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. SPM by default specifies multi-session GLMs for univariate inference. However, you may want to concatenate all sessions into one for later (esp. connectivity) analyses.
Why concatenate?[edit | edit source]
Concatenation is not necessary for GLM-based univariate inferences. Moreover, it could lead to different estimates compared to the multi-session 'full model' because 1) if a condition is too close to the end of a session, after being convolved with an HRF it could wrongly model data from the subsequent session (acquiring additional scans before the end of each session can effectively mitigate this effect) and 2) It disables condition × session interactions by giving a single beta to each condition throughout the concatenated session.
However, SPM timeseries extraction operates on a per-session basis. It is common, therefore, to concatenate and extract timeseries from multiple sessions for connectivity analyses such as PPI or DCM.
Procedure (fMRI)[edit | edit source]
Preparations[edit | edit source]
The trickiest part of concatenation is ensuring your onsets, collated across sessions, are correct. If you have obtained onsets within each session, in order to append these onsets for your new concatenated GLM, you need to add to these onsets the length of sessions before the session. 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]; % If onsets are in scans onsets_concat = [onsets1,onsets2 + 60, onsets3 + 115]; % If onsets are in seconds TR = 2; % Replace with the actual value according to your scanning protocol onsets_concat = [onsets1,onsets2 + 60*TR, onsets3 + 115*TR];
Other regressors (e.g. head motion regressors) should be similarly concatenated.
Specifying the concatenated GLM[edit | edit source]
You can then specify your single-session first-level design matrix. Instead of having multiple sessions, you should now specify only one session which includes image volumes from all sessions and appended onsets and other regressors. Run the batch to specify the model, but do not estimate for now.
By-session adjustment and estimation[edit | edit source]
Before estimating, you still need to tell SPM which scans belong to which original session so that it can adjust for its effects. This will add to your single-session GLM block effect regressors (replacing the usual mean column in the design matrix), and correct the high-pass filter and temporal non-sphericity calculations to account for the original session lengths.
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 adjusted GLM (SPM.mat) will replace the GLM you specified before (its copy will be saved as SPM_backup.mat). Now you can estimate the GLM and add contrasts (most importantly, an effect of interest F contrast for timeseries extraction) in the normal way.
Now you may use SPM Volumes of Interest utility to extract timeseries from the single session.