GLM statistics on multi-sessions and multi-runs task fmri data from one subject

AFNI Message Board
1

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?

  1. Simply averaging the t-statistics & coefficients? (seems not statistically valid?)
  2. 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?)
  3. or other methods I don’t aware?

Thanks, and look forwarding to your reply!

Cirong Liu

2

Cirong, if you have enough number of days/sessions (e.g., 10 or more), use 3dMEMA.

3

Hi, Gang,

If I understood correctly, for the 3dMEMA:
-set SETNAME
SUBJ_1 BETA_DSET T_DSET
SUBJ_2 BETA_DSET T_DSET
… … …
SUBJ_N BETA_DSET T_DSET \

Each data pairs (SUBJ_1 BETA_DSET T_DSET) is for one subject, and then a group analysis (between-subject variability and have a random effect will be modeled) was performed. This method will also take into account of within-subject variability and make the group analysis better.

However, for my case, I only have one subject and there is no between-subject variability. If I put the data as above:
-set SETNAME
Session_1 BETA_DSET T_DSET
Session_2 BETA_DSET T_DSET
… … …
Session_N BETA_DSET T_DSET \

The 3dMEMA will treat the sessions of the same subject as different subjects. That’s not what I want.

How can I tell the 3dMEMA not to model between-subject variability?

Thanks!

4

The 3dMEMA will treat the sessions of the same subject as different subjects. That’s not what I want.

How can I tell the 3dMEMA not to model between-subject variability?

Your script looks fine. In your case, you’re assessing the cross-session variability.

5

Thanks a lot for the good function! That makes sense.