Yes. You are absolutely correct. This method was described in McLaren
et al. 2012 in NeuroImage. The only difference between that paper and
your method is the deconvolution step that is part of SPM.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Sun, May 5, 2013 at 12:36 PM, Mark <[log in to unmask]> wrote:
> Hi,
>
> My 1st-level model feat originally has 26 EVs. EVs 1~4 are the stimulus combination of 2 stimulus modalities(S) and 2 cognitive condition (C):
>
> EV1=S1C1
> EV2=S1C2
> EV3=S2C1
> EV4=S2C2
>
> I want to identify brain regions whose activity showed different correlation with a seed region (region_A) across different stimulus combinations, that is, I want to know the ppi effect of S, C and their interaction.
>
> My understanding to construct this PPI model is, in addition to original 26 EVs, I have to add 5 more EVs listed below:
>
> EV(ts) = time series of region_A
> EV(i) = ppi for EV(ts) x EV1
> EV(ii) = ppi for EV(ts) x EV2
> EV(iii) = ppi EV(ts) x EV3
> EV(iv) = ppi for EV(ts) x EV4
>
> So, I have to re-run a new 1st-level model, each contains 26+5=31 EVs. Is my understanding correct?
>
> Then, in contrast setting, for EV(i), EV(ii), EV(iii), EV(iv) by order,
>
> [1 1 -1 -1] = PPI effect of S
> [1 -1 1 -1] = PPI effect of C
> [1 -1 -1 1] = PPI effect S x C
>
> Is this right?
>
> Thanks a lot.
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