onto one column, add the nuisance regressors and constant terms into one big matrix (X.mat) to estimate the beta values

2. create a matrix for each run separately and estimate the betas. Save regression coefficients, compute contrasts and significances of contrasts. The end (default) result is the average (i guess average of all the estimated quantities).

From the help message it looks like it is #2, and if thats the case then X.mat looks like its a summary. I ask this because I would like to use a X that looks something like below:

which is essentially the scenario in #2, but the output of X.mat looks like this:

(the image hasn't been normalized so that is why there are blank spaces for 3 of the 6 nuisance regressors)

There are slight differences between the two (so it doesn't matter in the long run) but I just want to know which one is being used. On a quick look of selxag3-sess, the matlab files look like compiled mex files, so I can't access them.

Thanks!

Dr Kevin Aquino

Research fellow,

Sir Peter Mansfield Magnetic Resonance Center, The University of Nottingham.

Honorary Research Fellow
School of Physics, Faculty of Science, University of Sydney

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On Thu, Aug 17, 2017 at 8:24 AM, Douglas N Greve <greve@nmr.mgh.harvard.edu> wrote:

It has the design matrix plus a lot of other stuff in it. For parametric
modulation, have you seen this page

https://surfer.nmr.mgh.harvard.edu/fswiki/FsFastParametricModulation

On 08/15/2017 09:52 PM, Kevin Aquino wrote:
> Dear all,
>
> I have a question regarding the output from selxavg3-sess. I noticed
> that within an analysis there is a X.mat that looks like a design
> matrix -- however is this used at any stage of the analysis? The help
> message in selxavg3-sess reads:
>
> "This program will construct the design matrix for each run, fit
> the GLM, save regression coefficients, compute contrasts and
> significances of contrasts. The runs are combined together so the
> output is the average across runs (some would call this a
> "second-level" analysis)."
>
> so this looks like that x.mat is constructed and used for each run
> (and not saved by default). This then looks like X.mat is not used in
> the analysis -- it is just there for illustrative purposes? or have I
> misinterpreted the help message.
>
>
> If this is correct (i.e. X.mat in the output is not used) when making
> contrasts using mkcontrast-sess, is there a way to parametrically
> modulate each run? e.g. to model behavioural changes per run etc.
>
> Cheers,
>
>
> *Dr Kevin Aquino*
> Research fellow,
> Sir Peter Mansfield Magnetic Resonance Center, The University of
> Nottingham.
>
> Honorary Research Fellow
> School of Physics, Faculty of Science, University of Sydney
>
> *E* Kevin.aquino@nottingham.ac.uk
> <mailto:Kevin.aquino@nottingham.ac.uk>, aquino@physics.usyd.edu.au
> <mailto:aquino@physics.usyd.edu.au> | *W* *MailScanner has detected a
> possible fraud attempt from "www.physics.usyd.edu.au" claiming to be*
> https://kevinaquino.github.io <http://www.physics.usyd.edu.au/%7Eaquino/>
>
> ----------------------------------------------
>
> The brain is a wonderful organ. It starts working the moment you get
> up and does not stop until you get into the office.
> -
> Robert Frost
>
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