Dear Peter,Thank you very much for the clarifications!Bests,DelinOn Thu, Jan 12, 2017 at 6:27 AM, Zeidman, Peter <[log in to unmask]> wrote:Dear Delin Sun
Thank you for the answers. Should I also include the 6 head motion parameters in GLM? It seems that SPM12 manual's example did not include head motion regressors in GLM.
Yes, it’s always good to model known contributions to the signal. Motion will then be regressed out during ROI extraction (thanks to your Effects of Interest F-contrast telling SPM which columns are interesting).
Thank you for the suggestion. I found in the SPM mailing list that Donald McLaren suggested to use his gPPI functions for concatenation. Is there any difference between your method and Donald's way?
The SPM function I mentioned (spm_fmri_concatenate) adjusts the high pass filter and temporal autocorrelation model to account for the session concatenation. I haven’t seen Donald’s script, but I would be surprised if it does this.
I am sorry for the confusion. I found in the SPM12 manual's example that the time series was extracted from VOI defined by "i1 AND i2" where i1 is the cluster of SPM outputs and i2 is a sphere covering the predefined ROI. What I want to do is to just extract time series from i2. Is it a correct way?
Yes that’s fine – if you’re using the “eigenvariate” button in the GUI, first set the p-value to p=1 (to include all voxels), then click eigenvariate and select sphere. If you’re using the batch, just select ‘New: Sphere’ from the ‘Region(s) of Interest’ option and set the coordinates. The expression would then just be ‘i1’. Let me know if you need more clarification on these options.
Thank you for the clarification. About C-matrix, if I understand it correctly, only column 1 is related with Task driving input, right?
That’s right – it’s one column per input, one row per region. So the first column will be Task and the second column will be Emotion.
All the best,
Peter