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 >> >> >> >> >>