A couple of comments:
(1) In the traditional approach to PPI, the conditions must sum to 0.
(2) I am not sure that you want to compare the task to the task
modulator, I think you would want to compare the 2 conditions or
parametric modulators between 2 conditions.
(3) I am confused at how you get 4 parameters. You said you have 2
conditions and 2 parametric modulators (2+2*2)=6 parameters. This is
why you have 3 names in the name variable.
(4) Where did you get the nx3 from?
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
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On Mon, Jan 31, 2011 at 2:12 AM, Daehyun Jung <[log in to unmask]> wrote:
> In parametric modulation analysis,
>
> I have two conditions, for example self and other
>
> I used the parametric modulation analysis and I got four parameters
>
> 2 conditions (Self Other)
> 2 parameters for each condition (Self_Model Self_Error Other_Model
>
> Now, concerning PPI analysis,
>
> I am trying to do the PPI analysis and I am not sure how to write the Input variables and contrast weights
>
> What I want to do is to find out the region which communicate with the VOI region
>
> according to Self Model or Other Model or Self Model- Other Model parameters
>
> Basically, Input variables and contrast weight are supposed to be nx3 matrix
>
> SPM.Sess.U(i) --> In my data, have two component which are self and other There was no parametric data here.
> SPM.Sess.U(i).name -> There are three names which are 'Self' 'SelfxSelf^1 'SelfxSelf^1'
>
> When I write down Input variables and contrast weights,
>
> [1 1 1] means self condition and self name and contrast equals 1 right?
>
> Then,
>
> my question is that is it correct to put 2 into the second column for example ([1 2 1] ) to find out the region communicationg with VOI according to the Self Model parameter?
>
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