Dear Peter, thanks again for your response,
I have agian a naïve question,
I encounter a problem while extracting the eigenvariate of different ROIs. Actually when I use even e liberal threshold there are some régions which don't show activation (I use masks for the ROIs). So I have used different thresholds for different regions. Now my concern is that by choosing different thresholds, different activation types will be seen. So will it affect the eigenvariate too much?
Should I use all the time the same threshold for extracting the ROIs?
Thanks in advance,
There needs to be some experimental effect in your ROIs for the DCM to explain. If there isn’t, then the DCM won’t fit the signal. There’s nothing special about any particular statistical threshold – you just need to convince yourself there’s something interesting going on that is worth applying DCM to. Often, people look for a group-level effect and extract the individual subjects’ ROIs in the corresponding locations at a very liberal threshold.
So, try using masks from the literature, and see if you get significant results. You can sanity check your models by checking the explained variance (spm_dcm_fmri_check()). Note that when you extract ROIs using the ‘eigenvariate’ button, the cursor will jump to the nearest peak which could be in another brain region. So make sure to set the threshold significantly liberally that you activated voxels in your ROI.
Dear DCM experts,
I am using DCM on a small number of patients (at single subject level). Regarding the regions of interest I have two questions :
1. To define my regions of interest, should I necessarily select the regions which are commonly activated in all participants/all conditions of interest or I can select the regions based on the current literature even if they are not significantly activated in the effect of interest?
2. To extract the time series of the ROIs from the t-contrast of the “condition of interest” in a sphere around the maxima, should the maxima be significant?
Many thanks in advance,