Dear Dr. Gang Chen,
Currently, I am analyzing visual-task fMRI data from a marmoset. It was a simple block design, ~16 sec task (visual stimuli) + ~16 sec resting block (in total of 512 time-points x 2s TR).
We performed the task on many different days (sessions) and in each day has many runs.
The simplest way (and the default way of AFNI?): we wanted to concatenate all sessions & runs and run a single GLM to examine the activation/deactivation.
However, concatenating all data results in a very big data that AFNI or our computer cannot handle appropriately. (There seems to be a max timepoint that AFNI can handle?)
Thus, we ran GLM on each session (only concatenating multi-runs within each session), and obtained t-statistics & coefficients for each session.
Now, what is the appropriate way to combine these multiple t-statistics & coefficients from different sessions?
- Simply averaging the t-statistics & coefficients? (seems not statistically valid?)
- run another 3dttest or 3dLME, etc on these t-statistics or coefficients? (If I understood correctly these programs will modeling the random effect as well, but our data is from one marmoset, and we should only examine the fix-effect?)
- or other methods I don’t aware?
Thanks, and look forwarding to your reply!