Hi Donald,

We got the PPPI toolbox up and running and it does look like it will provide us with the modeling capability we need.

I am wondering if you might elaborate on your concern about collinearity in the design, though.  We're getting the following warning in MATLAB and am wondering whether such a collinearity might be responsible:

Warning: Matrix is close to singular or badly scaled.
         Results may be inaccurate. RCOND = 1.266417e-020. 
> In spm_PEB>spm_inv at 371
  In spm_PEB at 244
  In PPPI at 633

Our design matrix has 7 columns as you specified below (plus one for the constant) - we only have one task which has a block design consisting of 5 control blocks interleaved with 4 experimental blocks.  Where exactly would problems with collinearity arise?  Let me know if you need more info about the task.

Thanks so much!

Annchen



On Oct 23, 2012, at 1:29 PM, MCLAREN, Donald wrote:

> Please see inline responses below.
> 
> On Tue, Oct 16, 2012 at 2:22 PM, Annchen Knodt <[log in to unmask]> wrote:
>> Hello All,
>> 
>> I would like to model a physio-physiological interaction in SPM 8
>> (interaction of time series from 2 separate ROIS), however I would like to
>> determine if that interaction is dependent on the psychological condition
>> determined by the experimental design. Stated differently, I want to test if
>> the physio-physiological interaction during condition A is significantly
>> different than the same interaction in condition B. My first concern is to
>> make sure I am modeling this question properly. The second issue regards
>> implementation.
>> 
>> 1)     If I understand the SPM8 manual properly, in order to test this
>> question I should enter the following predictors into the design matrix:  1)
>> a regressor representing the convolved time series from ROI 1; 2) a
>> regressor representing the convolved time series from ROI 2; 3) a condition
>> representing the experimental design; 4) a regressor representing the
>> convolved time series from ROI 1 multiplied by the experimental design; 5) a
>> regressor representing the convolved time series from ROI 2 multiplied by
>> the experimental design; 6) a regressor representing the convolved time
>> series from ROI 1 multiplied by the convolved time series from ROI 2; 7) a
>> regressor representing the convolved time series from ROI 1 multiplied by
>> the convolved time series from ROI 2 and the experimental design (the three
>> way interaction term).
>> 
>> Is this correct?
> 
>>>> Almost. You want the following regressors:
> (1) timeseries from ROI1 (BOLD signal extracted from the ROI)
> (2) timeseries from ROI2 (BOLD signal extracted from the ROI)
> (3) Your task design matrix (one column for each task)
> (4) the convolution of (the deconvolved timeseries in ROI1 * task) -->
> 1 column for each task
> (5) the convolution of (the deconvolved timeseries in ROI2 * task) -->
> 1 column for each task
> (6) the convolution of (the deconvolved timeseries in ROI1 * the
> deconvolved timeseries in ROI2)
> (7) the convolution of (the deconvolved timeseries in ROI1 * the
> deconvolved timeseries in ROI2 * task) --> one column for each task
> 
> This can be done automatically with the gPPI toolbox; however, nothing
> has been published on the three-way interaction as far as I know. I
> also think you will need a number of trials in your task to get the
> models to estimate the effects accurately to avoid collinearity in
> your design.
> 
> 
>> 
>> 2)     Finally, does anyone know if this can be implemented in SPM8 using
>> the Batch function? Additionally any other advice about implementation would
>> be appreciated.
> 
> Use the gPPI toolbox available through NITRC (www.nitrc.org/projects/gppi)
> 
>> 
>> Thanks in advance for any help.
>> 
>> 
>> Annchen Knodt
>> 
>> 
>> ~~~~~~~~~~~~
>> Annchen Knodt, M.S.
>> Research Associate
>> Laboratory of Neurogenetics
>> 919.684.1039
>> Duke University
>> 
>> 
>> 
>> 
>>