A session (also referred to as a 'run') is a period of data collection in the scanner. Between sessions, data acquisition is paused, typically to give the participant a rest and enable them to communicate with the experimenter. While the GLM-based analyses in SPM include provision for multi-session data, fMRI timeseries extraction and connectivity analyses such as DCM operate on a per-session basis. It is common, therefore, to concatenate the timeseries from multiple sessions prior to further analysis.

Procedure (fMRI)

1. Specify a single-session first-level design matrix including the image volumes from all sessions. Don't forget to remove any dummy scans if required by your sequence. The tricky part is ensuring your onsets, collated across sessions, are correct. Do check them carefully!

It is a common practice to obtain onsets within each session to enter them into multi-session GLMs. In order to append these onsets for your new concatenated GLM (supposing that they correspond to the same condition across sessions), you may add to these onsets the length of sessions before the session, i.e., [scans of sessions before × TR]. For example, suppose you have 3 sessions and corresponding onsets 1-3, and each session has 60, 55, 44 volume scans respectively:

scans = [60 55 40];
onsets_concat = [onsets1,onsets2 + 60*TR, onsets3 + 115*TR];

2. Having run the batch to specify the GLM, run the following code in the main Matlab window. Again, 'scans' here denotes the number of volumes in each session in the original timeseries:

scans = [60 55 40];
spm_fmri_concatenate('SPM.mat', scans);

The GLM you specified in 1 will be saved as SPM_backup.mat. Your new GLM (SPM.mat) will be different in several respects. It will replace the usual mean column in the design matrix with regressors modelling each session - you can check this using the 'Review' button in the main SPM window. It will also correct the high-pass filter and temporal non-sphericity calculations to account for the original session lengths.

3. Estimate the GLM and add contrasts in the normal way.

4. Use SPM Volumes of Interest utility to extract timeseries from the single session.