Dear SPM experts,
I encountered several questions when running DCM and PEB analysis on my task-based fMRI data. May I know how should I deal with them?
1. What is the size of smoothing kernal should I take during time series extraction? I found that the size of the smoothing core will influence the significance of the estimated parameters, especially the ROIs are very close to each other in anatomy. On my case, I found the modulatory connectivity from the left dorsolateral cortex to the left caudate is significant when a 8mm-FWHM Gaussian kernal was applied, however, its significance disappeared when a 5mm-FWHM Gaussian kernal was applied. I checked the mask of ROIs, the individual caudate mask was very close to the individual thalamus mask. And the modulatory connectivity from the left dorsolatoral cortex to the left thalamus was significant no matter what the size of smoothing kernal is. I am wandering that if the significant connectivity between the left dorsolateral cortex and the left caudate was caused by the connectivity between the left dorsolateral cortex and the left thalamus?
2. How should I define the design matrix X in PEB? If I have one group, and I just want to investigate the group effect, the X should be a N*1 vector, right? if I have two groups, for example, 6 subjects with 3 in each group, should I set the X matrix as following form?
1 1
1 1
1 1
1 -1
1 -1
1 -1
If I have more than two groups, how should I set the X matrix then? In addition, if I have other covariates such as gender and age, if the following X matrix is correct?
1 1 1 18
1 1 1 19
1 1 2 17
1 -1 2 19
1 -1 1 16
1 -1 2 20
The second column denotes group, while the third column denotes gender and the last column denotes age. Sorry I did not found the example of the X matrix setting, hence I could not understand it intuitively. For example, I am still not clear about the meaning of 'mean-correct' during the X matrix setting, the mean of group?
3. Another question regarding the design matrix X is that if I would like to investigate the difference between two experimental conditions on the same connectivity, between groups, which mean a 2 (between group) x 2 (within group) deign, should I set the deign matrix as the PEB example on the effect of treatment? LIke:
GCM = {GCM1_pre; GCM1_post; GCM2_pre; GCM2_post};
PEB = spm_dcm_peb(GCM, X);
4. Finally, according to my understanding, we need to use posterior probability or free energe to threshold the parameters, but which threshold should we take, more than 90 or 95 or even 99? How could I get the parameter estimation from each subject, still I need to use 'spm_dcm_bmr' to get them, PEB will not derive the individual averaging parameters across models, is that correct? In addition, how to interprete the results of PEB in investigate the effects of covariates. For example, I found a main effect of gender on a certain connectivity, the result seemed to be a parameter coefficient 0.565, what's the meaning of this coefficient, and how could I know if the males showed greater connectivity than females or vice versa?
Looking forward to your kind reply. Any suggestions would be much appreciated.
Best regards,
Lizhi
|